From f5f23a359e0f656f907ae755b6bbebde72ec4672 Mon Sep 17 00:00:00 2001 From: James Ball Date: Wed, 6 Nov 2024 11:59:50 +0000 Subject: [PATCH] new notebooks --- notebooks/241106_colab_JB/tile_multiclass_JB.ipynb | 1 + notebooks/241106_colab_JB/tilingJB.ipynb | 1 + notebooks/241106_colab_JB/tiling_ms_JB.ipynb | 1 + notebooks/241106_colab_JB/trainingJB.ipynb | 1 + notebooks/241106_colab_JB/training_ms_JB.ipynb | 1 + 5 files changed, 5 insertions(+) create mode 100644 notebooks/241106_colab_JB/tile_multiclass_JB.ipynb create mode 100644 notebooks/241106_colab_JB/tilingJB.ipynb create mode 100644 notebooks/241106_colab_JB/tiling_ms_JB.ipynb create mode 100644 notebooks/241106_colab_JB/trainingJB.ipynb create mode 100644 notebooks/241106_colab_JB/training_ms_JB.ipynb diff --git a/notebooks/241106_colab_JB/tile_multiclass_JB.ipynb b/notebooks/241106_colab_JB/tile_multiclass_JB.ipynb new file mode 100644 index 00000000..c794966e --- /dev/null +++ b/notebooks/241106_colab_JB/tile_multiclass_JB.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOw2gkG3tkmWrdYTjQqofZP"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"NzlFsJJXAAul","outputId":"533a9193-a364-4a24-d54e-9e2826be0a8b","executionInfo":{"status":"ok","timestamp":1726499544825,"user_tz":-60,"elapsed":206366,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","Collecting git+https://github.com/PatBall1/detectree2.git@jb/july24\n"," Cloning https://github.com/PatBall1/detectree2.git (to revision jb/july24) to /tmp/pip-req-build-m6ne_z8g\n"," Running command git clone --filter=blob:none --quiet https://github.com/PatBall1/detectree2.git /tmp/pip-req-build-m6ne_z8g\n"," Running command git checkout -b jb/july24 --track origin/jb/july24\n"," Switched to a new branch 'jb/july24'\n"," Branch 'jb/july24' set up to track remote branch 'jb/july24' from 'origin'.\n"," Resolved https://github.com/PatBall1/detectree2.git to commit db5c4b80d9b63df4c0a835d33c596158175d15a5\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting detectron2@ git+https://github.com/facebookresearch/detectron2.git (from detectree2==1.0.8)\n"," Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-install-enn08270/detectron2_cd3eeb19b55d4728bdd299b1660f98e6\n"," Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-install-enn08270/detectron2_cd3eeb19b55d4728bdd299b1660f98e6\n"," Resolved https://github.com/facebookresearch/detectron2.git to commit 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/tmp/pip-ephem-wheel-cache-85p456nw/wheels/47/e5/15/94c80df2ba85500c5d76599cc307c0a7079d0e221bb6fc4375\n"," Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pycrs: filename=PyCRS-1.0.2-py3-none-any.whl size=32686 sha256=bc9d5c177464fbfc04c766723a693767884abdf06c49cdb98779518603d57c1e\n"," Stored in directory: /root/.cache/pip/wheels/47/1d/70/7a5bdf33347e7c75e95b06b1fa38f076a59a9506653cc24aff\n"," Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61395 sha256=abc8ea8b06e2855bbac7d259b0dabd9e828948a6efe2dff69a571fa89f313199\n"," Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\n"," Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=d1e6ae1523f6aae20cf4f0e0696ac19396940718ce382b6cc4b23aa01352f91e\n"," Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n","Successfully built detectree2 detectron2 pycrs fvcore antlr4-python3-runtime\n","Installing collected packages: pypng, pycrs, antlr4-python3-runtime, yacs, types-python-dateutil, snuggs, rtree, pygeos, portalocker, pathspec, omegaconf, mypy-extensions, affine, rasterio, iopath, hydra-core, black, arrow, proj, geos, fvcore, descartes, detectron2, detectree2\n","Successfully installed affine-2.4.0 antlr4-python3-runtime-4.9.3 arrow-1.3.0 black-24.8.0 descartes-1.1.0 detectree2-1.0.8 detectron2-0.6 fvcore-0.1.5.post20221221 geos-0.2.3 hydra-core-1.3.2 iopath-0.1.9 mypy-extensions-1.0.0 omegaconf-2.3.0 pathspec-0.12.1 portalocker-2.10.1 proj-0.2.0 pycrs-1.0.2 pygeos-0.14 pypng-0.20220715.0 rasterio-1.3a3 rtree-1.3.0 snuggs-1.4.7 types-python-dateutil-2.9.0.20240906 yacs-0.1.8\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["pydevd_plugins"]},"id":"ba56360d31ab454e8c41c21dba6068a8"}},"metadata":{}}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","!pip install git+https://github.com/PatBall1/detectree2.git@jb/july24"]},{"cell_type":"code","source":["base_dir = \"/content/drive/MyDrive/WORK/detectree2\""],"metadata":{"id":"DuTraxsyV0ul","executionInfo":{"status":"ok","timestamp":1726499561451,"user_tz":-60,"elapsed":363,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Danum\n","import rasterio\n","import geopandas as gpd\n","\n","site_path = base_dir + \"/data/Danum_lianas\"\n","\n","img_path = site_path + \"/rgb/2017_50ha_Ortho_reproject.tif\"\n","crown_path = site_path + \"/crowns/Danum_lianas_full2017.gpkg\""],"metadata":{"id":"I8eXcbq9AGNL","executionInfo":{"status":"ok","timestamp":1726499563228,"user_tz":-60,"elapsed":1526,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# BCI\n","import rasterio\n","import geopandas as gpd\n","\n","site_path = base_dir + \"/data/BCI_50ha\"\n","\n","img_path = site_path + \"/rgb/Orthomosaic_20200801_BCI_50ha_geo.tif\"\n","crown_path = site_path + \"/crowns/010820_validated/Crowns_2020_08_01_MergedWithPlotData.shp\""],"metadata":{"id":"v-CkIYWpC-G1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Here, we set the name of the output folder.\n","# Set tiling parameters\n","buffer = 30\n","tile_width = 40\n","tile_height = 40\n","threshold = 0.6\n","appends = str(tile_width) + \"_\" + str(buffer) + \"_\" + str(threshold)\n","\n","out_dir = site_path + \"/tilesClass_\" + appends + \"/\"\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","crowns = crowns.to_crs(data.crs.data)\n","print(crowns.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OOIh-qvXLxbS","executionInfo":{"status":"ok","timestamp":1726499571837,"user_tz":-60,"elapsed":8613,"user":{"displayName":"James Ball","userId":"12200917192257062155"}},"outputId":"e5370ac1-406f-4a8d-fd3d-58fc4372268d"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]},{"output_type":"stream","name":"stdout","text":[" Tree Inside_plot NE_2017 SE_2017 SW_2017 NW_2017 LI_2017 \\\n","0 c0008 True 0.0 0.0 0.0 5.0 1.25 \n","1 c0009 True 0.0 0.0 0.0 0.0 0.00 \n","2 c0010 True 0.0 5.0 0.0 0.0 1.25 \n","3 c0011 True 0.0 5.0 0.0 0.0 1.25 \n","4 c0012 True 0.0 0.0 0.0 0.0 0.00 \n","\n"," comments x \\\n","0 Stiching errors 2016 None \n","1 None None \n","2 Stitching 2016 None \n","3 None None \n","4 None None \n","\n"," geometry \n","0 MULTIPOLYGON (((587824.637 547364.323, 587825.... \n","1 MULTIPOLYGON (((587809.335 547403.064, 587810.... \n","2 MULTIPOLYGON (((587852.786 547412.247, 587853.... \n","3 MULTIPOLYGON (((587848.671 547422.965, 587849.... \n","4 MULTIPOLYGON (((587860.198 547388.797, 587861.... \n"]}]},{"cell_type":"code","source":["def add_crown_classes(crowns):\n"," \"\"\" functions to add a column to the crowns Geodataframe which record the liana conditions when the conditions range from 0 to 100\"\"\"\n"," # add a column which shows the status of a tree\n"," crowns = crowns.assign(status='clean')\n","\n"," for i in range (len(crowns)):\n"," if crowns[\"LI_2017\"][i] > 0:\n"," crowns[\"status\"][i] = \"infested\"\n"," elif crowns[\"LI_2017\"][i] != crowns[\"LI_2017\"][i]:\n"," crowns['status'][i] = \"delete\"\n"," crowns = crowns.drop(crowns[crowns.status == \"delete\"].index)\n"," return(crowns)\n","\n","crowns = add_crown_classes(crowns)\n","\n","#now see if the function behaves as you want\n","print(crowns.head(10))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8nueJ2ysNrjx","executionInfo":{"status":"ok","timestamp":1726499584078,"user_tz":-60,"elapsed":12244,"user":{"displayName":"James Ball","userId":"12200917192257062155"}},"outputId":"db05736f-8602-40c2-896f-4645bcf55425"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns[\"status\"][i] = \"infested\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n",":10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," crowns['status'][i] = \"delete\"\n"]},{"output_type":"stream","name":"stdout","text":[" Tree Inside_plot NE_2017 SE_2017 SW_2017 NW_2017 LI_2017 \\\n","0 c0008 True 0.0 0.0 0.0 5.0 1.25 \n","1 c0009 True 0.0 0.0 0.0 0.0 0.00 \n","2 c0010 True 0.0 5.0 0.0 0.0 1.25 \n","3 c0011 True 0.0 5.0 0.0 0.0 1.25 \n","4 c0012 True 0.0 0.0 0.0 0.0 0.00 \n","5 c0013 True 0.0 0.0 0.0 0.0 0.00 \n","6 c0014 True 0.0 0.0 0.0 0.0 0.00 \n","7 c0015 True 0.0 0.0 0.0 0.0 0.00 \n","8 c0016 True 0.0 0.0 0.0 0.0 0.00 \n","9 c0017 True 0.0 0.0 0.0 0.0 0.00 \n","\n"," comments x \\\n","0 Stiching errors 2016 None \n","1 None None \n","2 Stitching 2016 None \n","3 None None \n","4 None None \n","5 None None \n","6 None None \n","7 None None \n","8 None None \n","9 None None \n","\n"," geometry status \n","0 MULTIPOLYGON (((587824.637 547364.323, 587825.... infested \n","1 MULTIPOLYGON (((587809.335 547403.064, 587810.... clean \n","2 MULTIPOLYGON (((587852.786 547412.247, 587853.... infested \n","3 MULTIPOLYGON (((587848.671 547422.965, 587849.... infested \n","4 MULTIPOLYGON (((587860.198 547388.797, 587861.... clean \n","5 MULTIPOLYGON (((587889.901 547361.328, 587890.... clean \n","6 MULTIPOLYGON (((587876.666 547367.680, 587876.... clean \n","7 MULTIPOLYGON (((587868.291 547380.538, 587868.... clean \n","8 MULTIPOLYGON (((587880.539 547383.515, 587880.... clean \n","9 MULTIPOLYGON (((587805.987 547383.990, 587806.... clean \n"]}]},{"cell_type":"code","source":["from detectree2.preprocessing.tiling import record_classes, tile_data, to_traintest_folders, load_class_mapping\n","import pickle\n","import os\n","import json\n","import time\n","import shutil\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","class_column = 'status'\n","#class_column = None\n","\n","# Record the classes and save the class mapping\n","record_classes(\n"," crowns=crowns, # Geopandas dataframe with crowns\n"," out_dir=out_dir, # Output directory to save class mapping\n"," column=class_column, # Column used for classes\n"," save_format='json' # Choose between 'json' or 'pickle'\n",")\n","\n","# Load the class-to-index mapping from the recorded classes\n","#class_mapping_file = os.path.join(out_dir, \"class_to_idx.json\")\n","\n","#with open(class_mapping_file, 'r') as f:\n","# class_to_idx = json.load(f)\n","\n","\n","start_time = time.time()\n","\n","# Perform the tiling, ensuring the selected class column is used\n","tile_data(\n"," img_path=img_path,\n"," out_dir=out_dir,\n"," buffer=buffer,\n"," tile_width=tile_width,\n"," tile_height=tile_height,\n"," crowns=crowns,\n"," threshold=threshold,\n"," class_column=class_column # Use the selected class column (e.g., 'species', 'status')\n"," #class_mapping_file=class_mapping_file # Load the class-to-index mapping\n",")\n","\n","end_time = time.time()\n","print(f\"Time taken to tile: {end_time - start_time} seconds\")\n","\n","# Split the data into training and validation sets\n","to_traintest_folders(\n"," tiles_folder=out_dir, # Directory where tiles are saved\n"," out_folder=out_dir, # Final directory for train/test data\n"," test_frac=0, # Fraction of data to be used for testing\n"," folds=5, # Number of folds (optional, can be set to 1 for no fold splitting)\n"," strict=False, # Ensure no overlap between train/test tiles\n"," seed=42 # Set seed for reproducibility\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MgE5eGp3Zeu_","outputId":"d22ce159-7959-4849-e97d-8b56ffbca280","executionInfo":{"status":"ok","timestamp":1726501275021,"user_tz":-60,"elapsed":541304,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Classes saved as json file: {'clean': 0, 'infested': 1}\n","Time taken to tile: 534.6449053287506 seconds\n"]}]}]} \ No newline at end of file diff --git a/notebooks/241106_colab_JB/tilingJB.ipynb b/notebooks/241106_colab_JB/tilingJB.ipynb new file mode 100644 index 00000000..be91f68a --- /dev/null +++ b/notebooks/241106_colab_JB/tilingJB.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"GdO1J81Oqnvg"},"source":["# Preparing the data\n","This notebook shows how to tile up RGB and crown data ready for training.\n","\n","## Mount drive to access data and install *detectree2* package."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23003,"status":"ok","timestamp":1721237057277,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"lVurfP7VXxLs","outputId":"9fbb0d0d-a5d7-4246-de32-de996c979a39"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","Collecting git+https://github.com/PatBall1/detectree2.git@jb/july24\n"," Cloning https://github.com/PatBall1/detectree2.git (to revision jb/july24) to /tmp/pip-req-build-lvjpyiwj\n"," Running command git clone --filter=blob:none --quiet https://github.com/PatBall1/detectree2.git /tmp/pip-req-build-lvjpyiwj\n"," Running command git checkout -b jb/july24 --track origin/jb/july24\n"," Switched to a new branch 'jb/july24'\n"," Branch 'jb/july24' set up to track remote branch 'jb/july24' from 'origin'.\n"," Resolved https://github.com/PatBall1/detectree2.git to commit c2267505f31ca3956d3c1ba17ffcd1faf23076f3\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting detectron2@ git+https://github.com/facebookresearch/detectron2.git (from detectree2==1.0.8)\n"," Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-install-0s2eo4dc/detectron2_1cfc718b4490423596f88a429153d168\n"," Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-install-0s2eo4dc/detectron2_1cfc718b4490423596f88a429153d168\n"," Resolved https://github.com/facebookresearch/detectron2.git to commit 2a420edb307c9bdf640f036d3b196bed474b8593\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (6.0.1)\n","Requirement already satisfied: GDAL>=1.11 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (3.6.4)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.25.2)\n","Requirement already satisfied: rtree in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.3.0)\n","Requirement already satisfied: proj in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.2.0)\n","Requirement already satisfied: geos in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.2.3)\n","Requirement already satisfied: pypng in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.20220715.0)\n","Requirement already satisfied: pygeos in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.14)\n","Requirement already satisfied: shapely in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (2.0.4)\n","Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.13.2)\n","Requirement already satisfied: rasterio==1.3a3 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.3a3)\n","Requirement already satisfied: fiona in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.9.6)\n","Requirement already satisfied: pycrs in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.0.2)\n","Requirement already satisfied: descartes in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.1.0)\n","Requirement already satisfied: affine in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (2.4.0)\n","Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (23.2.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (2024.7.4)\n","Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (8.1.7)\n","Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (0.7.2)\n","Requirement already satisfied: snuggs>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3->detectree2==1.0.8) (1.4.7)\n","Requirement 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(2.4.0)\n","Requirement already satisfied: yacs>=0.1.8 in /usr/local/lib/python3.10/dist-packages (from detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (0.1.8)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (0.9.0)\n","Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (2.2.1)\n","Requirement already satisfied: tqdm>4.29.0 in /usr/local/lib/python3.10/dist-packages (from detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (4.66.4)\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (from detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) 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satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard->detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (2.0.7)\n","Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (0.6.0)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard->detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==1.0.8) (3.2.2)\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","!pip install git+https://github.com/PatBall1/detectree2.git@jb/july24"]},{"cell_type":"markdown","metadata":{"id":"mxoGCI5Cp8Ns"},"source":["## Set parameters for tiling"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":419,"status":"ok","timestamp":1721252605292,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"yf6br45Kc4Wo"},"outputs":[],"source":["from detectree2.preprocessing.tiling import tile_data, to_traintest_folders\n","import rasterio\n","import geopandas as gpd\n","import shutil\n","import time\n","\n","# Set tiling parameters\n","buffer = 30\n","tile_width = 40\n","tile_height = 40\n","threshold = 0.6\n","appends = str(tile_width) + \"_\" + str(buffer) + \"_\" + str(threshold)\n","\n","# dtype_bool requires True: BCI_2019, Paracou"]},{"cell_type":"markdown","metadata":{"id":"d1J47hPQp5YE"},"source":["## Tile up the data\n","Function to tile up the data into managable training chunks. This function has some issues around the encoding of the input raster. ```dtype_bool``` should be switched if black tiles are being produced. A recommended threshold is ~0.5 but it depends on volume of available data (with abundant, dense crown data, a sticter threshold may be preferable)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":602409,"status":"ok","timestamp":1706718178420,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"DWrJ3zQrL67e","outputId":"745aff29-4b30-45c1-dfea-8edd64931034"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]},{"name":"stdout","output_type":"stream","text":["Tiling complete\n"]}],"source":["\n","### PARACOU 2016\n","out_dir = site_path + '/tilesISPRS_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","#shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","crowns = crowns.to_crs(data.crs.data)\n","\n","tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold)\n","#to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"executionInfo":{"elapsed":308,"status":"ok","timestamp":1689618326806,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"AaVUJUMAGVR4","outputId":"49aae16f-964b-4514-c3ad-26d184b738c3"},"outputs":[{"data":{"text/html":["\n","\n","
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fid_1SitePlotOrgPlotNumSubPlotLocalIDCensusYearCodeAliveFamilyGenus_Species...LianasStartDateEndDateGroundValidCreatorCommentsBaseLayerIDStatusDBHestgeometry
2197471ParacouCIRAD5.03.0398.02015.0TrueClusiaceaeSymphonia_globulifera...FalseNaNNaNTrueGreg Vincent398Lidar2016NaNNaNMULTIPOLYGON (((286188.347 583007.643, 286189....
43763281ParacouExternalNaNNaNNaNNaNNaNNaNNA_NA...NaNNaNNaNNaNManonNaNNaNNaNNaNMULTIPOLYGON (((286537.890 583781.031, 286536....
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\n"],"text/plain":[" fid_1 Site PlotOrg PlotNum SubPlot LocalID CensusYear \\\n","219 7471 Paracou CIRAD 5.0 3.0 398.0 2015.0 \n","4376 3281 Paracou External NaN NaN NaN NaN \n","\n"," CodeAlive Family Genus_Species ... Lianas StartDate \\\n","219 True Clusiaceae Symphonia_globulifera ... False NaN \n","4376 NaN NaN NA_NA ... NaN NaN \n","\n"," EndDate GroundValid Creator Comments BaseLayer IDStatus DBHest \\\n","219 NaN True Greg Vincent 398 Lidar2016 NaN NaN \n","4376 NaN NaN Manon NaN NaN NaN NaN \n","\n"," geometry \n","219 MULTIPOLYGON (((286188.347 583007.643, 286189.... \n","4376 MULTIPOLYGON (((286537.890 583781.031, 286536.... \n","\n","[2 rows x 27 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["crowns[~crowns.is_valid]"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":231426,"status":"ok","timestamp":1721253499162,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"ibJWCzC0kNQf","outputId":"62ef8de4-7a95-4adc-b4f2-226c0959dd17"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]},{"output_type":"stream","name":"stdout","text":["Elapsed time: 227.692321062088 seconds\n"]}],"source":["### PARACOU 2016\n","site_path = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou\"\n","img_path = site_path + \"/rgb/2016/Paracou_RGB_2016_10cm.tif\"\n","crown_path = site_path + \"/crowns/240717_full_detectree2016.gpkg\"\n","#crown_path = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou_multitemp/crowns/crowns_train_2016.gpkg\" # Excludes multitemp test crowns\n","out_dir = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou_TESTTESTTEST\" + '/tiles_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","crowns = crowns[crowns.is_valid]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","start_time = time.time()\n","tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = True)\n","end_time = time.time()\n","elapsed_time = end_time - start_time\n","print(f\"Elapsed time: {elapsed_time} seconds\")\n","\n","# OLD TIME: 420.24s, 669.93s\n","# NEW TIME: 470.47s (threadpool), 335.10 (taskpool), 299.08 (taskpool)\n","\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)\n","#/content/drive/MyDrive/WORK/detectree2/data/Paracou/crowns/240717_full_detectree2016.gpkg"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":573121,"status":"ok","timestamp":1689621133137,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"wqbTzmlkRUcH","outputId":"79aafc6b-2138-4b96-91b4-3098da87d940"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]}],"source":["### PARACOU 2019\n","site_path = \"/content/drive/Shareddrives/detectree2/data/Paracou\"\n","img_path = site_path + \"/rgb/2019/Paracou_RGB_2019.tif\"\n","#crown_path = site_path + \"/crowns/220908_Paracou2016.gpkg\"\n","crown_path = \"/content/drive/Shareddrives/detectree2/data/Paracou_multitemp/crowns/crowns_train_2019.gpkg\" # Excludes multitemp test crowns\n","out_dir = \"/content/drive/Shareddrives/detectree2/data/Paracou_multitemp\" + '/tiles2019_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","crowns = crowns.to_crs(data.crs.data)\n","\n","tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = True)\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":103038,"status":"ok","timestamp":1721215327372,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"e7iqFKh7zJpB","outputId":"5f089ac8-735a-43c6-af1c-af8667b2c9d1"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]},{"output_type":"stream","name":"stdout","text":["Elapsed time: 93.33679580688477 seconds\n"]}],"source":["### DANUM\n","site_path = \"/content/drive/MyDrive/WORK/detectree2/data/Danum\"\n","img_path = site_path + \"/rgb/Dan_2014_RGB_project_to_CHM.tif\"\n","crown_path = site_path + \"/crowns/Danum.gpkg\"\n","#out_dir = site_path + '/tiles_' + appends + \"/\"\n","out_dir = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou_TESTTESTTEST1\" + '/Dan_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","#crowns = crowns[crowns.conf==1]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","start_time = time.time()\n","tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold)\n","end_time = time.time()\n","elapsed_time = end_time - start_time\n","print(f\"Elapsed time: {elapsed_time} seconds\")\n","\n","# OLD TIME: 147.76s\n","# NEW TIME: 203.72s, 143.25 (taskpool), 93.34s (taskpool)\n","\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":357791,"status":"ok","timestamp":1689631074919,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"3njDRnVszb9X","outputId":"ca044c92-cb69-4bf5-ff82-8be0804e8617"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]}],"source":["### SEPILOK EAST\n","site_path = \"/content/drive/Shareddrives/detectree2/data/Sepilok\"\n","img_path = site_path + \"/rgb/RCD105_MA14_21_orthomosaic_20141023_reprojected_full_res.tif\"\n","crown_path = site_path + \"/crowns/SepilokEast.gpkg\"\n","#out_dir = site_path + '/tilesE_' + appends + \"/\"\n","out_dir = \"/content/drive/Shareddrives/detectree2/data/Paracou_multitemp\" + '/SepE_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","#crowns = crowns[crowns.conf==1]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = False)\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":793669,"status":"ok","timestamp":1689622395348,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"W2SnKOzyzua5","outputId":"cb6c28b4-80fd-44d9-e4ca-8870718c92c1"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]}],"source":["### SEPILOK West\n","site_path = \"/content/drive/Shareddrives/detectree2/data/Sepilok\"\n","img_path = site_path + \"/rgb/RCD105_MA14_21_orthomosaic_20141023_reprojected_full_res.tif\"\n","crown_path = site_path + \"/crowns/SepilokWest.gpkg\"\n","#out_dir = site_path + '/tilesW_' + appends + \"/\"\n","out_dir = \"/content/drive/Shareddrives/detectree2/data/Paracou_multitemp\" + '/SepW_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","#crowns = crowns[crowns.conf==1]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = False)\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":141029,"status":"ok","timestamp":1670845551869,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"Tnz5uNJrAssD","outputId":"7d7d8721-7cbd-42a3-e128-be5c18c3b8a5"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.8/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]}],"source":["### BCI50ha\n","### BCI 50 ha\n","site_path = \"/content/drive/Shareddrives/detectree2/data/BCI_50ha\"\n","img_path = site_path + \"/rgb/2015.06.10_07cm_ORTHO.tif\"\n","crown_path = site_path + \"/crowns/BCI_CrownData_2014-10-02_KCaligned/BCI_All_Crown_Data_10ha_50ha.shp\"\n","out_dir = site_path + '/tiles_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","#crowns = crowns[crowns.conf==1]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = True)\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3029386,"status":"ok","timestamp":1689697012321,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"VyremX9JsJrj","outputId":"fa038511-c372-4161-c5ba-fa7bd09975ab"},"outputs":[{"name":"stdout","output_type":"stream","text":["20201023\n","20201105\n","20201123\n","20201214\n","20210105\n","20210118\n","20210208\n","20210303\n","20210316\n","20210406\n","20210428\n","20210511\n","20210630\n","20210706\n","20210726\n","20210817\n","20210907\n","20210928\n","20211018\n","20211110\n","20211123\n","20211213\n","20220106\n","20220124\n","20220223\n","20220317\n","20220406\n","20220426\n","20220517\n","20220608\n","20220628\n","20220719\n","20220809\n","20220830\n","20220920\n","20221011\n","20221108\n","20221129\n","20221214\n","20230103\n","20230117\n","20230216\n","20230228\n","20230314\n","20230328\n","20230420\n"]}],"source":["import glob\n","import re\n","from pathlib import Path\n","from detectree2.preprocessing.tiling import to_traintest_folders\n","\n","### PARACOU UAV NEW\n","site_path = \"/content/drive/Shareddrives/detectree2/data/Paracou_multitemp\"\n","img_folder = site_path + \"/rgb/4D_rect/\"\n","images = glob.glob(img_folder + \"*.tif\")\n","images.sort()\n","crown_path = site_path + \"/crowns/crowns_train_2020.gpkg\"\n","out_dir = site_path + \"/tilesUAV_\" + appends + \"/\"\n","\n","#print(images)\n","\n","## Remove existing tile directory\n","#shutil.rmtree(out_dir, True)\n","\n","crowns = gpd.read_file(crown_path)\n","\n","for image in images:\n"," data = rasterio.open(image)\n"," p = Path(image)\n"," date = re.search(r'\\d{8}', image).group()\n"," print(date)\n"," foldername = \"tilesUAV_\" + date + \"_\" + appends\n"," #print(filename)\n"," out_path = Path(site_path) / foldername\n"," #print(out_path)\n"," tile_data_train(data, out_path, buffer, tile_width, tile_height, crowns, threshold, dtype_bool = True)\n"," to_traintest_folders(out_path, out_path, test_frac=0.0, folds=5)"]},{"cell_type":"markdown","metadata":{"id":"64QgSu4ZpbwR"},"source":["## Send geojson to train/test folders\n","Send geojsons to train folder (with folds for k-fold cross validation) and test folder. Training tiles will automatically be remove if there is any overlap with a test tile."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gYa3DBeq8M6M"},"outputs":[],"source":["from detectree2.preprocessing.tiling import to_traintest_folders\n","#out_folder = out_dir\n","to_traintest_folders(out_dir, out_dir, test_frac=0.0, folds=5)"]},{"cell_type":"markdown","metadata":{"id":"b0Bi6RVPDfMf"},"source":["## Visualise training data\n","\n","Need to edit to register properly. Fixed in training script"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1pE_fUhl-fcOQWqQ2syYN8uB3Zhs36Yfg"},"executionInfo":{"elapsed":152510,"status":"ok","timestamp":1721253935470,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"XRZK3-rqGLDK","outputId":"329c42bf-4092-46f9-ff82-b363e4f594e6"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["# Let's look at our training image and annos for our geojson\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","from detectron2.utils.visualizer import Visualizer\n","from detectree2.models.train import combine_dicts, register_train_data\n","import random\n","import cv2\n","from PIL import Image\n","\n","\n","\n","#name = \"GAN\"\n","#train_location = \"/content/drive/MyDrive/WORK/detectree2/data/\" + name + \"/tiles_\" + appends + \"/train\"\n","name = \"Paracou\"\n","train_location = out_dir + \"/train\"\n","dataset_dicts = combine_dicts(train_location, 5)\n","trees_metadata = MetadataCatalog.get(name + \"_train\")\n","#dataset_dicts = get_tree_dicts(\"./\")\n","for d in dataset_dicts:\n"," img = cv2.imread(d[\"file_name\"])\n"," visualizer = Visualizer(img[:, :, ::-1], metadata=trees_metadata, scale=0.8)\n"," out = visualizer.draw_dataset_dict(d)\n"," image = cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB)\n"," display(Image.fromarray(image))"]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[{"file_id":"1F8T-oU2Ru0TWKeWzbK8UOJIx1GcDJ_Fn","timestamp":1656085989617}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/notebooks/241106_colab_JB/tiling_ms_JB.ipynb b/notebooks/241106_colab_JB/tiling_ms_JB.ipynb new file mode 100644 index 00000000..feaaed16 --- /dev/null +++ b/notebooks/241106_colab_JB/tiling_ms_JB.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"GdO1J81Oqnvg"},"source":["# Preparing the data\n","This notebook shows how to tile up RGB and crown data ready for training.\n","\n","## Mount drive to access data and install *detectree2* package."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":263655,"status":"ok","timestamp":1724937185177,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"lVurfP7VXxLs","outputId":"a78a0f30-b4c8-4f4f-9b5a-2af570f17b34"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n","Collecting git+https://github.com/PatBall1/detectree2.git@jb/july24\n"," Cloning https://github.com/PatBall1/detectree2.git (to revision jb/july24) to /tmp/pip-req-build-l90b45hk\n"," Running command git clone --filter=blob:none --quiet https://github.com/PatBall1/detectree2.git /tmp/pip-req-build-l90b45hk\n"," Running command git checkout -b jb/july24 --track origin/jb/july24\n"," Switched to a new branch 'jb/july24'\n"," Branch 'jb/july24' set up to track remote branch 'jb/july24' from 'origin'.\n"," Resolved https://github.com/PatBall1/detectree2.git to commit 065ad16a3d2473d25e7289ba84192ecb00d2d495\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting detectron2@ git+https://github.com/facebookresearch/detectron2.git (from detectree2==1.0.8)\n"," Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-install-ivilcyfl/detectron2_8d8709dbe4d74bc9b40219542ee644b8\n"," Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-install-ivilcyfl/detectron2_8d8709dbe4d74bc9b40219542ee644b8\n"," Resolved https://github.com/facebookresearch/detectron2.git to commit 5b72c27ae39f99db75d43f18fd1312e1ea934e60\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: pyyaml\u003e=5.1 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (6.0.2)\n","Requirement already satisfied: GDAL\u003e=1.11 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (3.6.4)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.26.4)\n","Collecting rtree (from detectree2==1.0.8)\n"," Downloading Rtree-1.3.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.1 kB)\n","Collecting proj (from detectree2==1.0.8)\n"," Downloading proj-0.2.0-py2.py3-none-any.whl.metadata (3.3 kB)\n","Collecting geos (from detectree2==1.0.8)\n"," Downloading geos-0.2.3-py3-none-any.whl.metadata (480 bytes)\n","Collecting pypng (from detectree2==1.0.8)\n"," Downloading pypng-0.20220715.0-py3-none-any.whl.metadata (13 kB)\n","Collecting pygeos (from detectree2==1.0.8)\n"," Downloading pygeos-0.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n","Requirement already satisfied: shapely in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (2.0.6)\n","Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.14.4)\n","Collecting rasterio==1.3a3 (from detectree2==1.0.8)\n"," Downloading rasterio-1.3a3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n","Requirement already satisfied: fiona in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.9.6)\n","Collecting pycrs (from detectree2==1.0.8)\n"," Downloading PyCRS-1.0.2.tar.gz (36 kB)\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting descartes (from detectree2==1.0.8)\n"," Downloading descartes-1.1.0-py3-none-any.whl.metadata (2.4 kB)\n","Collecting affine (from rasterio==1.3a3-\u003edetectree2==1.0.8)\n"," Downloading affine-2.4.0-py3-none-any.whl.metadata (4.0 kB)\n","Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3-\u003edetectree2==1.0.8) (24.2.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3-\u003edetectree2==1.0.8) (2024.7.4)\n","Requirement already satisfied: click\u003e=4.0 in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3-\u003edetectree2==1.0.8) (8.1.7)\n","Requirement already satisfied: cligj\u003e=0.5 in /usr/local/lib/python3.10/dist-packages (from rasterio==1.3a3-\u003edetectree2==1.0.8) (0.7.2)\n","Collecting snuggs\u003e=1.4.1 (from rasterio==1.3a3-\u003edetectree2==1.0.8)\n"," Downloading snuggs-1.4.7-py3-none-any.whl.metadata (3.4 kB)\n","Requirement already satisfied: click-plugins in 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detectron2 pycrs fvcore antlr4-python3-runtime\n","Installing collected packages: pypng, pycrs, antlr4-python3-runtime, yacs, types-python-dateutil, snuggs, rtree, pygeos, portalocker, pathspec, omegaconf, mypy-extensions, affine, rasterio, iopath, hydra-core, black, arrow, proj, geos, fvcore, descartes, detectron2, detectree2\n","Successfully installed affine-2.4.0 antlr4-python3-runtime-4.9.3 arrow-1.3.0 black-24.8.0 descartes-1.1.0 detectree2-1.0.8 detectron2-0.6 fvcore-0.1.5.post20221221 geos-0.2.3 hydra-core-1.3.2 iopath-0.1.9 mypy-extensions-1.0.0 omegaconf-2.3.0 pathspec-0.12.1 portalocker-2.10.1 proj-0.2.0 pycrs-1.0.2 pygeos-0.14 pypng-0.20220715.0 rasterio-1.3a3 rtree-1.3.0 snuggs-1.4.7 types-python-dateutil-2.9.0.20240821 yacs-0.1.8\n"]},{"data":{"application/vnd.colab-display-data+json":{"id":"e0c6e0f652f4448c90a52d085f04bd71","pip_warning":{"packages":["pydevd_plugins"]}}},"metadata":{},"output_type":"display_data"}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","!pip install git+https://github.com/PatBall1/detectree2.git@jb/july24"]},{"cell_type":"markdown","metadata":{"id":"mxoGCI5Cp8Ns"},"source":["## Set parameters for tiling"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":536,"status":"ok","timestamp":1724937279462,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"yf6br45Kc4Wo"},"outputs":[],"source":["from detectree2.preprocessing.tiling import tile_data, to_traintest_folders\n","import rasterio\n","import geopandas as gpd\n","import shutil\n","import time\n","\n","# Set tiling parameters\n","buffer = 15\n","tile_width = 15\n","tile_height = 15\n","threshold = 0.7\n","appends = str(tile_width) + \"_\" + str(buffer) + \"_\" + str(threshold)\n","\n","# dtype_bool requires True: BCI_2019, Paracou"]},{"cell_type":"markdown","metadata":{"id":"d1J47hPQp5YE"},"source":["## Tile up the data\n","Function to tile up the data into managable training chunks. This function has some issues around the encoding of the input raster. ```dtype_bool``` should be switched if black tiles are being produced. A recommended threshold is ~0.5 but it depends on volume of available data (with abundant, dense crown data, a sticter threshold may be preferable)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"executionInfo":{"elapsed":308,"status":"ok","timestamp":1689618326806,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"AaVUJUMAGVR4","outputId":"49aae16f-964b-4514-c3ad-26d184b738c3"},"outputs":[{"data":{"text/html":["\n","\n"," \u003cdiv id=\"df-e63a36e7-e38a-43d5-8581-aee847daf4eb\"\u003e\n"," \u003cdiv class=\"colab-df-container\"\u003e\n"," \u003cdiv\u003e\n","\u003cstyle scoped\u003e\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n"," 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'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-e63a36e7-e38a-43d5-8581-aee847daf4eb');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," \u003c/script\u003e\n"," \u003c/div\u003e\n"," \u003c/div\u003e\n"],"text/plain":[" fid_1 Site PlotOrg PlotNum SubPlot LocalID CensusYear \\\n","219 7471 Paracou CIRAD 5.0 3.0 398.0 2015.0 \n","4376 3281 Paracou External NaN NaN NaN NaN \n","\n"," CodeAlive Family Genus_Species ... Lianas StartDate \\\n","219 True Clusiaceae Symphonia_globulifera ... False NaN \n","4376 NaN NaN NA_NA ... NaN NaN \n","\n"," EndDate GroundValid Creator Comments BaseLayer IDStatus DBHest \\\n","219 NaN True Greg Vincent 398 Lidar2016 NaN NaN \n","4376 NaN NaN Manon NaN NaN NaN NaN \n","\n"," geometry \n","219 MULTIPOLYGON (((286188.347 583007.643, 286189.... \n","4376 MULTIPOLYGON (((286537.890 583781.031, 286536.... \n","\n","[2 rows x 27 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["crowns[~crowns.is_valid]"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":250441,"status":"ok","timestamp":1724937541803,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"ibJWCzC0kNQf","outputId":"8ebb1e3c-254e-40f5-dbf6-33398422d4fe"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=\u003cauthority\u003e:\u003ccode\u003e' syntax is deprecated. '\u003cauthority\u003e:\u003ccode\u003e' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n"," in_crs_string = _prepare_from_proj_string(in_crs_string)\n"]},{"name":"stdout","output_type":"stream","text":["Elapsed time: 233.48648953437805 seconds\n"]}],"source":["### PARACOU MS 2023\n","site_path = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou\"\n","img_path = site_path + \"/ms/20230314_ORTHO_aligned_local.tif\"\n","crown_path = site_path + \"/crowns/240808_full_ms_2023.gpkg\"\n","out_dir = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou\" + '/tilesMS_' + appends + \"/\"\n","\n","# Remove existing tile directory\n","shutil.rmtree(out_dir, True)\n","\n","# Read in the tiff file\n","data = rasterio.open(img_path)\n","\n","# Read in crowns (then filter by an attribute?)\n","crowns = gpd.read_file(crown_path)\n","crowns = crowns[crowns.is_valid]\n","crowns = crowns.to_crs(data.crs.data)\n","\n","start_time = time.time()\n","tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold, mode=\"ms\")\n","end_time = time.time()\n","elapsed_time = end_time - start_time\n","print(f\"Elapsed time: {elapsed_time} seconds\")\n","\n","to_traintest_folders(out_dir, out_dir, test_frac=0, folds=5)"]},{"cell_type":"markdown","metadata":{"id":"64QgSu4ZpbwR"},"source":["## Send geojson to train/test folders\n","Send geojsons to train folder (with folds for k-fold cross validation) and test folder. Training tiles will automatically be remove if there is any overlap with a test tile."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gYa3DBeq8M6M"},"outputs":[],"source":["#from detectree2.preprocessing.tiling import to_traintest_folders\n","#out_folder = out_dir\n","to_traintest_folders(out_dir, out_dir, test_frac=0.0, folds=5)"]},{"cell_type":"markdown","metadata":{"id":"b0Bi6RVPDfMf"},"source":["## Visualise training data\n","\n","Need to edit to register properly. Fixed in training script"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1XuK9RtNmPZuWBCJczjzaNMusAkIor8In"},"id":"XRZK3-rqGLDK","outputId":"0caf2348-bf02-43e5-bc1c-2a8ec5a7ef10"},"outputs":[],"source":["import rasterio\n","from detectron2.utils.visualizer import Visualizer\n","from detectree2.models.train import combine_dicts\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","from PIL import Image\n","import numpy as np\n","import cv2\n","import matplotlib.pyplot as plt\n","from IPython.display import display\n","\n","val_fold = 1\n","name = \"Paracou\"\n","tiles = \"/tilesMS_\" + appends + \"/train\"\n","train_location = \"/content/drive/MyDrive/WORK/detectree2/data/\" + name + tiles\n","dataset_dicts = combine_dicts(train_location, val_fold)\n","trees_metadata = MetadataCatalog.get(name + \"_train\")\n","\n","# Function to normalize and convert multi-band image to RGB if needed\n","def prepare_image_for_visualization(image):\n"," if image.shape[2] == 3:\n"," # If the image has 3 bands, assume it's RGB\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n"," else:\n"," # If the image has more than 3 bands, choose the first 3 for visualization\n"," image = image[:, :, :3] # Or select specific bands\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n","\n"," return image\n","\n","# Visualize each image in the dataset\n","for d in dataset_dicts:\n"," with rasterio.open(d[\"file_name\"]) as src:\n"," img = src.read() # Read all bands\n"," img = np.transpose(img, (1, 2, 0)) # Convert to HWC format\n"," img = prepare_image_for_visualization(img) # Normalize and prepare for visualization\n","\n"," visualizer = Visualizer(img[:, :, ::-1]*10, metadata=trees_metadata, scale=0.5)\n"," out = visualizer.draw_dataset_dict(d)\n"," image = out.get_image()[:, :, ::-1]\n"," display(Image.fromarray(image))"]}],"metadata":{"colab":{"machine_shape":"hm","name":"","provenance":[{"file_id":"1F8T-oU2Ru0TWKeWzbK8UOJIx1GcDJ_Fn","timestamp":1656085989617}],"version":""},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/notebooks/241106_colab_JB/trainingJB.ipynb b/notebooks/241106_colab_JB/trainingJB.ipynb new file mode 100644 index 00000000..da05439e --- /dev/null +++ b/notebooks/241106_colab_JB/trainingJB.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"JmM04nS5rSrO"},"source":["Install package and load drive\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":272145,"status":"ok","timestamp":1672837899062,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"iZqHRLindp70","outputId":"fb18597e-19ee-4792-9823-ca589bdab54e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting git+https://github.com/PatBall1/detectree2.git\n"," Cloning https://github.com/PatBall1/detectree2.git to /tmp/pip-req-build-g236eocg\n"," Running command git clone --filter=blob:none --quiet https://github.com/PatBall1/detectree2.git /tmp/pip-req-build-g236eocg\n"," Resolved https://github.com/PatBall1/detectree2.git to commit b641b7a1d3f605997122b86e9556a8cb720c92a2\n"," Preparing metadata (setup.py) ... 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(3.8.2)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==0.0.1) (0.4.8)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->detectron2@ git+https://github.com/facebookresearch/detectron2.git->detectree2==0.0.1) (3.2.2)\n","Building wheels for collected packages: detectree2, pyyaml, detectron2, pycrs, fvcore, antlr4-python3-runtime\n"," Building wheel for detectree2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for detectree2: filename=detectree2-0.0.1-py3-none-any.whl size=42144 sha256=c664226bc2a1b3e1eb9263704e16644b986cb0857939bfeb85fb008ad762cdf5\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-u2hhs7n5/wheels/f2/eb/98/155a78ff37f15ea402c1a02c52852aa97ceadea01162e8d112\n"," Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pyyaml: filename=PyYAML-5.1-cp38-cp38-linux_x86_64.whl size=44089 sha256=ba624da2a9b013ebafff98e1c8700bccb7f0a36bb2dcf531b33e7f441415af17\n"," Stored in directory: /root/.cache/pip/wheels/52/dd/2b/10ff8b0ac81b93946bb5fb9e6749bae2dac246506c8774e6cf\n"," Building wheel for detectron2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for detectron2: filename=detectron2-0.6-cp38-cp38-linux_x86_64.whl size=5462673 sha256=0e0ee34bb68d1d731ec80b396e1e02c085f3617321f3fb95f8c7f6a03b261288\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-u2hhs7n5/wheels/19/ac/65/e48e5e4ec2702274d927c5a6efb75709b24014371d3bb778f2\n"," Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pycrs: filename=PyCRS-1.0.2-py3-none-any.whl size=32703 sha256=b4e45b088d6e04539bce35044aec3b0c1259566aef5f1e58a4a40d498a6e808d\n"," Stored in directory: /root/.cache/pip/wheels/c1/e9/f3/19ecf82bebc5cdaba5c2a83f673f7b9b09c26fbc9b57534a2e\n"," Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61431 sha256=063b28716f1628e3efb656fb61a47824166d500efecd644628a5eb3bc5b93db3\n"," Stored in directory: /root/.cache/pip/wheels/b8/79/07/c0e9367f5b5ea325e246bd73651e8af175fabbef943043b1cc\n"," Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144575 sha256=5d9168d97797df9c2aeccfc6ef0b97fb88af2e09dffbdd311286862ed4a1489a\n"," Stored in directory: /root/.cache/pip/wheels/b1/a3/c2/6df046c09459b73cc9bb6c4401b0be6c47048baf9a1617c485\n","Successfully built detectree2 pyyaml detectron2 pycrs fvcore antlr4-python3-runtime\n","Installing collected packages: pypng, pycrs, mypy-extensions, antlr4-python3-runtime, affine, snuggs, pyyaml, pyproj, pygeos, portalocker, pathspec, munch, MarkupSafe, itsdangerous, click, yacs, Werkzeug, omegaconf, Jinja2, iopath, huggingface-hub, cligj, click-plugins, black, arrow, timm, rasterio, proj, hydra-core, fvcore, flask, fiona, geos, geopandas, detectron2, detectree2\n"," Attempting uninstall: pyyaml\n"," Found existing installation: PyYAML 6.0\n"," Uninstalling PyYAML-6.0:\n"," Successfully uninstalled PyYAML-6.0\n"," Attempting uninstall: MarkupSafe\n"," Found existing installation: MarkupSafe 2.0.1\n"," Uninstalling MarkupSafe-2.0.1:\n"," Successfully uninstalled MarkupSafe-2.0.1\n"," Attempting uninstall: itsdangerous\n"," Found existing installation: itsdangerous 1.1.0\n"," Uninstalling itsdangerous-1.1.0:\n"," Successfully uninstalled itsdangerous-1.1.0\n"," Attempting uninstall: click\n"," Found existing installation: click 7.1.2\n"," Uninstalling click-7.1.2:\n"," Successfully uninstalled click-7.1.2\n"," Attempting uninstall: Werkzeug\n"," Found existing installation: Werkzeug 1.0.1\n"," Uninstalling Werkzeug-1.0.1:\n"," Successfully uninstalled Werkzeug-1.0.1\n"," Attempting uninstall: Jinja2\n"," Found existing installation: Jinja2 2.11.3\n"," Uninstalling Jinja2-2.11.3:\n"," Successfully uninstalled Jinja2-2.11.3\n"," Attempting uninstall: flask\n"," Found existing installation: Flask 1.1.4\n"," Uninstalling Flask-1.1.4:\n"," Successfully uninstalled Flask-1.1.4\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","notebook 5.7.16 requires jinja2<=3.0.0, but you have jinja2 3.1.2 which is incompatible.\n","dask 2022.2.1 requires pyyaml>=5.3.1, but you have pyyaml 5.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Jinja2-3.1.2 MarkupSafe-2.1.1 Werkzeug-2.2.2 affine-2.3.1 antlr4-python3-runtime-4.9.3 arrow-1.2.3 black-22.12.0 click-8.1.3 click-plugins-1.1.1 cligj-0.7.2 detectree2-0.0.1 detectron2-0.6 fiona-1.8.22 flask-2.2.2 fvcore-0.1.5.post20221221 geopandas-0.12.2 geos-0.2.3 huggingface-hub-0.11.1 hydra-core-1.3.1 iopath-0.1.9 itsdangerous-2.1.2 munch-2.5.0 mypy-extensions-0.4.3 omegaconf-2.3.0 pathspec-0.10.3 portalocker-2.6.0 proj-0.2.0 pycrs-1.0.2 pygeos-0.14 pypng-0.20220715.0 pyproj-3.4.1 pyyaml-5.1 rasterio-1.3a3 snuggs-1.4.7 timm-0.6.12 yacs-0.1.8\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","!pip install git+https://github.com/PatBall1/detectree2.git"]},{"cell_type":"markdown","metadata":{"id":"kiFolF2ywysk"},"source":["Registering the training (and validation) data. It is possible to register all the locations below.\n","\n","\n","Can duplicate to register many train/val folders (e.g. if you have multiple sites to train across)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_rTWZ3IDLvrF"},"outputs":[],"source":["from detectree2.models.train import register_train_data, remove_registered_data\n","val_fold = 4\n","#appends = \"30_30_0.4\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UYd6eqoNvz-V","executionInfo":{"status":"error","timestamp":1670934865526,"user_tz":0,"elapsed":34,"user":{"displayName":"James Ball","userId":"12200917192257062155"}},"colab":{"base_uri":"https://localhost:8080/","height":381},"outputId":"4827d10d-59c8-4d96-eeca-011cf935f852"},"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mremove_registered_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Paracou2016\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mremove_registered_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Paracou2019\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#remove_registered_data(\"Danum\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#remove_registered_data(\"SepilokE\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#remove_registered_data(\"SepilokW\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/detectree2/models/train.py\u001b[0m in \u001b[0;36mremove_registered_data\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mremove_registered_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tree\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 474\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"val\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m \u001b[0mDatasetCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 476\u001b[0m \u001b[0mMetadataCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/detectron2/data/catalog.py\u001b[0m in \u001b[0;36mremove\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mAlias\u001b[0m \u001b[0mof\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \"\"\"\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.8/_collections_abc.py\u001b[0m in \u001b[0;36mpop\u001b[0;34m(self, key, default)\u001b[0m\n\u001b[1;32m 793\u001b[0m '''\n\u001b[1;32m 794\u001b[0m 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\u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__missing__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1009\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1011\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1012\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__delitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'Paracou2016_train'"]}],"source":["#remove_registered_data(\"Paracou2016\")\n","#remove_registered_data(\"Paracou2019\")\n","#remove_registered_data(\"Danum\")\n","#remove_registered_data(\"SepilokE\")\n","#remove_registered_data(\"SepilokW\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lRw28MFLrtt2"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Paracou/tiles2016_\" + appends + \"/train/\"\n","register_train_data(train_location, \"Paracou2016\", val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yBh-2ZYbkFZC"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Paracou/tiles2019_\" + appends + \"/train/\"\n","register_train_data(train_location, \"Paracou2019\", val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m3mYWFRYzl6l"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Paracou/tilesUAV_\" + appends +\"/train/\"\n","register_train_data(train_location, \"ParacouUAV\", val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yFIzP5ObvFBm"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Danum/tiles_\" + appends + \"/train/\"\n","register_train_data(train_location, 'Danum', val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VvQSjkTkvFWx"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Sepilok/tilesE_\" + appends + \"/train/\"\n","register_train_data(train_location, 'SepilokE', val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cqvWBREfvTOS"},"outputs":[],"source":["appends = \"30_30_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/Sepilok/tilesW_\" + appends + \"/train/\"\n","register_train_data(train_location, 'SepilokW', val_fold)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JmaBsaQ_nVuc"},"outputs":[],"source":["appends = \"50_20_0.4\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/BCI_50ha/tiles_\" + appends + \"/train/\"\n","register_train_data(train_location, 'BCI_50ha', val_fold)"]},{"cell_type":"markdown","metadata":{"id":"eatbh46KxH1T"},"source":["## Visualise training data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","output_embedded_package_id":"1o0lHaz215aJaNqllL7AAzcQDUkFI1VRU","height":1000},"id":"q4OQr2PoL9-2","outputId":"e8f97960-34a5-4082-81cd-af1b4bbd4741","executionInfo":{"status":"ok","timestamp":1665597132389,"user_tz":-60,"elapsed":62204,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["from detectron2.utils.visualizer import Visualizer\n","from detectree2.models.train import combine_dicts\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","import cv2\n","from PIL import Image\n","\n","name = \"Sepilok\"\n","tiles = \"/tilesW/train\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/\" + name + tiles\n","dataset_dicts = combine_dicts(train_location, val_fold)\n","trees_metadata = MetadataCatalog.get(name + \"_train\")\n","#dataset_dicts = get_tree_dicts(\"./\")\n","for d in dataset_dicts:\n"," img = cv2.imread(d[\"file_name\"])\n"," visualizer = Visualizer(img[:, :, ::-1], metadata=trees_metadata, scale=0.5)\n"," out = visualizer.draw_dataset_dict(d)\n"," image = cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB)\n"," display(Image.fromarray(image))"]},{"cell_type":"markdown","metadata":{"id":"Qf7nXHaNvQPq"},"source":["## Train!\n","\n","GPU/CUDA should be available here. Chose which datasets you want to train and test on with `trains` and `tests`. Set up the configurations with `setup_cfg`.\n","\n","If tuning has been completed, train and validation datasets can be combined in `trains` for full training."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pHsXWYepud8W"},"outputs":[],"source":["from detectree2.models.train import MyTrainer, setup_cfg\n","#from detectree2.models.train import setup_cfg\n","\n","# Set the base (pre-trained) model from the detectron2 model_zoo\n","base_model = \"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\"\n","# Set the names of the registered train and test sets\n","# pretrained model?\n","# trained_model = \"/content/drive/Shareddrives/detectree2/models/220629_ParacouSepilokDanum_JB.pth\"\n","#trains = (\"Paracou_train\", \"Paracou2019_train\", \"ParacouUAV_train\", \"Danum_train\", \"SepilokEast_train\", \"SepilokWest_train\")\n","#tests = (\"Paracou_val\", \"Paracou2019_val\", \"ParacouUAV_val\", \"Danum_val\", \"SepilokEast_val\", \"SepilokWest_val\")\n","\n","#trains = (\"BCI_50ha_train\",)\n","#tests = (\"BCI_50ha_val\",)\n","#out_dir = \"/content/drive/Shareddrives/detectree2/220818_Paracou\"\n","\n","#cfg = setup_cfg(base_model, trains, tests, workers = 4, eval_period=100, max_iter=3000, out_dir=out_dir) # update_model arg can be used to load in trained model"]},{"cell_type":"markdown","metadata":{"id":"RdEteDyVXMgn"},"source":["Get training! Patience sets the number of evaluation periods that will be undergone without improvement in model performance before training will be terminated (best model will be saved)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VM0bZ9ucz7IN"},"outputs":[],"source":["from datetime import date\n","\n","today = date.today()\n","today = today.strftime(\"%y%m%d\")"]},{"cell_type":"markdown","source":["To train the model sequentially on a series of sites, loop over the \"names\""],"metadata":{"id":"E-hRk4ce6buJ"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1541735,"status":"error","timestamp":1670513583665,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"Ws-1-_z1e7-4","outputId":"3524971e-04f7-41c8-936b-155df34cbe26"},"outputs":[{"output_type":"stream","name":"stdout","text":["[12/08 15:07:30 d2.engine.defaults]: Model:\n","GeneralizedRCNN(\n"," (backbone): FPN(\n"," (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (top_block): LastLevelMaxPool()\n"," (bottom_up): ResNet(\n"," (stem): BasicStem(\n"," (conv1): Conv2d(\n"," 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," )\n"," (res2): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res3): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res4): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (4): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (5): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (6): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (7): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (8): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (9): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (10): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (11): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (12): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (13): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (14): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (15): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (16): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (17): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (18): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (19): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (20): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (21): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (22): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res5): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," )\n"," )\n"," )\n"," (proposal_generator): RPN(\n"," (rpn_head): StandardRPNHead(\n"," (conv): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n"," (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n"," )\n"," (anchor_generator): DefaultAnchorGenerator(\n"," (cell_anchors): BufferList()\n"," )\n"," )\n"," (roi_heads): StandardROIHeads(\n"," (box_pooler): ROIPooler(\n"," (level_poolers): ModuleList(\n"," (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"," (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"," (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"," (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"," )\n"," )\n"," (box_head): FastRCNNConvFCHead(\n"," (flatten): Flatten(start_dim=1, end_dim=-1)\n"," (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n"," (fc_relu1): ReLU()\n"," (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n"," (fc_relu2): ReLU()\n"," )\n"," (box_predictor): FastRCNNOutputLayers(\n"," (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n"," (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n"," )\n"," (mask_pooler): ROIPooler(\n"," (level_poolers): ModuleList(\n"," (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"," (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"," (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"," (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"," )\n"," )\n"," (mask_head): MaskRCNNConvUpsampleHead(\n"," (mask_fcn1): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn3): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn4): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n"," (deconv_relu): ReLU()\n"," (predictor): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n"," )\n"," )\n",")\n","[12/08 15:12:02 d2.data.build]: Removed 0 images with no usable annotations. 87 images left.\n","[12/08 15:12:02 d2.data.build]: Distribution of instances among all 1 categories:\n","| category | #instances |\n","|:----------:|:-------------|\n","| tree | 4669 |\n","| | |\n","[12/08 15:12:02 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(1000, 1000), max_size=1333, sample_style='choice'), RandomFlip()]\n","[12/08 15:12:02 d2.data.build]: Using training sampler TrainingSampler\n","[12/08 15:12:02 d2.data.common]: Serializing the dataset using: \n","[12/08 15:12:02 d2.data.common]: Serializing 87 elements to byte tensors and concatenating them all ...\n","[12/08 15:12:02 d2.data.common]: Serialized dataset takes 4.15 MiB\n","WARNING [12/08 15:12:02 d2.solver.build]: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:12:05 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(1000, 1000), max_size=1333, sample_style='choice'), RandomFlip()]\n","[12/08 15:12:58 d2.data.build]: Distribution of instances among all 1 categories:\n","| category | #instances |\n","|:----------:|:-------------|\n","| tree | 1135 |\n","| | |\n","[12/08 15:12:58 d2.data.common]: Serializing the dataset using: \n","[12/08 15:12:58 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:12:58 d2.data.common]: Serialized dataset takes 1.03 MiB\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:13:14 d2.checkpoint.c2_model_loading]: Following weights matched with model:\n","| Names in Model | Names in Checkpoint | Shapes |\n","|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|\n","| backbone.bottom_up.res2.0.conv1.* | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,1,1) |\n","| backbone.bottom_up.res2.0.conv2.* | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.0.conv3.* | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.0.shortcut.* | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.1.conv1.* | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |\n","| backbone.bottom_up.res2.1.conv2.* | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.1.conv3.* | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.2.conv1.* | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |\n","| backbone.bottom_up.res2.2.conv2.* | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.2.conv3.* | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res3.0.conv1.* | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,256,1,1) |\n","| backbone.bottom_up.res3.0.conv2.* | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.0.conv3.* | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.0.shortcut.* | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1) |\n","| backbone.bottom_up.res3.1.conv1.* | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.1.conv2.* | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.1.conv3.* | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.2.conv1.* | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.2.conv2.* | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.2.conv3.* | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.3.conv1.* | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.3.conv2.* | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.3.conv3.* | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res4.0.conv1.* | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,512,1,1) |\n","| backbone.bottom_up.res4.0.conv2.* | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.0.conv3.* | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.0.shortcut.* | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1) |\n","| backbone.bottom_up.res4.1.conv1.* | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.1.conv2.* | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.1.conv3.* | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.10.conv1.* | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.10.conv2.* | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.10.conv3.* | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.11.conv1.* | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.11.conv2.* | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.11.conv3.* | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.12.conv1.* | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.12.conv2.* | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.12.conv3.* | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.13.conv1.* | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.13.conv2.* | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.13.conv3.* | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.14.conv1.* | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.14.conv2.* | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.14.conv3.* | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.15.conv1.* | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.15.conv2.* | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.15.conv3.* | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.16.conv1.* | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.16.conv2.* | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.16.conv3.* | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.17.conv1.* | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.17.conv2.* | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.17.conv3.* | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.18.conv1.* | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.18.conv2.* | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.18.conv3.* | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.19.conv1.* | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.19.conv2.* | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.19.conv3.* | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.2.conv1.* | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.2.conv2.* | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.2.conv3.* | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.20.conv1.* | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.20.conv2.* | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.20.conv3.* | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.21.conv1.* | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.21.conv2.* | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.21.conv3.* | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.22.conv1.* | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.22.conv2.* | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.22.conv3.* | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.3.conv1.* | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.3.conv2.* | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.3.conv3.* | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.4.conv1.* | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.4.conv2.* | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.4.conv3.* | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.5.conv1.* | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.5.conv2.* | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.5.conv3.* | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.6.conv1.* | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.6.conv2.* | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.6.conv3.* | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.7.conv1.* | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.7.conv2.* | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.7.conv3.* | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.8.conv1.* | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.8.conv2.* | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.8.conv3.* | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.9.conv1.* | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.9.conv2.* | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.9.conv3.* | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res5.0.conv1.* | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,1024,1,1) |\n","| backbone.bottom_up.res5.0.conv2.* | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.0.conv3.* | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.res5.0.shortcut.* | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n","| backbone.bottom_up.res5.1.conv1.* | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |\n","| backbone.bottom_up.res5.1.conv2.* | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.1.conv3.* | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.res5.2.conv1.* | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |\n","| backbone.bottom_up.res5.2.conv2.* | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.2.conv3.* | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.stem.conv1.* | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,3,7,7) |\n","| backbone.fpn_lateral2.* | backbone.fpn_lateral2.{bias,weight} | (256,) (256,256,1,1) |\n","| backbone.fpn_lateral3.* | backbone.fpn_lateral3.{bias,weight} | (256,) (256,512,1,1) |\n","| backbone.fpn_lateral4.* | backbone.fpn_lateral4.{bias,weight} | (256,) (256,1024,1,1) |\n","| backbone.fpn_lateral5.* | backbone.fpn_lateral5.{bias,weight} | (256,) (256,2048,1,1) |\n","| backbone.fpn_output2.* | backbone.fpn_output2.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output3.* | backbone.fpn_output3.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output4.* | backbone.fpn_output4.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output5.* | backbone.fpn_output5.{bias,weight} | (256,) (256,256,3,3) |\n","| proposal_generator.rpn_head.anchor_deltas.* | proposal_generator.rpn_head.anchor_deltas.{bias,weight} | (12,) (12,256,1,1) |\n","| proposal_generator.rpn_head.conv.* | proposal_generator.rpn_head.conv.{bias,weight} | (256,) (256,256,3,3) |\n","| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight} | (3,) (3,256,1,1) |\n","| roi_heads.box_head.fc1.* | roi_heads.box_head.fc1.{bias,weight} | (1024,) (1024,12544) |\n","| roi_heads.box_head.fc2.* | roi_heads.box_head.fc2.{bias,weight} | (1024,) (1024,1024) |\n","| roi_heads.box_predictor.bbox_pred.* | roi_heads.box_predictor.bbox_pred.{bias,weight} | (4,) (4,1024) |\n","| roi_heads.box_predictor.cls_score.* | roi_heads.box_predictor.cls_score.{bias,weight} | (2,) (2,1024) |\n","| roi_heads.mask_head.deconv.* | roi_heads.mask_head.deconv.{bias,weight} | (256,) (256,256,2,2) |\n","| roi_heads.mask_head.mask_fcn1.* | roi_heads.mask_head.mask_fcn1.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn2.* | roi_heads.mask_head.mask_fcn2.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn3.* | roi_heads.mask_head.mask_fcn3.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn4.* | roi_heads.mask_head.mask_fcn4.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.predictor.* | roi_heads.mask_head.predictor.{bias,weight} | (1,) (1,256,1,1) |\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n"," return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:13:44 d2.utils.events]: eta: 1:15:59 iter: 19 total_loss: 1.759 loss_cls: 0.4272 loss_box_reg: 0.5861 loss_mask: 0.3945 loss_rpn_cls: 0.1514 loss_rpn_loc: 0.179 time: 0.9161 data_time: 0.0351 lr: 5.3944e-05 max_mem: 3490M\n","[12/08 15:13:50 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:13:50 d2.data.common]: Serializing the dataset using: \n","[12/08 15:13:50 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:13:50 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:13:50 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:13:50 d2.evaluation.coco_evaluation]: Trying to convert 'Paracou2016_val' to COCO format ...\n","[12/08 15:13:50 d2.data.datasets.coco]: Converting annotations of dataset 'Paracou2016_val' to COCO format ...)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:13:51 d2.data.datasets.coco]: Converting dataset dicts into COCO format\n","[12/08 15:13:51 d2.data.datasets.coco]: Conversion finished, #images: 21, #annotations: 1135\n","[12/08 15:13:51 d2.data.datasets.coco]: Caching COCO format annotations at 'eval/Paracou2016_val_coco_format.json' ...\n","[12/08 15:13:51 d2.evaluation.evaluator]: Start inference on 21 batches\n","[12/08 15:13:56 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0021 s/iter. Inference: 0.1483 s/iter. Eval: 0.1949 s/iter. Total: 0.3454 s/iter. ETA=0:00:03\n","[12/08 15:14:00 d2.evaluation.evaluator]: Total inference time: 0:00:05.660819 (0.353801 s / iter per device, on 1 devices)\n","[12/08 15:14:00 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.150469 s / iter per device, on 1 devices)\n","[12/08 15:14:00 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:14:00 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:14:00 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.175\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522\n","[12/08 15:14:00 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.451 | 40.580 | 12.633 | 0.495 | 15.229 | 35.342 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:00 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.00 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.160\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.402\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.099\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.080\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.234\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463\n","[12/08 15:14:00 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 15.970 | 40.182 | 9.856 | 0.351 | 10.278 | 33.508 |\n","[12/08 15:14:00 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: 17.4507,40.5797,12.6325,0.4950,15.2295,35.3423\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:00 d2.evaluation.testing]: copypaste: 15.9695,40.1822,9.8562,0.3510,10.2783,33.5079\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:14:06 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:14:06 d2.data.common]: Serializing the dataset using: \n","[12/08 15:14:06 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:14:06 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:14:06 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:14:06 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:14:11 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0020 s/iter. Inference: 0.1498 s/iter. Eval: 0.1938 s/iter. Total: 0.3455 s/iter. ETA=0:00:03\n","[12/08 15:14:14 d2.evaluation.evaluator]: Total inference time: 0:00:05.625424 (0.351589 s / iter per device, on 1 devices)\n","[12/08 15:14:14 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149159 s / iter per device, on 1 devices)\n","[12/08 15:14:14 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:14:14 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:14:14 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.175\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522\n","[12/08 15:14:14 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.451 | 40.580 | 12.633 | 0.495 | 15.229 | 35.342 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:14 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.160\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.402\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.099\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.080\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.234\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463\n","[12/08 15:14:14 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 15.970 | 40.182 | 9.856 | 0.351 | 10.278 | 33.508 |\n","[12/08 15:14:14 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: 17.4507,40.5797,12.6325,0.4950,15.2295,35.3423\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:14 d2.evaluation.testing]: copypaste: 15.9695,40.1822,9.8562,0.3510,10.2783,33.5079\n","Av. AP50 = 40.18219669402122\n","[12/08 15:14:31 d2.utils.events]: eta: 1:16:54 iter: 39 total_loss: 1.675 loss_cls: 0.3858 loss_box_reg: 0.5875 loss_mask: 0.3957 loss_rpn_cls: 0.1343 loss_rpn_loc: 0.1497 validation_loss: 1.791 time: 0.9424 data_time: 0.0175 lr: 0.00011037 max_mem: 3490M\n","[12/08 15:14:41 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:14:41 d2.data.common]: Serializing the dataset using: \n","[12/08 15:14:41 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:14:41 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:14:41 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:14:42 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:14:47 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0017 s/iter. Inference: 0.1500 s/iter. Eval: 0.1949 s/iter. Total: 0.3466 s/iter. ETA=0:00:03\n","[12/08 15:14:50 d2.evaluation.evaluator]: Total inference time: 0:00:05.620448 (0.351278 s / iter per device, on 1 devices)\n","[12/08 15:14:50 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148628 s / iter per device, on 1 devices)\n","[12/08 15:14:50 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:14:50 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:14:50 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:14:50 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:14:50 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:14:50 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:50 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.151\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.366\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.089\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532\n","[12/08 15:14:50 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.011 | 41.891 | 13.624 | 0.512 | 15.122 | 36.622 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:14:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:14:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:14:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:14:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.110\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.100\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.473\n","[12/08 15:14:51 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.267 | 40.034 | 11.005 | 0.233 | 10.029 | 34.704 |\n","[12/08 15:14:51 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: 18.0108,41.8905,13.6241,0.5116,15.1217,36.6218\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:14:51 d2.evaluation.testing]: copypaste: 16.2672,40.0341,11.0052,0.2325,10.0285,34.7036\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:14:57 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:14:57 d2.data.common]: Serializing the dataset using: \n","[12/08 15:14:57 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:14:57 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:14:57 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:14:57 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:15:01 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0019 s/iter. Inference: 0.1508 s/iter. Eval: 0.1931 s/iter. Total: 0.3457 s/iter. ETA=0:00:03\n","[12/08 15:15:05 d2.evaluation.evaluator]: Total inference time: 0:00:05.663719 (0.353982 s / iter per device, on 1 devices)\n","[12/08 15:15:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149405 s / iter per device, on 1 devices)\n","[12/08 15:15:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:15:05 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:15:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.419\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.151\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.366\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.089\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.532\n","[12/08 15:15:05 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.011 | 41.891 | 13.624 | 0.512 | 15.122 | 36.622 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.110\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.100\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.473\n","[12/08 15:15:05 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.267 | 40.034 | 11.005 | 0.233 | 10.029 | 34.704 |\n","[12/08 15:15:05 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: 18.0108,41.8905,13.6241,0.5116,15.1217,36.6218\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:05 d2.evaluation.testing]: copypaste: 16.2672,40.0341,11.0052,0.2325,10.0285,34.7036\n","Av. AP50 = 40.03407344436922\n","[12/08 15:15:14 d2.utils.events]: eta: 1:16:10 iter: 59 total_loss: 1.654 loss_cls: 0.3926 loss_box_reg: 0.5744 loss_mask: 0.3901 loss_rpn_cls: 0.1326 loss_rpn_loc: 0.1636 validation_loss: 1.791 time: 0.9362 data_time: 0.0103 lr: 0.0001668 max_mem: 3490M\n","[12/08 15:15:29 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:15:29 d2.data.common]: Serializing the dataset using: \n","[12/08 15:15:29 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:15:29 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:15:29 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:15:29 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:15:34 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0081 s/iter. Inference: 0.1631 s/iter. Eval: 0.2893 s/iter. Total: 0.4605 s/iter. ETA=0:00:04\n","[12/08 15:15:38 d2.evaluation.evaluator]: Total inference time: 0:00:06.678987 (0.417437 s / iter per device, on 1 devices)\n","[12/08 15:15:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.156653 s / iter per device, on 1 devices)\n","[12/08 15:15:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:15:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:15:38 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.121\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522\n","[12/08 15:15:38 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.651 | 42.118 | 12.106 | 0.792 | 15.173 | 36.336 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.412\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.111\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[12/08 15:15:38 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.939 | 41.226 | 11.180 | 0.347 | 11.114 | 35.720 |\n","[12/08 15:15:38 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: 17.6509,42.1181,12.1059,0.7921,15.1733,36.3361\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:38 d2.evaluation.testing]: copypaste: 16.9386,41.2256,11.1801,0.3465,11.1144,35.7202\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:15:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:15:45 d2.data.common]: Serializing the dataset using: \n","[12/08 15:15:45 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:15:45 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:15:45 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:15:45 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:15:50 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1494 s/iter. Eval: 0.1921 s/iter. Total: 0.3428 s/iter. ETA=0:00:03\n","[12/08 15:15:53 d2.evaluation.evaluator]: Total inference time: 0:00:05.607468 (0.350467 s / iter per device, on 1 devices)\n","[12/08 15:15:53 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148724 s / iter per device, on 1 devices)\n","[12/08 15:15:53 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:15:53 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:15:53 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:15:53 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:15:53 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:15:53 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:53 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.421\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.121\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522\n","[12/08 15:15:53 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.651 | 42.118 | 12.106 | 0.792 | 15.173 | 36.336 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:15:54 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:15:54 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:15:54 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:15:54 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.412\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.111\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[12/08 15:15:54 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.939 | 41.226 | 11.180 | 0.347 | 11.114 | 35.720 |\n","[12/08 15:15:54 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: 17.6509,42.1181,12.1059,0.7921,15.1733,36.3361\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:15:54 d2.evaluation.testing]: copypaste: 16.9386,41.2256,11.1801,0.3465,11.1144,35.7202\n","Av. AP50 = 41.22562383429986\n","[12/08 15:16:00 d2.utils.events]: eta: 1:16:09 iter: 79 total_loss: 1.749 loss_cls: 0.4002 loss_box_reg: 0.5685 loss_mask: 0.3881 loss_rpn_cls: 0.143 loss_rpn_loc: 0.1928 validation_loss: 1.79 time: 0.9350 data_time: 0.0102 lr: 0.00022322 max_mem: 3490M\n","[12/08 15:16:19 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:16:19 d2.data.common]: Serializing the dataset using: \n","[12/08 15:16:19 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:16:19 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:16:19 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:16:19 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:16:25 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1568 s/iter. Eval: 0.2877 s/iter. Total: 0.4461 s/iter. ETA=0:00:04\n","[12/08 15:16:29 d2.evaluation.evaluator]: Total inference time: 0:00:06.884386 (0.430274 s / iter per device, on 1 devices)\n","[12/08 15:16:29 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.153689 s / iter per device, on 1 devices)\n","[12/08 15:16:29 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:16:29 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:16:29 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.088\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533\n","[12/08 15:16:29 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.997 | 42.336 | 12.593 | 0.891 | 15.247 | 36.800 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:16:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.416\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.101\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.111\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.351\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483\n","[12/08 15:16:29 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.874 | 41.618 | 10.107 | 0.688 | 11.100 | 35.112 |\n","[12/08 15:16:29 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: 17.9974,42.3356,12.5927,0.8911,15.2470,36.8000\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:16:29 d2.evaluation.testing]: copypaste: 16.8740,41.6180,10.1073,0.6884,11.0999,35.1116\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:16:35 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:16:35 d2.data.common]: Serializing the dataset using: \n","[12/08 15:16:35 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:16:35 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:16:35 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:16:35 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:16:40 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0050 s/iter. Inference: 0.1664 s/iter. Eval: 0.2814 s/iter. Total: 0.4528 s/iter. ETA=0:00:04\n","[12/08 15:16:44 d2.evaluation.evaluator]: Total inference time: 0:00:06.679827 (0.417489 s / iter per device, on 1 devices)\n","[12/08 15:16:44 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.156947 s / iter per device, on 1 devices)\n","[12/08 15:16:44 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:16:44 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:16:44 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.088\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.248\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533\n","[12/08 15:16:44 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.997 | 42.336 | 12.593 | 0.891 | 15.247 | 36.800 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:16:44 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.416\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.101\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.111\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.351\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.083\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483\n","[12/08 15:16:44 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 16.874 | 41.618 | 10.107 | 0.688 | 11.100 | 35.112 |\n","[12/08 15:16:45 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: 17.9974,42.3356,12.5927,0.8911,15.2470,36.8000\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:16:45 d2.evaluation.testing]: copypaste: 16.8740,41.6180,10.1073,0.6884,11.0999,35.1116\n","Av. AP50 = 41.61798223245485\n","[12/08 15:16:46 d2.utils.events]: eta: 1:15:53 iter: 99 total_loss: 1.685 loss_cls: 0.3854 loss_box_reg: 0.5506 loss_mask: 0.3868 loss_rpn_cls: 0.1461 loss_rpn_loc: 0.2266 validation_loss: 1.782 time: 0.9341 data_time: 0.0139 lr: 0.00027965 max_mem: 3490M\n","[12/08 15:17:05 d2.utils.events]: eta: 1:15:36 iter: 119 total_loss: 1.666 loss_cls: 0.3923 loss_box_reg: 0.5558 loss_mask: 0.391 loss_rpn_cls: 0.1374 loss_rpn_loc: 0.179 validation_loss: 1.782 time: 0.9355 data_time: 0.0109 lr: 0.00033608 max_mem: 3490M\n","[12/08 15:17:10 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:17:10 d2.data.common]: Serializing the dataset using: \n","[12/08 15:17:10 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:17:11 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:17:11 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:17:11 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:17:15 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0055 s/iter. Inference: 0.1565 s/iter. Eval: 0.2586 s/iter. Total: 0.4206 s/iter. ETA=0:00:04\n","[12/08 15:17:20 d2.evaluation.evaluator]: Total inference time: 0:00:06.778775 (0.423673 s / iter per device, on 1 devices)\n","[12/08 15:17:20 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.155272 s / iter per device, on 1 devices)\n","[12/08 15:17:20 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:17:20 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:17:20 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.467\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.167\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n","[12/08 15:17:20 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.272 | 46.683 | 16.245 | 0.825 | 16.695 | 40.154 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:17:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.131\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496\n","[12/08 15:17:20 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.743 | 45.208 | 13.081 | 0.330 | 11.977 | 38.453 |\n","[12/08 15:17:20 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: 20.2716,46.6835,16.2446,0.8251,16.6945,40.1539\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:17:20 d2.evaluation.testing]: copypaste: 18.7426,45.2079,13.0814,0.3300,11.9768,38.4531\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:17:26 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:17:26 d2.data.common]: Serializing the dataset using: \n","[12/08 15:17:26 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:17:26 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:17:26 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:17:26 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:17:31 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0031 s/iter. Inference: 0.1500 s/iter. Eval: 0.1928 s/iter. Total: 0.3459 s/iter. ETA=0:00:03\n","[12/08 15:17:34 d2.evaluation.evaluator]: Total inference time: 0:00:05.643311 (0.352707 s / iter per device, on 1 devices)\n","[12/08 15:17:34 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148851 s / iter per device, on 1 devices)\n","[12/08 15:17:34 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:17:34 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:17:34 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.467\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.167\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n","[12/08 15:17:34 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.272 | 46.683 | 16.245 | 0.825 | 16.695 | 40.154 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:17:34 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.187\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.131\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496\n","[12/08 15:17:34 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.743 | 45.208 | 13.081 | 0.330 | 11.977 | 38.453 |\n","[12/08 15:17:34 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: 20.2716,46.6835,16.2446,0.8251,16.6945,40.1539\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:17:34 d2.evaluation.testing]: copypaste: 18.7426,45.2079,13.0814,0.3300,11.9768,38.4531\n","Av. AP50 = 45.2079092489683\n","[12/08 15:17:50 d2.utils.events]: eta: 1:15:18 iter: 139 total_loss: 1.694 loss_cls: 0.3958 loss_box_reg: 0.5558 loss_mask: 0.3789 loss_rpn_cls: 0.1374 loss_rpn_loc: 0.2452 validation_loss: 1.773 time: 0.9347 data_time: 0.0106 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:18:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:18:00 d2.data.common]: Serializing the dataset using: \n","[12/08 15:18:00 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:18:00 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:18:00 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:18:00 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:18:05 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1498 s/iter. Eval: 0.1923 s/iter. Total: 0.3439 s/iter. ETA=0:00:03\n","[12/08 15:18:09 d2.evaluation.evaluator]: Total inference time: 0:00:06.580770 (0.411298 s / iter per device, on 1 devices)\n","[12/08 15:18:09 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.150291 s / iter per device, on 1 devices)\n","[12/08 15:18:09 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:18:09 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:18:09 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:18:09 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:18:09 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[12/08 15:18:09 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:09 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.444\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.150\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538\n","[12/08 15:18:09 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.251 | 44.414 | 15.019 | 0.723 | 16.643 | 38.392 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[12/08 15:18:10 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:18:10 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[12/08 15:18:10 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:10 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.431\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.123\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.246\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.495\n","[12/08 15:18:10 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.985 | 43.116 | 12.291 | 0.652 | 11.748 | 37.308 |\n","[12/08 15:18:10 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: 19.2508,44.4145,15.0192,0.7228,16.6430,38.3916\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:10 d2.evaluation.testing]: copypaste: 17.9852,43.1155,12.2914,0.6517,11.7482,37.3078\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:18:16 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:18:16 d2.data.common]: Serializing the dataset using: \n","[12/08 15:18:16 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:18:16 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:18:16 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:18:16 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:18:20 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1475 s/iter. Eval: 0.1951 s/iter. Total: 0.3439 s/iter. ETA=0:00:03\n","[12/08 15:18:24 d2.evaluation.evaluator]: Total inference time: 0:00:05.645444 (0.352840 s / iter per device, on 1 devices)\n","[12/08 15:18:24 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148178 s / iter per device, on 1 devices)\n","[12/08 15:18:24 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:18:24 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:18:24 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.444\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.150\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538\n","[12/08 15:18:24 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.251 | 44.414 | 15.019 | 0.723 | 16.643 | 38.392 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:24 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.431\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.123\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.246\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.495\n","[12/08 15:18:24 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 17.985 | 43.116 | 12.291 | 0.652 | 11.748 | 37.308 |\n","[12/08 15:18:24 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: 19.2508,44.4145,15.0192,0.7228,16.6430,38.3916\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:24 d2.evaluation.testing]: copypaste: 17.9852,43.1155,12.2914,0.6517,11.7482,37.3078\n","Av. AP50 = 43.11552833307133\n","[12/08 15:18:33 d2.utils.events]: eta: 1:14:56 iter: 159 total_loss: 1.624 loss_cls: 0.3755 loss_box_reg: 0.5359 loss_mask: 0.3698 loss_rpn_cls: 0.1301 loss_rpn_loc: 0.1841 validation_loss: 1.751 time: 0.9335 data_time: 0.0109 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:18:48 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:18:48 d2.data.common]: Serializing the dataset using: \n","[12/08 15:18:48 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:18:48 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:18:48 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:18:48 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:18:53 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1495 s/iter. Eval: 0.1938 s/iter. Total: 0.3445 s/iter. ETA=0:00:03\n","[12/08 15:18:56 d2.evaluation.evaluator]: Total inference time: 0:00:05.645662 (0.352854 s / iter per device, on 1 devices)\n","[12/08 15:18:56 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148028 s / iter per device, on 1 devices)\n","[12/08 15:18:56 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:18:56 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:18:56 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547\n","[12/08 15:18:56 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.251 | 45.967 | 15.444 | 1.274 | 16.774 | 40.363 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:18:56 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.133\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496\n","[12/08 15:18:56 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.066 | 45.469 | 13.300 | 0.805 | 12.576 | 38.922 |\n","[12/08 15:18:56 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: 20.2506,45.9669,15.4443,1.2739,16.7742,40.3626\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:18:56 d2.evaluation.testing]: copypaste: 19.0656,45.4695,13.3003,0.8049,12.5763,38.9221\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:19:03 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:19:03 d2.data.common]: Serializing the dataset using: \n","[12/08 15:19:03 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:19:03 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:19:03 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:19:03 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:19:08 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1501 s/iter. Eval: 0.1922 s/iter. Total: 0.3437 s/iter. ETA=0:00:03\n","[12/08 15:19:11 d2.evaluation.evaluator]: Total inference time: 0:00:05.608891 (0.350556 s / iter per device, on 1 devices)\n","[12/08 15:19:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148905 s / iter per device, on 1 devices)\n","[12/08 15:19:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:19:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:19:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547\n","[12/08 15:19:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.251 | 45.967 | 15.444 | 1.274 | 16.774 | 40.363 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:19:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.133\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.126\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.496\n","[12/08 15:19:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.066 | 45.469 | 13.300 | 0.805 | 12.576 | 38.922 |\n","[12/08 15:19:11 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: 20.2506,45.9669,15.4443,1.2739,16.7742,40.3626\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:19:11 d2.evaluation.testing]: copypaste: 19.0656,45.4695,13.3003,0.8049,12.5763,38.9221\n","Av. AP50 = 45.4694570098791\n","[12/08 15:19:18 d2.utils.events]: eta: 1:14:33 iter: 179 total_loss: 1.59 loss_cls: 0.3544 loss_box_reg: 0.5237 loss_mask: 0.383 loss_rpn_cls: 0.1353 loss_rpn_loc: 0.1639 validation_loss: 1.729 time: 0.9337 data_time: 0.0137 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:19:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:19:38 d2.data.common]: Serializing the dataset using: \n","[12/08 15:19:38 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:19:38 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:19:38 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:19:38 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:19:42 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1494 s/iter. Eval: 0.1932 s/iter. Total: 0.3443 s/iter. ETA=0:00:03\n","[12/08 15:19:46 d2.evaluation.evaluator]: Total inference time: 0:00:05.627943 (0.351746 s / iter per device, on 1 devices)\n","[12/08 15:19:46 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148924 s / iter per device, on 1 devices)\n","[12/08 15:19:46 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:19:46 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:19:46 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.198\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.451\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.092\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553\n","[12/08 15:19:46 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.794 | 45.075 | 15.444 | 0.984 | 16.940 | 40.075 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:19:46 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.184\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.436\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.503\n","[12/08 15:19:46 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.428 | 43.644 | 13.351 | 0.778 | 11.890 | 38.364 |\n","[12/08 15:19:46 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: 19.7936,45.0750,15.4436,0.9840,16.9395,40.0755\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:19:46 d2.evaluation.testing]: copypaste: 18.4283,43.6439,13.3509,0.7778,11.8902,38.3639\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:19:52 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:19:52 d2.data.common]: Serializing the dataset using: \n","[12/08 15:19:52 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:19:52 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:19:52 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:19:52 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:19:57 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0010 s/iter. Inference: 0.1486 s/iter. Eval: 0.1930 s/iter. Total: 0.3426 s/iter. ETA=0:00:03\n","[12/08 15:20:01 d2.evaluation.evaluator]: Total inference time: 0:00:05.641872 (0.352617 s / iter per device, on 1 devices)\n","[12/08 15:20:01 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148078 s / iter per device, on 1 devices)\n","[12/08 15:20:01 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:20:01 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:20:01 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.198\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.451\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.092\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553\n","[12/08 15:20:01 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.794 | 45.075 | 15.444 | 0.984 | 16.940 | 40.075 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.184\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.436\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.503\n","[12/08 15:20:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 18.428 | 43.644 | 13.351 | 0.778 | 11.890 | 38.364 |\n","[12/08 15:20:01 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: 19.7936,45.0750,15.4436,0.9840,16.9395,40.0755\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:01 d2.evaluation.testing]: copypaste: 18.4283,43.6439,13.3509,0.7778,11.8902,38.3639\n","Av. AP50 = 43.64393489754535\n","[12/08 15:20:01 d2.utils.events]: eta: 1:14:22 iter: 199 total_loss: 1.583 loss_cls: 0.3546 loss_box_reg: 0.5203 loss_mask: 0.3735 loss_rpn_cls: 0.1252 loss_rpn_loc: 0.2015 validation_loss: 1.72 time: 0.9335 data_time: 0.0136 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:20:20 d2.utils.events]: eta: 1:14:02 iter: 219 total_loss: 1.597 loss_cls: 0.3747 loss_box_reg: 0.5248 loss_mask: 0.3725 loss_rpn_cls: 0.1294 loss_rpn_loc: 0.1763 validation_loss: 1.72 time: 0.9330 data_time: 0.0145 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:20:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:20:25 d2.data.common]: Serializing the dataset using: \n","[12/08 15:20:25 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:20:25 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:20:25 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:20:25 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:20:30 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0012 s/iter. Inference: 0.1511 s/iter. Eval: 0.1933 s/iter. Total: 0.3455 s/iter. ETA=0:00:03\n","[12/08 15:20:33 d2.evaluation.evaluator]: Total inference time: 0:00:05.673396 (0.354587 s / iter per device, on 1 devices)\n","[12/08 15:20:33 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149657 s / iter per device, on 1 devices)\n","[12/08 15:20:33 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:20:33 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:20:33 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n","[12/08 15:20:33 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.505 | 45.775 | 15.856 | 0.792 | 17.207 | 41.036 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:33 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.443\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.397\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:20:33 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.119 | 44.308 | 14.014 | 0.581 | 12.531 | 39.718 |\n","[12/08 15:20:33 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: 20.5051,45.7747,15.8558,0.7921,17.2074,41.0362\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:33 d2.evaluation.testing]: copypaste: 19.1191,44.3077,14.0136,0.5809,12.5314,39.7176\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:20:40 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:20:40 d2.data.common]: Serializing the dataset using: \n","[12/08 15:20:40 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:20:40 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:20:40 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:20:40 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:20:45 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0050 s/iter. Inference: 0.1675 s/iter. Eval: 0.2891 s/iter. Total: 0.4615 s/iter. ETA=0:00:04\n","[12/08 15:20:49 d2.evaluation.evaluator]: Total inference time: 0:00:06.442755 (0.402672 s / iter per device, on 1 devices)\n","[12/08 15:20:49 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.157473 s / iter per device, on 1 devices)\n","[12/08 15:20:49 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:20:49 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:20:49 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n","[12/08 15:20:49 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.505 | 45.775 | 15.856 | 0.792 | 17.207 | 41.036 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:20:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.443\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.140\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.397\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:20:49 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.119 | 44.308 | 14.014 | 0.581 | 12.531 | 39.718 |\n","[12/08 15:20:49 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: 20.5051,45.7747,15.8558,0.7921,17.2074,41.0362\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:20:49 d2.evaluation.testing]: copypaste: 19.1191,44.3077,14.0136,0.5809,12.5314,39.7176\n","Av. AP50 = 44.30768854226651\n","[12/08 15:21:03 d2.utils.events]: eta: 1:13:37 iter: 239 total_loss: 1.54 loss_cls: 0.3539 loss_box_reg: 0.5101 loss_mask: 0.3701 loss_rpn_cls: 0.1209 loss_rpn_loc: 0.1587 validation_loss: 1.712 time: 0.9322 data_time: 0.0129 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:21:14 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:21:14 d2.data.common]: Serializing the dataset using: \n","[12/08 15:21:14 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:21:14 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:21:14 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:21:14 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:21:19 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0011 s/iter. Inference: 0.1483 s/iter. Eval: 0.1932 s/iter. Total: 0.3427 s/iter. ETA=0:00:03\n","[12/08 15:21:23 d2.evaluation.evaluator]: Total inference time: 0:00:05.654172 (0.353386 s / iter per device, on 1 devices)\n","[12/08 15:21:23 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.147743 s / iter per device, on 1 devices)\n","[12/08 15:21:23 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:21:23 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:21:23 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n","[12/08 15:21:23 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.199 | 47.058 | 15.929 | 0.923 | 18.126 | 41.625 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:21:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.463\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.139\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.127\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:21:23 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.696 | 46.323 | 13.926 | 0.720 | 12.680 | 40.224 |\n","[12/08 15:21:23 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: 21.1988,47.0579,15.9291,0.9229,18.1262,41.6246\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:21:23 d2.evaluation.testing]: copypaste: 19.6961,46.3233,13.9259,0.7201,12.6801,40.2245\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:21:29 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:21:29 d2.data.common]: Serializing the dataset using: \n","[12/08 15:21:29 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:21:29 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:21:29 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:21:29 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:21:34 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0042 s/iter. Inference: 0.1565 s/iter. Eval: 0.2598 s/iter. Total: 0.4204 s/iter. ETA=0:00:04\n","[12/08 15:21:38 d2.evaluation.evaluator]: Total inference time: 0:00:06.906417 (0.431651 s / iter per device, on 1 devices)\n","[12/08 15:21:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.153851 s / iter per device, on 1 devices)\n","[12/08 15:21:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:21:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:21:39 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.159\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n","[12/08 15:21:39 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.199 | 47.058 | 15.929 | 0.923 | 18.126 | 41.625 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:21:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.197\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.463\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.139\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.127\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.402\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.259\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:21:39 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.696 | 46.323 | 13.926 | 0.720 | 12.680 | 40.224 |\n","[12/08 15:21:39 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: 21.1988,47.0579,15.9291,0.9229,18.1262,41.6246\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:21:39 d2.evaluation.testing]: copypaste: 19.6961,46.3233,13.9259,0.7201,12.6801,40.2245\n","Av. AP50 = 46.323286934806504\n","[12/08 15:21:50 d2.utils.events]: eta: 1:13:12 iter: 259 total_loss: 1.481 loss_cls: 0.3441 loss_box_reg: 0.5165 loss_mask: 0.3717 loss_rpn_cls: 0.1143 loss_rpn_loc: 0.165 validation_loss: 1.708 time: 0.9322 data_time: 0.0116 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:22:05 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:22:05 d2.data.common]: Serializing the dataset using: \n","[12/08 15:22:05 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:22:05 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:22:05 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:22:05 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:22:09 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0010 s/iter. Inference: 0.1486 s/iter. Eval: 0.1951 s/iter. Total: 0.3448 s/iter. ETA=0:00:03\n","[12/08 15:22:13 d2.evaluation.evaluator]: Total inference time: 0:00:05.638462 (0.352404 s / iter per device, on 1 devices)\n","[12/08 15:22:13 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148568 s / iter per device, on 1 devices)\n","[12/08 15:22:13 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:22:13 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:22:13 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553\n","[12/08 15:22:13 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.525 | 45.587 | 15.698 | 1.050 | 17.688 | 40.507 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:22:13 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.447\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.128\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.297\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:22:13 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.084 | 44.670 | 14.660 | 0.776 | 12.758 | 39.145 |\n","[12/08 15:22:13 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: 20.5250,45.5873,15.6975,1.0502,17.6876,40.5072\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:22:13 d2.evaluation.testing]: copypaste: 19.0837,44.6704,14.6599,0.7765,12.7584,39.1454\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:22:19 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:22:19 d2.data.common]: Serializing the dataset using: \n","[12/08 15:22:19 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:22:19 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:22:19 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:22:19 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:22:24 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1494 s/iter. Eval: 0.1947 s/iter. Total: 0.3455 s/iter. ETA=0:00:03\n","[12/08 15:22:28 d2.evaluation.evaluator]: Total inference time: 0:00:06.047964 (0.377998 s / iter per device, on 1 devices)\n","[12/08 15:22:28 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149439 s / iter per device, on 1 devices)\n","[12/08 15:22:28 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:22:28 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:22:28 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.456\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553\n","[12/08 15:22:28 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.525 | 45.587 | 15.698 | 1.050 | 17.688 | 40.507 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:22:28 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.191\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.447\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.128\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.091\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.297\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.256\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[12/08 15:22:28 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 19.084 | 44.670 | 14.660 | 0.776 | 12.758 | 39.145 |\n","[12/08 15:22:28 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: 20.5250,45.5873,15.6975,1.0502,17.6876,40.5072\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:22:28 d2.evaluation.testing]: copypaste: 19.0837,44.6704,14.6599,0.7765,12.7584,39.1454\n","Av. AP50 = 44.67041892195106\n","[12/08 15:22:33 d2.utils.events]: eta: 1:12:58 iter: 279 total_loss: 1.536 loss_cls: 0.369 loss_box_reg: 0.5145 loss_mask: 0.3662 loss_rpn_cls: 0.1206 loss_rpn_loc: 0.1489 validation_loss: 1.705 time: 0.9324 data_time: 0.0116 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:22:52 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:22:52 d2.data.common]: Serializing the dataset using: \n","[12/08 15:22:52 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:22:52 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:22:52 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:22:52 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:22:57 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1489 s/iter. Eval: 0.1974 s/iter. Total: 0.3477 s/iter. ETA=0:00:03\n","[12/08 15:23:00 d2.evaluation.evaluator]: Total inference time: 0:00:05.636412 (0.352276 s / iter per device, on 1 devices)\n","[12/08 15:23:00 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148331 s / iter per device, on 1 devices)\n","[12/08 15:23:00 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:23:00 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:23:00 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:23:00 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:23:00 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:23:00 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:00 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.478\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.188\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567\n","[12/08 15:23:00 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.996 | 47.778 | 18.089 | 1.066 | 18.844 | 42.448 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:23:00 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:23:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:23:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.130\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.097\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.512\n","[12/08 15:23:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.279 | 47.065 | 15.207 | 0.702 | 13.028 | 41.186 |\n","[12/08 15:23:01 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: 21.9956,47.7782,18.0891,1.0659,18.8443,42.4476\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:01 d2.evaluation.testing]: copypaste: 20.2791,47.0654,15.2070,0.7015,13.0280,41.1860\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:23:07 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:23:07 d2.data.common]: Serializing the dataset using: \n","[12/08 15:23:07 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:23:07 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:23:07 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:23:07 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:23:11 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0010 s/iter. Inference: 0.1496 s/iter. Eval: 0.1960 s/iter. Total: 0.3466 s/iter. ETA=0:00:03\n","[12/08 15:23:15 d2.evaluation.evaluator]: Total inference time: 0:00:05.611677 (0.350730 s / iter per device, on 1 devices)\n","[12/08 15:23:15 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148317 s / iter per device, on 1 devices)\n","[12/08 15:23:15 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:23:15 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:23:15 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.478\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.188\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567\n","[12/08 15:23:15 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.996 | 47.778 | 18.089 | 1.066 | 18.844 | 42.448 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:15 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.203\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.130\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.097\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.512\n","[12/08 15:23:15 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.279 | 47.065 | 15.207 | 0.702 | 13.028 | 41.186 |\n","[12/08 15:23:15 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: 21.9956,47.7782,18.0891,1.0659,18.8443,42.4476\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:15 d2.evaluation.testing]: copypaste: 20.2791,47.0654,15.2070,0.7015,13.0280,41.1860\n","Av. AP50 = 47.06538951914288\n","[12/08 15:23:17 d2.utils.events]: eta: 1:12:35 iter: 299 total_loss: 1.512 loss_cls: 0.3376 loss_box_reg: 0.4962 loss_mask: 0.3598 loss_rpn_cls: 0.1084 loss_rpn_loc: 0.2383 validation_loss: 1.689 time: 0.9318 data_time: 0.0106 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:23:36 d2.utils.events]: eta: 1:12:23 iter: 319 total_loss: 1.48 loss_cls: 0.3511 loss_box_reg: 0.5016 loss_mask: 0.3631 loss_rpn_cls: 0.1143 loss_rpn_loc: 0.1115 validation_loss: 1.689 time: 0.9323 data_time: 0.0150 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:23:41 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:23:41 d2.data.common]: Serializing the dataset using: \n","[12/08 15:23:41 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:23:41 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:23:41 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:23:41 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:23:46 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0011 s/iter. Inference: 0.1492 s/iter. Eval: 0.1973 s/iter. Total: 0.3476 s/iter. ETA=0:00:03\n","[12/08 15:23:49 d2.evaluation.evaluator]: Total inference time: 0:00:05.658553 (0.353660 s / iter per device, on 1 devices)\n","[12/08 15:23:49 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148709 s / iter per device, on 1 devices)\n","[12/08 15:23:49 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:23:49 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:23:49 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.190\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.103\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572\n","[12/08 15:23:49 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.025 | 48.481 | 17.622 | 0.891 | 18.998 | 42.866 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:23:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.206\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.135\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515\n","[12/08 15:23:49 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.647 | 47.449 | 15.432 | 0.644 | 13.519 | 41.819 |\n","[12/08 15:23:49 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: 22.0248,48.4809,17.6218,0.8911,18.9983,42.8659\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:23:49 d2.evaluation.testing]: copypaste: 20.6466,47.4489,15.4324,0.6436,13.5191,41.8192\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:23:55 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:23:55 d2.data.common]: Serializing the dataset using: \n","[12/08 15:23:55 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:23:55 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:23:55 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:23:55 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:24:00 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1477 s/iter. Eval: 0.1961 s/iter. Total: 0.3451 s/iter. ETA=0:00:03\n","[12/08 15:24:03 d2.evaluation.evaluator]: Total inference time: 0:00:05.655286 (0.353455 s / iter per device, on 1 devices)\n","[12/08 15:24:03 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148636 s / iter per device, on 1 devices)\n","[12/08 15:24:03 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:24:03 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:24:04 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.485\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.190\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.103\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572\n","[12/08 15:24:04 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.025 | 48.481 | 17.622 | 0.891 | 18.998 | 42.866 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.206\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.135\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.269\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515\n","[12/08 15:24:04 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.647 | 47.449 | 15.432 | 0.644 | 13.519 | 41.819 |\n","[12/08 15:24:04 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: 22.0248,48.4809,17.6218,0.8911,18.9983,42.8659\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:04 d2.evaluation.testing]: copypaste: 20.6466,47.4489,15.4324,0.6436,13.5191,41.8192\n","Av. AP50 = 47.44890682180925\n","[12/08 15:24:20 d2.utils.events]: eta: 1:12:07 iter: 339 total_loss: 1.458 loss_cls: 0.3462 loss_box_reg: 0.4866 loss_mask: 0.3564 loss_rpn_cls: 0.1171 loss_rpn_loc: 0.1588 validation_loss: 1.673 time: 0.9326 data_time: 0.0138 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:24:30 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:24:30 d2.data.common]: Serializing the dataset using: \n","[12/08 15:24:30 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:24:30 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:24:30 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:24:30 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:24:34 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0023 s/iter. Inference: 0.1500 s/iter. Eval: 0.1928 s/iter. Total: 0.3451 s/iter. ETA=0:00:03\n","[12/08 15:24:38 d2.evaluation.evaluator]: Total inference time: 0:00:05.702999 (0.356437 s / iter per device, on 1 devices)\n","[12/08 15:24:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149681 s / iter per device, on 1 devices)\n","[12/08 15:24:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:24:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:24:38 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.103\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.288\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n","[12/08 15:24:38 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.912 | 47.401 | 17.181 | 1.270 | 18.873 | 42.422 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.207\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.470\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.135\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.098\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518\n","[12/08 15:24:38 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.725 | 46.991 | 15.685 | 0.849 | 13.509 | 41.927 |\n","[12/08 15:24:38 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: 21.9121,47.4013,17.1813,1.2695,18.8725,42.4217\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:38 d2.evaluation.testing]: copypaste: 20.7249,46.9912,15.6853,0.8490,13.5092,41.9266\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:24:44 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:24:44 d2.data.common]: Serializing the dataset using: \n","[12/08 15:24:44 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:24:44 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:24:44 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:24:44 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:24:49 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1482 s/iter. Eval: 0.1937 s/iter. Total: 0.3434 s/iter. ETA=0:00:03\n","[12/08 15:24:52 d2.evaluation.evaluator]: Total inference time: 0:00:05.624625 (0.351539 s / iter per device, on 1 devices)\n","[12/08 15:24:52 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148236 s / iter per device, on 1 devices)\n","[12/08 15:24:52 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:24:52 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:24:52 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:24:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:24:52 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[12/08 15:24:52 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:52 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.103\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.288\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n","[12/08 15:24:52 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.912 | 47.401 | 17.181 | 1.270 | 18.873 | 42.422 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:24:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:24:53 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:24:53 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:24:53 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.207\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.470\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.157\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.135\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.098\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.518\n","[12/08 15:24:53 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.725 | 46.991 | 15.685 | 0.849 | 13.509 | 41.927 |\n","[12/08 15:24:53 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: 21.9121,47.4013,17.1813,1.2695,18.8725,42.4217\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:24:53 d2.evaluation.testing]: copypaste: 20.7249,46.9912,15.6853,0.8490,13.5092,41.9266\n","Av. AP50 = 46.99116905117629\n","[12/08 15:25:02 d2.utils.events]: eta: 1:11:47 iter: 359 total_loss: 1.481 loss_cls: 0.351 loss_box_reg: 0.4953 loss_mask: 0.3577 loss_rpn_cls: 0.1115 loss_rpn_loc: 0.1635 validation_loss: 1.67 time: 0.9322 data_time: 0.0107 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:25:17 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:25:17 d2.data.common]: Serializing the dataset using: \n","[12/08 15:25:17 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:25:17 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:25:17 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:25:17 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:25:21 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0027 s/iter. Inference: 0.1495 s/iter. Eval: 0.2004 s/iter. Total: 0.3527 s/iter. ETA=0:00:03\n","[12/08 15:25:25 d2.evaluation.evaluator]: Total inference time: 0:00:05.703350 (0.356459 s / iter per device, on 1 devices)\n","[12/08 15:25:25 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148763 s / iter per device, on 1 devices)\n","[12/08 15:25:25 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:25:25 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:25:25 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.284\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579\n","[12/08 15:25:25 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.043 | 48.183 | 17.618 | 0.985 | 19.272 | 43.097 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:25:25 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523\n","[12/08 15:25:25 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.375 | 47.104 | 15.625 | 0.685 | 13.638 | 41.305 |\n","[12/08 15:25:25 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: 22.0426,48.1834,17.6177,0.9851,19.2717,43.0971\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:25:25 d2.evaluation.testing]: copypaste: 20.3752,47.1042,15.6247,0.6846,13.6378,41.3045\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:25:31 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:25:31 d2.data.common]: Serializing the dataset using: \n","[12/08 15:25:31 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:25:31 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:25:31 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:25:31 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:25:36 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1482 s/iter. Eval: 0.1968 s/iter. Total: 0.3465 s/iter. ETA=0:00:03\n","[12/08 15:25:39 d2.evaluation.evaluator]: Total inference time: 0:00:05.641180 (0.352574 s / iter per device, on 1 devices)\n","[12/08 15:25:39 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148107 s / iter per device, on 1 devices)\n","[12/08 15:25:39 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:25:39 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:25:39 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:25:39 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.220\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.431\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.284\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.579\n","[12/08 15:25:40 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.043 | 48.183 | 17.618 | 0.985 | 19.272 | 43.097 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:25:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.471\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.095\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523\n","[12/08 15:25:40 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 20.375 | 47.104 | 15.625 | 0.685 | 13.638 | 41.305 |\n","[12/08 15:25:40 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: 22.0426,48.1834,17.6177,0.9851,19.2717,43.0971\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:25:40 d2.evaluation.testing]: copypaste: 20.3752,47.1042,15.6247,0.6846,13.6378,41.3045\n","Av. AP50 = 47.10422254060936\n","[12/08 15:25:44 d2.utils.events]: eta: 1:11:28 iter: 379 total_loss: 1.519 loss_cls: 0.3546 loss_box_reg: 0.4908 loss_mask: 0.365 loss_rpn_cls: 0.116 loss_rpn_loc: 0.2 validation_loss: 1.668 time: 0.9322 data_time: 0.0156 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:26:04 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:26:04 d2.data.common]: Serializing the dataset using: \n","[12/08 15:26:04 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:26:04 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:26:04 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:26:04 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:26:08 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1502 s/iter. Eval: 0.1937 s/iter. Total: 0.3457 s/iter. ETA=0:00:03\n","[12/08 15:26:12 d2.evaluation.evaluator]: Total inference time: 0:00:05.642504 (0.352657 s / iter per device, on 1 devices)\n","[12/08 15:26:12 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149442 s / iter per device, on 1 devices)\n","[12/08 15:26:12 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:26:12 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:26:12 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.491\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.107\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570\n","[12/08 15:26:12 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.684 | 49.064 | 16.792 | 0.960 | 19.566 | 43.201 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:26:12 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.160\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.137\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515\n","[12/08 15:26:12 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.038 | 48.172 | 16.004 | 0.752 | 13.663 | 42.307 |\n","[12/08 15:26:12 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: 22.6843,49.0636,16.7917,0.9597,19.5662,43.2012\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:26:12 d2.evaluation.testing]: copypaste: 21.0377,48.1720,16.0045,0.7516,13.6631,42.3069\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:26:18 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:26:18 d2.data.common]: Serializing the dataset using: \n","[12/08 15:26:18 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:26:18 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:26:18 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:26:18 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:26:23 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1501 s/iter. Eval: 0.1987 s/iter. Total: 0.3506 s/iter. ETA=0:00:03\n","[12/08 15:26:26 d2.evaluation.evaluator]: Total inference time: 0:00:05.719139 (0.357446 s / iter per device, on 1 devices)\n","[12/08 15:26:26 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149190 s / iter per device, on 1 devices)\n","[12/08 15:26:26 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:26:26 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:26:26 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:26:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:26:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:26:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:26:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.491\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.107\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570\n","[12/08 15:26:26 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.684 | 49.064 | 16.792 | 0.960 | 19.566 | 43.201 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:26:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:26:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:26:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:26:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.160\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.137\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.266\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515\n","[12/08 15:26:27 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.038 | 48.172 | 16.004 | 0.752 | 13.663 | 42.307 |\n","[12/08 15:26:27 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: 22.6843,49.0636,16.7917,0.9597,19.5662,43.2012\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:26:27 d2.evaluation.testing]: copypaste: 21.0377,48.1720,16.0045,0.7516,13.6631,42.3069\n","Av. AP50 = 48.17195095349827\n","[12/08 15:26:28 d2.utils.events]: eta: 1:11:09 iter: 399 total_loss: 1.512 loss_cls: 0.3393 loss_box_reg: 0.4825 loss_mask: 0.3538 loss_rpn_cls: 0.114 loss_rpn_loc: 0.182 validation_loss: 1.662 time: 0.9322 data_time: 0.0142 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:26:47 d2.utils.events]: eta: 1:10:52 iter: 419 total_loss: 1.449 loss_cls: 0.3462 loss_box_reg: 0.4852 loss_mask: 0.3562 loss_rpn_cls: 0.1013 loss_rpn_loc: 0.1701 validation_loss: 1.662 time: 0.9322 data_time: 0.0123 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:26:53 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:26:53 d2.data.common]: Serializing the dataset using: \n","[12/08 15:26:53 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:26:53 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:26:53 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:26:53 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:26:57 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0012 s/iter. Inference: 0.1496 s/iter. Eval: 0.1952 s/iter. Total: 0.3460 s/iter. ETA=0:00:03\n","[12/08 15:27:01 d2.evaluation.evaluator]: Total inference time: 0:00:05.654887 (0.353430 s / iter per device, on 1 devices)\n","[12/08 15:27:01 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148700 s / iter per device, on 1 devices)\n","[12/08 15:27:01 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:27:01 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:27:01 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.494\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.170\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575\n","[12/08 15:27:01 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.447 | 49.375 | 16.976 | 1.146 | 19.982 | 42.948 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.11 seconds.\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525\n","[12/08 15:27:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.137 | 48.226 | 16.143 | 0.741 | 14.615 | 41.871 |\n","[12/08 15:27:01 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: 22.4466,49.3751,16.9760,1.1459,19.9815,42.9482\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:01 d2.evaluation.testing]: copypaste: 21.1367,48.2257,16.1429,0.7411,14.6150,41.8714\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:27:07 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:27:07 d2.data.common]: Serializing the dataset using: \n","[12/08 15:27:07 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:27:07 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:27:07 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:27:07 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:27:12 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1501 s/iter. Eval: 0.1934 s/iter. Total: 0.3448 s/iter. ETA=0:00:03\n","[12/08 15:27:15 d2.evaluation.evaluator]: Total inference time: 0:00:05.651530 (0.353221 s / iter per device, on 1 devices)\n","[12/08 15:27:15 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148920 s / iter per device, on 1 devices)\n","[12/08 15:27:15 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:27:15 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:27:15 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:27:15 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:27:15 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:27:15 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:15 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.494\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.170\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575\n","[12/08 15:27:16 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.447 | 49.375 | 16.976 | 1.146 | 19.982 | 42.948 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:27:16 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:27:16 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:27:16 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:16 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.161\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.096\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.271\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525\n","[12/08 15:27:16 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.137 | 48.226 | 16.143 | 0.741 | 14.615 | 41.871 |\n","[12/08 15:27:16 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: 22.4466,49.3751,16.9760,1.1459,19.9815,42.9482\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:16 d2.evaluation.testing]: copypaste: 21.1367,48.2257,16.1429,0.7411,14.6150,41.8714\n","Av. AP50 = 48.2257420271696\n","[12/08 15:27:32 d2.utils.events]: eta: 1:10:33 iter: 439 total_loss: 1.417 loss_cls: 0.3197 loss_box_reg: 0.475 loss_mask: 0.357 loss_rpn_cls: 0.1154 loss_rpn_loc: 0.1601 validation_loss: 1.656 time: 0.9322 data_time: 0.0127 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:27:42 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:27:42 d2.data.common]: Serializing the dataset using: \n","[12/08 15:27:42 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:27:42 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:27:43 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:27:43 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:27:47 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1507 s/iter. Eval: 0.1927 s/iter. Total: 0.3450 s/iter. ETA=0:00:03\n","[12/08 15:27:51 d2.evaluation.evaluator]: Total inference time: 0:00:05.622245 (0.351390 s / iter per device, on 1 devices)\n","[12/08 15:27:51 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149357 s / iter per device, on 1 devices)\n","[12/08 15:27:51 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:27:51 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:27:51 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.293\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582\n","[12/08 15:27:51 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.835 | 49.512 | 19.178 | 1.291 | 19.253 | 43.789 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:27:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.100\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519\n","[12/08 15:27:51 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.249 | 48.400 | 16.320 | 0.699 | 13.853 | 42.559 |\n","[12/08 15:27:51 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: 22.8351,49.5116,19.1779,1.2905,19.2530,43.7888\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:27:51 d2.evaluation.testing]: copypaste: 21.2488,48.4004,16.3199,0.6989,13.8532,42.5591\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:27:57 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:27:57 d2.data.common]: Serializing the dataset using: \n","[12/08 15:27:57 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:27:57 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:27:57 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:27:57 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:28:02 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0011 s/iter. Inference: 0.1486 s/iter. Eval: 0.1965 s/iter. Total: 0.3462 s/iter. ETA=0:00:03\n","[12/08 15:28:05 d2.evaluation.evaluator]: Total inference time: 0:00:05.658984 (0.353686 s / iter per device, on 1 devices)\n","[12/08 15:28:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.147899 s / iter per device, on 1 devices)\n","[12/08 15:28:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:28:05 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:28:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:28:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.228\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.192\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.193\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.293\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582\n","[12/08 15:28:06 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.835 | 49.512 | 19.178 | 1.291 | 19.253 | 43.789 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.484\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.100\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.275\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519\n","[12/08 15:28:06 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.249 | 48.400 | 16.320 | 0.699 | 13.853 | 42.559 |\n","[12/08 15:28:06 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: 22.8351,49.5116,19.1779,1.2905,19.2530,43.7888\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:06 d2.evaluation.testing]: copypaste: 21.2488,48.4004,16.3199,0.6989,13.8532,42.5591\n","Av. AP50 = 48.40040009483603\n","[12/08 15:28:17 d2.utils.events]: eta: 1:10:16 iter: 459 total_loss: 1.397 loss_cls: 0.3261 loss_box_reg: 0.467 loss_mask: 0.3489 loss_rpn_cls: 0.1028 loss_rpn_loc: 0.1075 validation_loss: 1.645 time: 0.9333 data_time: 0.0151 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:28:32 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:28:32 d2.data.common]: Serializing the dataset using: \n","[12/08 15:28:32 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:28:32 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:28:32 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:28:32 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:28:38 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0024 s/iter. Inference: 0.1616 s/iter. Eval: 0.2421 s/iter. Total: 0.4061 s/iter. ETA=0:00:04\n","[12/08 15:28:41 d2.evaluation.evaluator]: Total inference time: 0:00:06.022532 (0.376408 s / iter per device, on 1 devices)\n","[12/08 15:28:41 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.152835 s / iter per device, on 1 devices)\n","[12/08 15:28:41 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:28:41 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:28:41 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:28:41 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:28:41 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:28:41 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:41 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.503\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.446\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.589\n","[12/08 15:28:41 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.495 | 50.283 | 19.385 | 1.143 | 20.379 | 44.600 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:28:41 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:28:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:28:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.148\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534\n","[12/08 15:28:42 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.894 | 48.888 | 17.330 | 0.616 | 14.779 | 43.510 |\n","[12/08 15:28:42 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: 23.4951,50.2833,19.3850,1.1435,20.3788,44.6003\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:42 d2.evaluation.testing]: copypaste: 21.8935,48.8878,17.3297,0.6165,14.7787,43.5097\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:28:48 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:28:48 d2.data.common]: Serializing the dataset using: \n","[12/08 15:28:48 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:28:48 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:28:48 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:28:48 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:28:54 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0063 s/iter. Inference: 0.1691 s/iter. Eval: 0.3300 s/iter. Total: 0.5055 s/iter. ETA=0:00:05\n","[12/08 15:28:58 d2.evaluation.evaluator]: Total inference time: 0:00:07.745388 (0.484087 s / iter per device, on 1 devices)\n","[12/08 15:28:58 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.161122 s / iter per device, on 1 devices)\n","[12/08 15:28:58 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:28:58 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:28:58 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:28:58 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:28:58 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[12/08 15:28:58 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:58 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.503\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.446\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.589\n","[12/08 15:28:59 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.495 | 50.283 | 19.385 | 1.143 | 20.379 | 44.600 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:28:59 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:28:59 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.10 seconds.\n","[12/08 15:28:59 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:28:59 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.148\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534\n","[12/08 15:28:59 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.894 | 48.888 | 17.330 | 0.616 | 14.779 | 43.510 |\n","[12/08 15:28:59 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: 23.4951,50.2833,19.3850,1.1435,20.3788,44.6003\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:28:59 d2.evaluation.testing]: copypaste: 21.8935,48.8878,17.3297,0.6165,14.7787,43.5097\n","Av. AP50 = 48.88779751431735\n","[12/08 15:29:05 d2.utils.events]: eta: 1:09:59 iter: 479 total_loss: 1.462 loss_cls: 0.3373 loss_box_reg: 0.4711 loss_mask: 0.3516 loss_rpn_cls: 0.1032 loss_rpn_loc: 0.1684 validation_loss: 1.635 time: 0.9333 data_time: 0.0120 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:29:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:29:25 d2.data.common]: Serializing the dataset using: \n","[12/08 15:29:25 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:29:26 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:29:26 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:29:26 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:29:31 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0010 s/iter. Inference: 0.1477 s/iter. Eval: 0.1965 s/iter. Total: 0.3452 s/iter. ETA=0:00:03\n","[12/08 15:29:35 d2.evaluation.evaluator]: Total inference time: 0:00:05.718894 (0.357431 s / iter per device, on 1 devices)\n","[12/08 15:29:35 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148650 s / iter per device, on 1 devices)\n","[12/08 15:29:35 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:29:35 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:29:35 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.500\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584\n","[12/08 15:29:35 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.508 | 50.035 | 19.629 | 1.175 | 19.938 | 44.511 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:29:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.488\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.526\n","[12/08 15:29:35 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.876 | 48.814 | 17.338 | 0.733 | 14.472 | 43.308 |\n","[12/08 15:29:35 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: 23.5081,50.0346,19.6294,1.1749,19.9383,44.5108\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:29:35 d2.evaluation.testing]: copypaste: 21.8764,48.8142,17.3385,0.7329,14.4724,43.3078\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:29:41 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:29:41 d2.data.common]: Serializing the dataset using: \n","[12/08 15:29:41 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:29:41 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:29:41 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:29:41 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:29:46 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1490 s/iter. Eval: 0.1971 s/iter. Total: 0.3476 s/iter. ETA=0:00:03\n","[12/08 15:29:49 d2.evaluation.evaluator]: Total inference time: 0:00:05.701133 (0.356321 s / iter per device, on 1 devices)\n","[12/08 15:29:49 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148052 s / iter per device, on 1 devices)\n","[12/08 15:29:50 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:29:50 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:29:50 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.235\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.500\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584\n","[12/08 15:29:50 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.508 | 50.035 | 19.629 | 1.175 | 19.938 | 44.511 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:29:50 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.488\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.526\n","[12/08 15:29:50 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.876 | 48.814 | 17.338 | 0.733 | 14.472 | 43.308 |\n","[12/08 15:29:50 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: 23.5081,50.0346,19.6294,1.1749,19.9383,44.5108\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:29:50 d2.evaluation.testing]: copypaste: 21.8764,48.8142,17.3385,0.7329,14.4724,43.3078\n","Av. AP50 = 48.8141919870721\n","[12/08 15:29:50 d2.utils.events]: eta: 1:09:39 iter: 499 total_loss: 1.366 loss_cls: 0.3139 loss_box_reg: 0.4576 loss_mask: 0.3463 loss_rpn_cls: 0.09127 loss_rpn_loc: 0.1481 validation_loss: 1.634 time: 0.9331 data_time: 0.0102 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:30:08 d2.utils.events]: eta: 1:09:20 iter: 519 total_loss: 1.508 loss_cls: 0.32 loss_box_reg: 0.4732 loss_mask: 0.3566 loss_rpn_cls: 0.111 loss_rpn_loc: 0.2021 validation_loss: 1.634 time: 0.9328 data_time: 0.0117 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:30:14 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:30:14 d2.data.common]: Serializing the dataset using: \n","[12/08 15:30:14 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:30:14 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:30:14 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:30:14 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:30:19 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0054 s/iter. Inference: 0.1666 s/iter. Eval: 0.2960 s/iter. Total: 0.4680 s/iter. ETA=0:00:04\n","[12/08 15:30:23 d2.evaluation.evaluator]: Total inference time: 0:00:06.652868 (0.415804 s / iter per device, on 1 devices)\n","[12/08 15:30:23 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.157405 s / iter per device, on 1 devices)\n","[12/08 15:30:23 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:30:23 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:30:23 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584\n","[12/08 15:30:23 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.165 | 49.496 | 18.919 | 1.135 | 19.404 | 44.513 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:30:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533\n","[12/08 15:30:23 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.855 | 48.645 | 17.273 | 0.655 | 14.520 | 43.566 |\n","[12/08 15:30:23 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: 23.1650,49.4955,18.9188,1.1350,19.4035,44.5126\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:30:23 d2.evaluation.testing]: copypaste: 21.8553,48.6453,17.2726,0.6554,14.5201,43.5661\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:30:29 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:30:29 d2.data.common]: Serializing the dataset using: \n","[12/08 15:30:29 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:30:29 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:30:29 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:30:29 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:30:34 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1497 s/iter. Eval: 0.1939 s/iter. Total: 0.3450 s/iter. ETA=0:00:03\n","[12/08 15:30:38 d2.evaluation.evaluator]: Total inference time: 0:00:05.866582 (0.366661 s / iter per device, on 1 devices)\n","[12/08 15:30:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148782 s / iter per device, on 1 devices)\n","[12/08 15:30:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:30:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:30:38 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.289\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584\n","[12/08 15:30:38 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.165 | 49.496 | 18.919 | 1.135 | 19.404 | 44.513 |\n","Loading and preparing results...\n","DONE (t=0.03s)\n","creating index...\n","index created!\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:30:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.486\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.101\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533\n","[12/08 15:30:38 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 21.855 | 48.645 | 17.273 | 0.655 | 14.520 | 43.566 |\n","[12/08 15:30:38 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: 23.1650,49.4955,18.9188,1.1350,19.4035,44.5126\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:30:38 d2.evaluation.testing]: copypaste: 21.8553,48.6453,17.2726,0.6554,14.5201,43.5661\n","Av. AP50 = 48.64525037960297\n","[12/08 15:30:52 d2.utils.events]: eta: 1:09:01 iter: 539 total_loss: 1.345 loss_cls: 0.3222 loss_box_reg: 0.4507 loss_mask: 0.3451 loss_rpn_cls: 0.1021 loss_rpn_loc: 0.1395 validation_loss: 1.628 time: 0.9325 data_time: 0.0118 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:31:02 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:31:02 d2.data.common]: Serializing the dataset using: \n","[12/08 15:31:02 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:31:02 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:31:02 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:31:02 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:31:06 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1501 s/iter. Eval: 0.1954 s/iter. Total: 0.3472 s/iter. ETA=0:00:03\n","[12/08 15:31:11 d2.evaluation.evaluator]: Total inference time: 0:00:06.324888 (0.395306 s / iter per device, on 1 devices)\n","[12/08 15:31:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149714 s / iter per device, on 1 devices)\n","[12/08 15:31:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:31:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:31:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610\n","[12/08 15:31:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.090 | 50.511 | 20.435 | 1.002 | 20.917 | 46.171 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:31:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.496\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.100\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.543\n","[12/08 15:31:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.365 | 49.566 | 17.688 | 0.622 | 15.528 | 43.733 |\n","[12/08 15:31:11 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: 24.0898,50.5106,20.4350,1.0025,20.9169,46.1714\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:31:11 d2.evaluation.testing]: copypaste: 22.3654,49.5659,17.6878,0.6224,15.5284,43.7326\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:31:18 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:31:18 d2.data.common]: Serializing the dataset using: \n","[12/08 15:31:18 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:31:18 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:31:18 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:31:18 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:31:22 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0017 s/iter. Inference: 0.1487 s/iter. Eval: 0.1964 s/iter. Total: 0.3468 s/iter. ETA=0:00:03\n","[12/08 15:31:26 d2.evaluation.evaluator]: Total inference time: 0:00:05.680478 (0.355030 s / iter per device, on 1 devices)\n","[12/08 15:31:26 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.148670 s / iter per device, on 1 devices)\n","[12/08 15:31:26 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:31:26 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:31:26 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.204\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.106\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610\n","[12/08 15:31:26 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.090 | 50.511 | 20.435 | 1.002 | 20.917 | 46.171 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:31:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.496\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.177\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.100\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.543\n","[12/08 15:31:26 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.365 | 49.566 | 17.688 | 0.622 | 15.528 | 43.733 |\n","[12/08 15:31:26 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: 24.0898,50.5106,20.4350,1.0025,20.9169,46.1714\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:31:26 d2.evaluation.testing]: copypaste: 22.3654,49.5659,17.6878,0.6224,15.5284,43.7326\n","Av. AP50 = 49.565917117866\n","[12/08 15:31:37 d2.utils.events]: eta: 1:08:41 iter: 559 total_loss: 1.388 loss_cls: 0.3033 loss_box_reg: 0.4488 loss_mask: 0.3494 loss_rpn_cls: 0.1029 loss_rpn_loc: 0.1886 validation_loss: 1.619 time: 0.9322 data_time: 0.0109 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:31:52 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:31:52 d2.data.common]: Serializing the dataset using: \n","[12/08 15:31:52 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:31:52 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:31:52 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:31:52 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:31:57 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0089 s/iter. Inference: 0.1821 s/iter. Eval: 0.3248 s/iter. Total: 0.5159 s/iter. ETA=0:00:05\n","[12/08 15:32:03 d2.evaluation.evaluator]: Inference done 21/21. Dataloading: 0.0058 s/iter. Inference: 0.1984 s/iter. Eval: 0.3222 s/iter. Total: 0.5267 s/iter. ETA=0:00:00\n","[12/08 15:32:03 d2.evaluation.evaluator]: Total inference time: 0:00:08.716691 (0.544793 s / iter per device, on 1 devices)\n","[12/08 15:32:03 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:03 (0.198411 s / iter per device, on 1 devices)\n","[12/08 15:32:03 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:32:03 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:32:03 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:32:03 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:32:03 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[12/08 15:32:03 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:32:03 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.503\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.349\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591\n","[12/08 15:32:03 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.002 | 50.329 | 20.541 | 1.158 | 21.373 | 45.485 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[12/08 15:32:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:32:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.27 seconds.\n","[12/08 15:32:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:32:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.223\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.497\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.282\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530\n","[12/08 15:32:04 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.325 | 49.714 | 17.227 | 0.617 | 15.249 | 43.579 |\n","[12/08 15:32:04 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: 24.0022,50.3292,20.5410,1.1581,21.3735,45.4847\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:32:04 d2.evaluation.testing]: copypaste: 22.3245,49.7140,17.2272,0.6169,15.2488,43.5788\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:32:11 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:32:11 d2.data.common]: Serializing the dataset using: \n","[12/08 15:32:11 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:32:11 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:32:11 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:32:11 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:32:15 d2.evaluation.evaluator]: Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1498 s/iter. Eval: 0.1957 s/iter. Total: 0.3472 s/iter. ETA=0:00:03\n","[12/08 15:32:19 d2.evaluation.evaluator]: Total inference time: 0:00:05.669377 (0.354336 s / iter per device, on 1 devices)\n","[12/08 15:32:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.149093 s / iter per device, on 1 devices)\n","[12/08 15:32:19 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[12/08 15:32:19 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[12/08 15:32:19 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.503\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.349\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591\n","[12/08 15:32:19 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.002 | 50.329 | 20.541 | 1.158 | 21.373 | 45.485 |\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.09 seconds.\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[12/08 15:32:19 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.223\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.497\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.282\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530\n","[12/08 15:32:19 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 22.325 | 49.714 | 17.227 | 0.617 | 15.249 | 43.579 |\n","[12/08 15:32:19 d2.engine.defaults]: Evaluation results for Paracou2016_val in csv format:\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: Task: bbox\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: 24.0022,50.3292,20.5410,1.1581,21.3735,45.4847\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: Task: segm\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[12/08 15:32:19 d2.evaluation.testing]: copypaste: 22.3245,49.7140,17.2272,0.6169,15.2488,43.5788\n","Av. AP50 = 49.71400578275023\n","[12/08 15:32:26 d2.utils.events]: eta: 1:08:22 iter: 579 total_loss: 1.394 loss_cls: 0.3089 loss_box_reg: 0.4661 loss_mask: 0.3539 loss_rpn_cls: 0.1014 loss_rpn_loc: 0.1429 validation_loss: 1.612 time: 0.9324 data_time: 0.0138 lr: 0.0003389 max_mem: 3490M\n","[12/08 15:32:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","[12/08 15:32:45 d2.data.common]: Serializing the dataset using: \n","[12/08 15:32:45 d2.data.common]: Serializing 21 elements to byte tensors and concatenating them all ...\n","[12/08 15:32:45 d2.data.common]: Serialized dataset takes 1.03 MiB\n","WARNING [12/08 15:32:45 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[12/08 15:32:45 d2.evaluation.evaluator]: Start inference on 21 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," warnings.warn(_create_warning_msg(\n"]},{"output_type":"stream","name":"stdout","text":["[12/08 15:32:46 d2.engine.hooks]: Overall training speed: 597 iterations in 0:09:17 (0.9341 s / it)\n","[12/08 15:32:46 d2.engine.hooks]: Total training time: 0:19:18 (0:10:00 on hooks)\n","[12/08 15:33:02 d2.checkpoint.c2_model_loading]: Following weights matched with model:\n","| Names in Model | Names in Checkpoint | Shapes |\n","|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|\n","| backbone.bottom_up.res2.0.conv1.* | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,1,1) |\n","| backbone.bottom_up.res2.0.conv2.* | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.0.conv3.* | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.0.shortcut.* | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.1.conv1.* | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |\n","| backbone.bottom_up.res2.1.conv2.* | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.1.conv3.* | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res2.2.conv1.* | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |\n","| backbone.bottom_up.res2.2.conv2.* | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |\n","| backbone.bottom_up.res2.2.conv3.* | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |\n","| backbone.bottom_up.res3.0.conv1.* | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,256,1,1) |\n","| backbone.bottom_up.res3.0.conv2.* | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.0.conv3.* | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.0.shortcut.* | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1) |\n","| backbone.bottom_up.res3.1.conv1.* | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.1.conv2.* | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.1.conv3.* | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.2.conv1.* | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.2.conv2.* | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.2.conv3.* | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res3.3.conv1.* | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |\n","| backbone.bottom_up.res3.3.conv2.* | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |\n","| backbone.bottom_up.res3.3.conv3.* | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |\n","| backbone.bottom_up.res4.0.conv1.* | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,512,1,1) |\n","| backbone.bottom_up.res4.0.conv2.* | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.0.conv3.* | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.0.shortcut.* | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1) |\n","| backbone.bottom_up.res4.1.conv1.* | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.1.conv2.* | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.1.conv3.* | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.10.conv1.* | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.10.conv2.* | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.10.conv3.* | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.11.conv1.* | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.11.conv2.* | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.11.conv3.* | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.12.conv1.* | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.12.conv2.* | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.12.conv3.* | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.13.conv1.* | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.13.conv2.* | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.13.conv3.* | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.14.conv1.* | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.14.conv2.* | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.14.conv3.* | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.15.conv1.* | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.15.conv2.* | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.15.conv3.* | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.16.conv1.* | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.16.conv2.* | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.16.conv3.* | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.17.conv1.* | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.17.conv2.* | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.17.conv3.* | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.18.conv1.* | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.18.conv2.* | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.18.conv3.* | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.19.conv1.* | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.19.conv2.* | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.19.conv3.* | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.2.conv1.* | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.2.conv2.* | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.2.conv3.* | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.20.conv1.* | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.20.conv2.* | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.20.conv3.* | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.21.conv1.* | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.21.conv2.* | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.21.conv3.* | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.22.conv1.* | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.22.conv2.* | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.22.conv3.* | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.3.conv1.* | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.3.conv2.* | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.3.conv3.* | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.4.conv1.* | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.4.conv2.* | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.4.conv3.* | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.5.conv1.* | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.5.conv2.* | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.5.conv3.* | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.6.conv1.* | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.6.conv2.* | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.6.conv3.* | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.7.conv1.* | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.7.conv2.* | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.7.conv3.* | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.8.conv1.* | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.8.conv2.* | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.8.conv3.* | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res4.9.conv1.* | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |\n","| backbone.bottom_up.res4.9.conv2.* | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |\n","| backbone.bottom_up.res4.9.conv3.* | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |\n","| backbone.bottom_up.res5.0.conv1.* | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,1024,1,1) |\n","| backbone.bottom_up.res5.0.conv2.* | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.0.conv3.* | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.res5.0.shortcut.* | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n","| backbone.bottom_up.res5.1.conv1.* | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |\n","| backbone.bottom_up.res5.1.conv2.* | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.1.conv3.* | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.res5.2.conv1.* | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |\n","| backbone.bottom_up.res5.2.conv2.* | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |\n","| backbone.bottom_up.res5.2.conv3.* | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |\n","| backbone.bottom_up.stem.conv1.* | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,3,7,7) |\n","| backbone.fpn_lateral2.* | backbone.fpn_lateral2.{bias,weight} | (256,) (256,256,1,1) |\n","| backbone.fpn_lateral3.* | backbone.fpn_lateral3.{bias,weight} | (256,) (256,512,1,1) |\n","| backbone.fpn_lateral4.* | backbone.fpn_lateral4.{bias,weight} | (256,) (256,1024,1,1) |\n","| backbone.fpn_lateral5.* | backbone.fpn_lateral5.{bias,weight} | (256,) (256,2048,1,1) |\n","| backbone.fpn_output2.* | backbone.fpn_output2.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output3.* | backbone.fpn_output3.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output4.* | backbone.fpn_output4.{bias,weight} | (256,) (256,256,3,3) |\n","| backbone.fpn_output5.* | backbone.fpn_output5.{bias,weight} | (256,) (256,256,3,3) |\n","| proposal_generator.rpn_head.anchor_deltas.* | proposal_generator.rpn_head.anchor_deltas.{bias,weight} | (12,) (12,256,1,1) |\n","| proposal_generator.rpn_head.conv.* | proposal_generator.rpn_head.conv.{bias,weight} | (256,) (256,256,3,3) |\n","| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight} | (3,) (3,256,1,1) |\n","| roi_heads.box_head.fc1.* | roi_heads.box_head.fc1.{bias,weight} | (1024,) (1024,12544) |\n","| roi_heads.box_head.fc2.* | roi_heads.box_head.fc2.{bias,weight} | (1024,) (1024,1024) |\n","| roi_heads.box_predictor.bbox_pred.* | roi_heads.box_predictor.bbox_pred.{bias,weight} | (4,) (4,1024) |\n","| roi_heads.box_predictor.cls_score.* | roi_heads.box_predictor.cls_score.{bias,weight} | (2,) (2,1024) |\n","| roi_heads.mask_head.deconv.* | roi_heads.mask_head.deconv.{bias,weight} | (256,) (256,256,2,2) |\n","| roi_heads.mask_head.mask_fcn1.* | roi_heads.mask_head.mask_fcn1.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn2.* | roi_heads.mask_head.mask_fcn2.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn3.* | roi_heads.mask_head.mask_fcn3.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.mask_fcn4.* | roi_heads.mask_head.mask_fcn4.{bias,weight} | (256,) (256,256,3,3) |\n","| roi_heads.mask_head.predictor.* | roi_heads.mask_head.predictor.{bias,weight} | (1,) (1,256,1,1) |\n","[12/08 15:33:02 d2.engine.hooks]: Loading scheduler from state_dict ...\n","[12/08 15:33:02 d2.utils.events]: eta: 1:08:05 iter: 599 total_loss: 1.378 loss_cls: 0.3177 loss_box_reg: 0.4498 loss_mask: 0.3426 loss_rpn_cls: 0.09859 loss_rpn_loc: 0.1371 validation_loss: 1.612 time: 0.9325 data_time: 0.0126 lr: 0.0003389 max_mem: 3490M\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMyTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresume_or_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m 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model, evaluators)\u001b[0m\n\u001b[1;32m 615\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 617\u001b[0;31m \u001b[0mresults_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_on_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 618\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_i\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 619\u001b[0m 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_global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1191\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1192\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/detectron2/layers/wrappers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 111\u001b[0m ), \"SyncBatchNorm does not support empty inputs!\"\n\u001b[1;32m 112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m x = F.conv2d(\n\u001b[0m\u001b[1;32m 114\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdilation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m )\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["trained_model = \"/content/drive/Shareddrives/detectree2/models/221026_Paracou2016DanumSepilokESepilokW/model_22.pth\"\n","\n","\n","#names = [\"Paracou2016\", \"Danum\", \"SepilokE\", \"SepilokW\"]\n","names = [\"Paracou2016\"]\n","for name in names:\n"," trains = (name + \"_train\",)\n"," tests = (name + \"_val\",)\n"," out_dir = \"/content/drive/Shareddrives/detectree2/models/\" + today + \"combined_\" + name\n"," # Add in trained model as required\n"," cfg = setup_cfg(base_model, trains, tests, workers = 4, eval_period=25,\n"," update_model=trained_model,\n"," max_iter=5000, out_dir=out_dir, resize = \"random\") # update_model arg can be used to load in trained model\n"," #cfg.INPUT.MIN_SIZE_TRAIN = 1000\n"," trainer = MyTrainer(cfg, patience = 5)\n"," trainer.resume_or_load(resume=False)\n"," trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"f3EJqp8ZuhFw"},"source":["For that combines all sites, include them all in the `trains` and `tests` tuples"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","output_embedded_package_id":"1_lZ21nkm1mJCJ5HxAvTj6kv9_h0niOT_"},"id":"irksEkyFuV4A","outputId":"6f23068d-66ef-40e7-9061-f1997d51a4a9"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["names = [\"Paracou2016\",\n"," \"Danum\",\n"," \"SepilokE\",\n"," \"SepilokW\",\n"," \"Paracou2019\",\n"," #\"ParacouUAV\",\n"," #\"BCI_50ha\"\n"," ]\n","\n","trained_model = \"/content/drive/Shareddrives/detectree2/models/230103_resize_full/model_4.pth\"\n","\n","trains = (names[0] + \"_train\", names[1] + \"_train\",names[2] + \"_train\", names[3] + \"_train\", names[4] + \"_train\", names[5] + \"_train\", names[6] + \"_train\",)\n","tests = (names[0] + \"_val\", names[1] + \"_val\", names[2] + \"_val\", names[3] + \"_val\",names[4] + \"_val\", names[5] + \"_val\", names[6] + \"_val\",)\n","out_dir = \"/content/drive/Shareddrives/detectree2/models/\" + today + \"_resize_full\"\n","cfg = setup_cfg(base_model, trains, tests, workers = 4, eval_period=50,\n"," update_model = trained_model,\n"," max_iter=7000, out_dir=out_dir, resize = \"random\") # update_model arg can be used to load in trained model\n","#cfg.INPUT.MIN_SIZE_TRAIN = 1000\n","trainer = MyTrainer(cfg, patience = 3)\n","\n","trainer.resume_or_load(resume=False)\n","trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"Yg6IrLI-lLuZ"},"source":["## Plot the loss"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"elapsed":449,"status":"ok","timestamp":1672838672468,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"6KQxaS3o0Rv6","outputId":"61db585b-3752-49fd-cdf9-a264f906470f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["### Plot training and validation loss on the same plot to check how the training has gone\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","out_dir = \"/content/drive/Shareddrives/detectree2/models/230103_resize_full\"\n","experiment_folder = out_dir\n","\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'validation_loss' in x],\n"," [x['validation_loss'] for x in experiment_metrics if 'validation_loss' in x], label='Total Validation Loss', color='red')\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'total_loss' in x],\n"," [x['total_loss'] for x in experiment_metrics if 'total_loss' in x], label='Total Training Loss')\n","\n","plt.legend(loc='upper right')\n","plt.title('Comparison of the training and validation loss of Mask R-CNN')\n","plt.ylabel('Total Loss')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"uobNPgiyZBfZ"},"source":["### How did the AP50 change through time?\n","\n","Early stopping means that if the AP50 stops increasing after the ```patience``` interval, training will terminate and the best model will be saved."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"elapsed":768,"status":"ok","timestamp":1672838686434,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":0},"id":"vsgvjxQVXwsH","outputId":"77c01fc8-aa7c-4d2f-8853-cd34eb22e96d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["### Plot ap50\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","experiment_folder = out_dir\n","\n","names = [\"Paracou2016\", \"Danum\", \"SepilokE\", \"SepilokW\", \"Paracou2019\", \"ParacouUAV\", \"BCI_50ha\"]\n","name = names[6]\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if name + '_val/segm/AP50' in x],\n"," [x[name + '_val/segm/AP50'] for x in experiment_metrics if name + '_val/segm/AP50' in x], label='Site Validation AP50', color='red')\n","\n","plt.legend(loc='upper right')\n","plt.title('Comparison of the training and validation loss of Mask R-CNN')\n","plt.ylabel('AP50')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"elapsed":718,"status":"ok","timestamp":1662401451724,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"HmKv9SqXz9ES","outputId":"27fb2469-801b-4f9c-9c5b-d673cd4521e3"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["### Plot ap50\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","experiment_folder = out_dir\n","name = names[0]\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'segm/AP50' in x],\n"," [x['segm/AP50'] for x in experiment_metrics if 'segm/AP50' in x], label='Total Validation Loss', color='red')\n","\n","plt.legend(loc='upper right')\n","plt.title('Comparison of the training and validation loss of Mask R-CNN')\n","plt.ylabel('AP50')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"UERn9vzjlRs3"},"source":["## Make predictions on the validation set and visualise"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"executionInfo":{"elapsed":1084,"status":"error","timestamp":1660869568776,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"kgjxuCreg1xp","outputId":"d9d86d37-79a6-4924-cde9-bcfe97bc2cff"},"outputs":[{"ename":"AssertionError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMODEL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mROI_HEADS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSCORE_THRESH_TEST\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#cfg.DATASETS.TEST = (\"trees_test\",)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mpredictor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDefaultPredictor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/detectron2/engine/defaults.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cfg)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0mcheckpointer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDetectionCheckpointer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m 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found!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAssertionError\u001b[0m: Checkpoint /content/drive/Shareddrives/detectree2/220818_Danum/model_3.pth not found!"]}],"source":["# Setup to predict on new images, here setting up for the trees_test dataset, but can also use this setup\n","# for predicting on individual images as seen 2 cells down\n","import os\n","from detectron2.utils.visualizer import ColorMode\n","from detectron2.engine import DefaultPredictor\n","\n","# Weights automatically saved to OUTPUT_DIR + model_final.pth following training\n","cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_3.pth\")\n","cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.20\n","#cfg.DATASETS.TEST = (\"trees_test\",)\n","predictor = DefaultPredictor(cfg)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1I1V2hledvcjIkbP6ZC2XvJ3oyU5pvTSG"},"executionInfo":{"elapsed":23335,"status":"ok","timestamp":1660679922188,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"zlYlFAsag4hL","outputId":"c13baada-db71-4a8c-94b7-2d9718068299"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["from detectree2.models.train import combine_dicts\n","import cv2\n","from detectron2.utils.visualizer import Visualizer\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","from PIL import Image\n","\n","#name = \"Paracou\"\n","train_location = \"/content/drive/Shareddrives/detectree2/data/\"+ name + \"/tiles/train/\"\n","MetadataCatalog.get(name + \"train\").set(thing_classes=['tree'])\n","trees_metadata = MetadataCatalog.get(name + \"train\")\n","dataset_dicts = combine_dicts(train_location, val_fold, mode='val')\n","for d in dataset_dicts:\n"," img = cv2.imread(d[\"file_name\"])\n"," outputs = predictor(img)\n"," v = Visualizer(img[:, :, ::-1], metadata=trees_metadata, scale=0.7) # remove the colors of unsegmented pixels\n"," v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n"," image = cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB)\n"," display(Image.fromarray(image))"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/notebooks/241106_colab_JB/training_ms_JB.ipynb b/notebooks/241106_colab_JB/training_ms_JB.ipynb new file mode 100644 index 00000000..d6710a98 --- /dev/null +++ b/notebooks/241106_colab_JB/training_ms_JB.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"JmM04nS5rSrO"},"source":["Install package and load drive\n"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":311512,"status":"ok","timestamp":1725378188534,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"iZqHRLindp70","outputId":"dbadba3b-b0f8-4657-cff7-741ad3e02919"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","Collecting git+https://github.com/PatBall1/detectree2.git@jb/july24\n"," Cloning https://github.com/PatBall1/detectree2.git (to revision jb/july24) to /tmp/pip-req-build-olkwfht_\n"," Running command git clone --filter=blob:none --quiet https://github.com/PatBall1/detectree2.git /tmp/pip-req-build-olkwfht_\n"," Running command git checkout -b jb/july24 --track origin/jb/july24\n"," Switched to a new branch 'jb/july24'\n"," Branch 'jb/july24' set up to track remote branch 'jb/july24' from 'origin'.\n"," Resolved https://github.com/PatBall1/detectree2.git to commit 5682e7581cc651202dac77f81280b70ae82523a4\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting detectron2@ git+https://github.com/facebookresearch/detectron2.git (from detectree2==1.0.8)\n"," Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-install-qw0uicnf/detectron2_c2cdb38123ee4d4e92b1912549331bea\n"," Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-install-qw0uicnf/detectron2_c2cdb38123ee4d4e92b1912549331bea\n"," Resolved https://github.com/facebookresearch/detectron2.git to commit 5b72c27ae39f99db75d43f18fd1312e1ea934e60\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (6.0.2)\n","Requirement already satisfied: GDAL>=1.11 in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (3.6.4)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.26.4)\n","Collecting rtree (from detectree2==1.0.8)\n"," Downloading Rtree-1.3.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.1 kB)\n","Collecting proj (from detectree2==1.0.8)\n"," Downloading proj-0.2.0-py2.py3-none-any.whl.metadata (3.3 kB)\n","Collecting geos (from detectree2==1.0.8)\n"," Downloading geos-0.2.3-py3-none-any.whl.metadata (480 bytes)\n","Collecting pypng (from detectree2==1.0.8)\n"," Downloading pypng-0.20220715.0-py3-none-any.whl.metadata (13 kB)\n","Collecting pygeos (from detectree2==1.0.8)\n"," Downloading pygeos-0.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n","Requirement already satisfied: shapely in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (2.0.6)\n","Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (0.14.4)\n","Collecting rasterio==1.3a3 (from detectree2==1.0.8)\n"," Downloading rasterio-1.3a3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n","Requirement already satisfied: fiona in /usr/local/lib/python3.10/dist-packages (from detectree2==1.0.8) (1.9.6)\n","Collecting pycrs (from detectree2==1.0.8)\n"," Downloading PyCRS-1.0.2.tar.gz (36 kB)\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting descartes (from detectree2==1.0.8)\n"," Downloading descartes-1.1.0-py3-none-any.whl.metadata (2.4 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yacs-0.1.8\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["pydevd_plugins"]},"id":"b5f5452f135e41848087cfba54ef9c95"}},"metadata":{}}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","!pip install git+https://github.com/PatBall1/detectree2.git@jb/july24"]},{"cell_type":"markdown","metadata":{"id":"kiFolF2ywysk"},"source":["Registering the training (and validation) data. It is possible to register all the locations below.\n","\n","\n","Can duplicate to register many train/val folders (e.g. if you have multiple sites to train across)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UYd6eqoNvz-V"},"outputs":[],"source":["remove_registered_data(\"ParacouMS\")\n"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":4378,"status":"ok","timestamp":1725378860542,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"lRw28MFLrtt2"},"outputs":[],"source":["from detectree2.models.train import register_train_data, remove_registered_data\n","val_fold = 5\n","appends = \"15_15_0.7\"\n","site_path = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou\"\n","train_location = site_path + \"/tilesMS_\" + appends + \"/train/\"\n","register_train_data(train_location, \"ParacouMS\", val_fold)"]},{"cell_type":"markdown","metadata":{"id":"eatbh46KxH1T"},"source":["## Visualise training data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1Zo1Kp3P7MzP4E4iIEj9cZ-b_IrMeVw4C"},"id":"woLdj17MVzrJ","outputId":"8ee40bc1-706b-4143-a875-3aaae8765b6c"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["import rasterio\n","from detectron2.utils.visualizer import Visualizer\n","from detectree2.models.train import combine_dicts\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","from PIL import Image\n","import numpy as np\n","import cv2\n","import matplotlib.pyplot as plt\n","from IPython.display import display\n","\n","#val_fold = 1\n","name = \"Paracou\"\n","tiles = \"/tilesMS_\" + appends + \"/train\"\n","train_location = site_path + tiles\n","dataset_dicts = combine_dicts(train_location, val_fold)\n","trees_metadata = MetadataCatalog.get(name + \"_train\")\n","\n","# Function to normalize and convert multi-band image to RGB if needed\n","def prepare_image_for_visualization(image):\n"," if image.shape[2] == 3:\n"," # If the image has 3 bands, assume it's RGB\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n"," else:\n"," # If the image has more than 3 bands, choose the first 3 for visualization\n"," image = image[:, :, :3] # Or select specific bands\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n","\n"," return image\n","\n","# Visualize each image in the dataset\n","for d in dataset_dicts:\n"," with rasterio.open(d[\"file_name\"]) as src:\n"," img = src.read() # Read all bands\n"," img = np.transpose(img, (1, 2, 0)) # Convert to HWC format\n"," img = prepare_image_for_visualization(img) # Normalize and prepare for visualization\n","\n"," #img = img[:, :, :3]/10\n"," v = Visualizer(img[:, :, ::-1]*20, metadata=trees_metadata, scale=0.5)\n"," out = v.draw_dataset_dict(d)\n"," image = out.get_image()[:, :, ::-1]\n"," display(Image.fromarray(image))\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Qf7nXHaNvQPq"},"source":["## Train!\n","\n","GPU/CUDA should be available here. Chose which datasets you want to train and test on with `trains` and `tests`. Set up the configurations with `setup_cfg`.\n","\n","If tuning has been completed, train and validation datasets can be combined in `trains` for full training."]},{"cell_type":"markdown","metadata":{"id":"RdEteDyVXMgn"},"source":["Get training! Patience sets the number of evaluation periods that will be undergone without improvement in model performance before training will be terminated (best model will be saved)."]},{"cell_type":"markdown","metadata":{"id":"E-hRk4ce6buJ"},"source":["To train the model sequentially on a series of sites, loop over the \"names\""]},{"cell_type":"markdown","metadata":{"id":"f3EJqp8ZuhFw"},"source":["For that combines all sites, include them all in the `trains` and `tests` tuples"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":492,"status":"ok","timestamp":1725378969284,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"biP5xyYbFJg4","outputId":"569427d3-504b-42b1-b5c2-4b2782532ba9"},"outputs":[{"output_type":"stream","name":"stdout","text":["The raster has 5 bands.\n"]}],"source":["import rasterio\n","import os\n","import glob\n","\n","\n","# Read in geotif and assess mean and sd for each band\n","#site_path = \"/content/drive/MyDrive/WORK/detectree2/data/Paracou\"\n","folder_path = site_path + \"/tilesMS_\" + appends + \"/\"\n","\n","# Select path of first .tif file\n","img_paths = glob.glob(folder_path + \"*.tif\")\n","img_path = img_paths[0]\n","\n","# Open the raster file\n","with rasterio.open(img_path) as dataset:\n"," # Get the number of bands\n"," num_bands = dataset.count\n","\n","# Print the number of bands\n","print(f'The raster has {num_bands} bands.')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":244,"status":"ok","timestamp":1725286946436,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"VA5KB89pG5wp","outputId":"9cc463ec-4a14-44df-81ff-d671c92c1e57"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default PIXEL_MEAN: [103.53, 116.28, 123.675]\n","Default PIXEL_STD: [1.0, 1.0, 1.0]\n","New PIXEL_MEAN: [103.53, 116.28, 123.675, 103.53, 116.28]\n","New PIXEL_STD: [1.0, 1.0, 1.0, 1.0, 1.0]\n"]}],"source":["from detectron2.config import get_cfg\n","\n","cfg = get_cfg()\n","\n","# Adjust PIXEL_MEAN and PIXEL_STD for the number of bands\n","default_pixel_mean = cfg.MODEL.PIXEL_MEAN\n","default_pixel_std = cfg.MODEL.PIXEL_STD\n","\n","print(\"Default PIXEL_MEAN:\", default_pixel_mean)\n","print(\"Default PIXEL_STD:\", default_pixel_std)\n","\n","# Extend or truncate the PIXEL_MEAN and PIXEL_STD based on num_bands\n","new_pixel_mean = (default_pixel_mean * (num_bands // len(default_pixel_mean)) +\n"," default_pixel_mean[:num_bands % len(default_pixel_mean)])\n","new_pixel_std = (default_pixel_std * (num_bands // len(default_pixel_std)) +\n"," default_pixel_std[:num_bands % len(default_pixel_std)])\n","\n","print(\"New PIXEL_MEAN:\", new_pixel_mean)\n","print(\"New PIXEL_STD:\", new_pixel_std)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gy3KD3oqKvlO"},"outputs":[],"source":["import torch.nn as nn\n","import torch\n","\n","# Function to modify the first convolutional layer\n","def modify_conv1_weights(model, num_input_channels):\n"," with torch.no_grad():\n"," old_weights = model.backbone.bottom_up.stem.conv1.weight\n"," new_weights = torch.zeros((old_weights.size(0), num_input_channels, *old_weights.shape[2:]))\n","\n"," # Example: Repeat the first 3 channels across the new channels\n"," for i in range(num_input_channels):\n"," new_weights[:, i, :, :] = old_weights[:, i % 3, :, :]\n","\n"," model.backbone.bottom_up.stem.conv1 = nn.Conv2d(num_input_channels, old_weights.size(0), kernel_size=7, stride=2, padding=3, bias=False)\n"," model.backbone.bottom_up.stem.conv1.weight.copy_(new_weights)"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":1745,"status":"ok","timestamp":1725379076777,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"irksEkyFuV4A"},"outputs":[],"source":["from detectron2.modeling import build_model\n","#import torch\n","import torch.nn as nn\n","import torch.nn.init as init\n","from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers\n","import numpy as np\n","from detectree2.models.train import MyTrainer, setup_cfg, modify_conv1_weights\n","from datetime import date\n","\n","\n","today = date.today()\n","today = today.strftime(\"%y%m%d\")\n","\n","names = [\"ParacouMS\",]\n","#num_bands = 5\n","#trained_model = \"/content/drive/Shareddrives/detectree2/models/230103_resize_full/model_4.pth\"\n","\n","trains = (names[0] + \"_train\",)\n","tests = (names[0] + \"_val\",)\n","out_dir = \"/content/drive/MyDrive/WORK/detectree2/models/\" + today + \"_ParacouMS\"\n","\n","base_model = \"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\" # Path to the model config\n","\n","# When you increase the number of channels (i.e., the number of filters) in a Convolutional Neural Network (CNN), the general recommendation is to decrease the learning rate\n","lrs = [0.003, 0.0003, 0.00003]\n","\n","\n","cfg = setup_cfg(base_model, trains, tests, workers = 2, eval_period=50,\n"," base_lr = lrs[1], backbone_freeze=0, gamma = 0.9,\n"," #update_model = trained_model,\n"," max_iter=500000, out_dir=out_dir, resize = \"rand_fixed\", imgmode=\"ms\", num_bands=num_bands) # update_model arg can be used to load in trained model\n","\n","\n","# Might be necessary to fix sizes?\n","cfg.INPUT.MIN_SIZE_TEST = 1000\n","#cfg.INPUT.MAX_SIZE_TEST = 2000\n","\n","# Build the model\n","model = build_model(cfg)\n","\n","# Adjust input layer to accept correct number of channels\n","modify_conv1_weights(model, num_input_channels=num_bands)\n","\n","# Assuming num_classes is the number of classes (without background)\n","num_classes = 1 # Update this to your actual number of classes\n","\n","# Update ROI heads and Mask predictor - THIS DOESNT SEEM TO BE WORKING WELL, OMIT?\n","in_features = model.roi_heads.box_predictor.cls_score.in_features\n","model.roi_heads.box_predictor = FastRCNNOutputLayers(cfg, in_features)\n","\n","in_channels = model.roi_heads.mask_head.predictor.in_channels\n","model.roi_heads.mask_head.predictor = nn.Conv2d(in_channels, cfg.MODEL.ROI_HEADS.NUM_CLASSES, kernel_size=1)\n","\n","# Randomize the weights using one of the initialization methods\n","#init.kaiming_normal_(model.backbone.bottom_up.stem.conv1.weight, mode='fan_out', nonlinearity='relu')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":250,"status":"ok","timestamp":1724939030923,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"vLMhluwcjJfj","outputId":"cb770695-53bc-4621-89ba-7508fa5620f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["0.0003\n","0.9\n","10000\n","2\n","(210000, 250000)\n"]}],"source":["print(cfg.SOLVER.BASE_LR)\n","print(cfg.SOLVER.GAMMA)\n","print(cfg.SOLVER.MAX_ITER)\n","print(cfg.SOLVER.IMS_PER_BATCH)\n","print(cfg.SOLVER.STEPS)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49263,"status":"ok","timestamp":1724414402320,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"n_RsfFyya_S_","outputId":"1b06d035-9a96-4261-c73a-65dacfdd7107"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training dataset size: 76\n","Filtered dataset size: 76\n"]}],"source":["from detectron2.data import DatasetCatalog\n","\n","dataset_dicts = DatasetCatalog.get(cfg.DATASETS.TRAIN[0])\n","print(f\"Training dataset size: {len(dataset_dicts)}\")\n","# If images are filtered due to missing annotations, check and adjust the filtering\n","dataset_dicts = [d for d in dataset_dicts if len(d.get(\"annotations\", [])) > 0]\n","print(f\"Filtered dataset size: {len(dataset_dicts)}\")\n"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"2IguggsXHeHG","executionInfo":{"status":"ok","timestamp":1725379179825,"user_tz":-60,"elapsed":3,"user":{"displayName":"James Ball","userId":"12200917192257062155"}}},"outputs":[],"source":["cfg.SOLVER.IMS_PER_BATCH = 2 # Might need to reduce to 1 to go easy on GPU allocation"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":179126,"status":"ok","timestamp":1725383850520,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"KRGm517M6Mqn","outputId":"b52e7b58-de9a-42ad-f1bb-e6740c58e28c"},"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 15:59:57 d2.engine.defaults]: Model:\n","GeneralizedRCNN(\n"," (backbone): FPN(\n"," (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (top_block): LastLevelMaxPool()\n"," (bottom_up): ResNet(\n"," (stem): BasicStem(\n"," (conv1): Conv2d(\n"," 5, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," )\n"," (res2): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res3): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res4): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (4): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (5): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (6): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (7): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (8): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (9): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (10): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (11): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (12): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (13): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (14): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (15): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (16): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (17): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (18): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (19): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (20): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (21): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (22): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res5): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," )\n"," )\n"," )\n"," (proposal_generator): RPN(\n"," (rpn_head): StandardRPNHead(\n"," (conv): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n"," (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n"," )\n"," (anchor_generator): DefaultAnchorGenerator(\n"," (cell_anchors): BufferList()\n"," )\n"," )\n"," (roi_heads): StandardROIHeads(\n"," (box_pooler): ROIPooler(\n"," (level_poolers): ModuleList(\n"," (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"," (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"," (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"," (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"," )\n"," )\n"," (box_head): FastRCNNConvFCHead(\n"," (flatten): Flatten(start_dim=1, end_dim=-1)\n"," (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n"," (fc_relu1): ReLU()\n"," (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n"," (fc_relu2): ReLU()\n"," )\n"," (box_predictor): FastRCNNOutputLayers(\n"," (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n"," (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n"," )\n"," (mask_pooler): ROIPooler(\n"," (level_poolers): ModuleList(\n"," (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"," (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"," (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"," (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"," )\n"," )\n"," (mask_head): MaskRCNNConvUpsampleHead(\n"," (mask_fcn1): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn3): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (mask_fcn4): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n"," (deconv_relu): ReLU()\n"," (predictor): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n"," )\n"," )\n",")\n","[09/03 15:59:57 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [RandomRotation(angle=[90, 90], expand=False), RandomFlip(prob=0.4), RandomFlip(prob=0.4, horizontal=False, vertical=True), ResizeScale(min_scale=0.6, max_scale=1.4, target_height=1000, target_width=1000)]\n","[09/03 16:01:03 d2.data.build]: Removed 0 images with no usable annotations. 149 images left.\n","[09/03 16:01:03 d2.data.build]: Distribution of instances among all 1 categories:\n","| category | #instances |\n","|:----------:|:-------------|\n","| tree | 3971 |\n","| | |\n","[09/03 16:01:03 d2.data.build]: Using training sampler TrainingSampler\n","[09/03 16:01:03 d2.data.common]: Serializing the dataset using: \n","[09/03 16:01:03 d2.data.common]: Serializing 149 elements to byte tensors and concatenating them all ...\n","[09/03 16:01:03 d2.data.common]: Serialized dataset takes 2.94 MiB\n","[09/03 16:01:03 d2.data.build]: Making batched data loader with batch_size=2\n","[09/03 16:01:03 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=[1000, 1000], max_size=1333)]\n","[09/03 16:01:15 d2.data.build]: Distribution of instances among all 1 categories:\n","| category | #instances |\n","|:----------:|:-------------|\n","| tree | 988 |\n","| | |\n","[09/03 16:01:15 d2.data.common]: Serializing the dataset using: \n","[09/03 16:01:15 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:01:15 d2.data.common]: Serialized dataset takes 0.73 MiB\n","[09/03 16:01:15 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl ...\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["model_final_a3ec72.pkl: 254MB [00:01, 204MB/s] \n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'backbone.bottom_up.stem.conv1.weight' to the model due to incompatible shapes: (64, 3, 7, 7) in the checkpoint but (64, 5, 7, 7) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 1024) in the checkpoint but (2, 1024) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (2,) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 1024) in the checkpoint but (4, 1024) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (4,) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.mask_head.predictor.weight' to the model due to incompatible shapes: (80, 256, 1, 1) in the checkpoint but (1, 256, 1, 1) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.mask_head.predictor.bias' to the model due to incompatible shapes: (80,) in the checkpoint but (1,) in the model! You might want to double check if this is expected.\n","WARNING:fvcore.common.checkpoint:Some model parameters or buffers are not found in the checkpoint:\n","backbone.bottom_up.stem.conv1.weight\n","roi_heads.box_predictor.bbox_pred.{bias, weight}\n","roi_heads.box_predictor.cls_score.{bias, weight}\n","roi_heads.mask_head.predictor.{bias, weight}\n","/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)\n"," return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:01:49 d2.utils.events]: eta: 7 days, 20:30:31 iter: 19 total_loss: 4.638 loss_cls: 1.012 loss_box_reg: 0.09653 loss_mask: 0.7481 loss_rpn_cls: 2.128 loss_rpn_loc: 0.6183 time: 1.3135 last_time: 1.1480 data_time: 0.0606 last_data_time: 0.0340 lr: 4.7752e-05 max_mem: 7829M\n","[09/03 16:02:19 d2.utils.events]: eta: 7 days, 18:28:28 iter: 39 total_loss: 2.716 loss_cls: 0.7306 loss_box_reg: 0.7608 loss_mask: 0.7104 loss_rpn_cls: 0.3061 loss_rpn_loc: 0.2234 time: 1.2989 last_time: 1.6753 data_time: 0.0230 last_data_time: 0.0399 lr: 9.7702e-05 max_mem: 7829M\n","[09/03 16:02:31 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:02:32 d2.data.common]: Serializing the dataset using: \n","[09/03 16:02:32 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:02:32 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:02:32 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:02:32 d2.evaluation.coco_evaluation]: Trying to convert 'ParacouMS_val' to COCO format ...\n","[09/03 16:02:32 d2.data.datasets.coco]: Converting annotations of dataset 'ParacouMS_val' to COCO format ...)\n","[09/03 16:02:33 d2.data.datasets.coco]: Converting dataset dicts into COCO format\n","[09/03 16:02:33 d2.data.datasets.coco]: Conversion finished, #images: 37, #annotations: 988\n","[09/03 16:02:33 d2.data.datasets.coco]: Caching COCO format annotations at 'eval/ParacouMS_val_coco_format.json' ...\n","[09/03 16:02:34 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:02:39 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1826 s/iter. Eval: 0.2011 s/iter. Total: 0.3884 s/iter. ETA=0:00:10\n","[09/03 16:02:44 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0053 s/iter. Inference: 0.1814 s/iter. Eval: 0.2021 s/iter. Total: 0.3889 s/iter. ETA=0:00:05\n","[09/03 16:02:49 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0066 s/iter. Inference: 0.1825 s/iter. Eval: 0.2359 s/iter. Total: 0.4252 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:02:50 d2.evaluation.evaluator]: Total inference time: 0:00:13.901738 (0.434429 s / iter per device, on 1 devices)\n","[09/03 16:02:50 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182489 s / iter per device, on 1 devices)\n","[09/03 16:02:50 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:02:50 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:02:50 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:02:50 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.045\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.003\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.088\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.128\n","[09/03 16:02:51 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.899 | 4.540 | 0.030 | 0.000 | 1.077 | 1.293 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:02:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n","[09/03 16:02:51 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.004 | 0.029 | 0.000 | 0.000 | 0.000 | 0.013 |\n","[09/03 16:02:51 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: 0.8986,4.5405,0.0299,0.0000,1.0771,1.2927\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:02:51 d2.evaluation.testing]: copypaste: 0.0040,0.0287,0.0000,0.0000,0.0005,0.0126\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:03:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:03:01 d2.data.common]: Serializing the dataset using: \n","[09/03 16:03:01 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:03:01 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:03:01 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:03:01 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:03:07 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0264 s/iter. Inference: 0.1958 s/iter. Eval: 0.3059 s/iter. Total: 0.5281 s/iter. ETA=0:00:13\n","[09/03 16:03:12 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0154 s/iter. Inference: 0.1881 s/iter. Eval: 0.2491 s/iter. Total: 0.4530 s/iter. ETA=0:00:05\n","[09/03 16:03:17 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0116 s/iter. Inference: 0.1854 s/iter. Eval: 0.2280 s/iter. Total: 0.4254 s/iter. ETA=0:00:00\n","[09/03 16:03:17 d2.evaluation.evaluator]: Total inference time: 0:00:13.669259 (0.427164 s / iter per device, on 1 devices)\n","[09/03 16:03:17 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185376 s / iter per device, on 1 devices)\n","[09/03 16:03:17 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:03:17 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:03:17 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.045\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.011\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.003\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.088\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.128\n","[09/03 16:03:17 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.899 | 4.540 | 0.030 | 0.000 | 1.077 | 1.293 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.12 seconds.\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:03:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n","[09/03 16:03:17 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.004 | 0.029 | 0.000 | 0.000 | 0.000 | 0.013 |\n","[09/03 16:03:17 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: 0.8986,4.5405,0.0299,0.0000,1.0771,1.2927\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:03:17 d2.evaluation.testing]: copypaste: 0.0040,0.0287,0.0000,0.0000,0.0005,0.0126\n","Av. segm AP50 = 0.02866558625584296\n","[09/03 16:03:34 d2.utils.events]: eta: 7 days, 22:15:17 iter: 59 total_loss: 2.425 loss_cls: 0.5616 loss_box_reg: 0.8684 loss_mask: 0.6882 loss_rpn_cls: 0.2347 loss_rpn_loc: 0.1318 validation_loss: 2.53 time: 1.3154 last_time: 1.5432 data_time: 0.0277 last_data_time: 0.0323 lr: 0.00014765 max_mem: 7830M\n","[09/03 16:04:01 d2.utils.events]: eta: 8 days, 1:51:13 iter: 79 total_loss: 2.438 loss_cls: 0.5545 loss_box_reg: 0.897 loss_mask: 0.6791 loss_rpn_cls: 0.1946 loss_rpn_loc: 0.1458 validation_loss: 2.53 time: 1.3289 last_time: 1.6989 data_time: 0.0260 last_data_time: 0.0230 lr: 0.0001976 max_mem: 7830M\n","[09/03 16:04:26 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:04:27 d2.data.common]: Serializing the dataset using: \n","[09/03 16:04:27 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:04:27 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:04:27 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:04:27 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:04:32 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1807 s/iter. Eval: 0.1980 s/iter. Total: 0.3834 s/iter. ETA=0:00:09\n","[09/03 16:04:37 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0082 s/iter. Inference: 0.1846 s/iter. Eval: 0.2366 s/iter. Total: 0.4295 s/iter. ETA=0:00:06\n","[09/03 16:04:42 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0091 s/iter. Inference: 0.1844 s/iter. Eval: 0.2418 s/iter. Total: 0.4356 s/iter. ETA=0:00:01\n","[09/03 16:04:43 d2.evaluation.evaluator]: Total inference time: 0:00:13.872956 (0.433530 s / iter per device, on 1 devices)\n","[09/03 16:04:43 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184010 s / iter per device, on 1 devices)\n","[09/03 16:04:43 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:04:43 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:04:43 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:04:43 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:04:43 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:04:43 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:04:43 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.017\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.083\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.021\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.003\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.028\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.131\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.193\n","[09/03 16:04:43 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 1.684 | 8.281 | 0.092 | 0.000 | 2.058 | 2.501 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 16:04:44 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:04:44 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.19 seconds.\n","[09/03 16:04:44 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:04:44 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.030\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.010\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.069\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.106\n","[09/03 16:04:44 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.502 | 3.015 | 0.008 | 0.000 | 0.234 | 0.762 |\n","[09/03 16:04:44 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: 1.6843,8.2813,0.0920,0.0000,2.0584,2.5010\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:04:44 d2.evaluation.testing]: copypaste: 0.5024,3.0147,0.0077,0.0000,0.2338,0.7615\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:04:53 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:04:54 d2.data.common]: Serializing the dataset using: \n","[09/03 16:04:54 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:04:54 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:04:54 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:04:54 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:04:59 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1808 s/iter. Eval: 0.1944 s/iter. Total: 0.3798 s/iter. ETA=0:00:09\n","[09/03 16:05:04 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0056 s/iter. Inference: 0.1819 s/iter. Eval: 0.1957 s/iter. Total: 0.3834 s/iter. ETA=0:00:04\n","[09/03 16:05:09 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0068 s/iter. Inference: 0.1849 s/iter. Eval: 0.2296 s/iter. Total: 0.4215 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:05:10 d2.evaluation.evaluator]: Total inference time: 0:00:13.766847 (0.430214 s / iter per device, on 1 devices)\n","[09/03 16:05:10 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184752 s / iter per device, on 1 devices)\n","[09/03 16:05:10 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:05:10 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:05:10 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:05:10 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:05:10 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[09/03 16:05:10 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:05:10 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.017\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.083\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.021\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.003\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.028\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.131\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.193\n","[09/03 16:05:10 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 1.684 | 8.281 | 0.092 | 0.000 | 2.058 | 2.501 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:05:10 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:05:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.20 seconds.\n","[09/03 16:05:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:05:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.030\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.010\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.069\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.106\n","[09/03 16:05:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 0.502 | 3.015 | 0.008 | 0.000 | 0.234 | 0.762 |\n","[09/03 16:05:11 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: 1.6843,8.2813,0.0920,0.0000,2.0584,2.5010\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:05:11 d2.evaluation.testing]: copypaste: 0.5024,3.0147,0.0077,0.0000,0.2338,0.7615\n","Av. segm AP50 = 3.014708135138908\n","[09/03 16:05:13 d2.utils.events]: eta: 7 days, 22:14:22 iter: 99 total_loss: 2.467 loss_cls: 0.555 loss_box_reg: 0.8832 loss_mask: 0.6632 loss_rpn_cls: 0.1786 loss_rpn_loc: 0.1381 validation_loss: 2.485 time: 1.3129 last_time: 1.5948 data_time: 0.0237 last_data_time: 0.0116 lr: 0.00024755 max_mem: 7830M\n","[09/03 16:05:41 d2.utils.events]: eta: 8 days, 1:51:12 iter: 119 total_loss: 2.456 loss_cls: 0.5374 loss_box_reg: 0.8788 loss_mask: 0.6321 loss_rpn_cls: 0.1918 loss_rpn_loc: 0.1703 validation_loss: 2.485 time: 1.3288 last_time: 0.8903 data_time: 0.0332 last_data_time: 0.0524 lr: 0.0002975 max_mem: 7830M\n","[09/03 16:06:07 d2.utils.events]: eta: 8 days, 1:50:45 iter: 139 total_loss: 2.347 loss_cls: 0.5202 loss_box_reg: 0.8407 loss_mask: 0.599 loss_rpn_cls: 0.1849 loss_rpn_loc: 0.1283 validation_loss: 2.485 time: 1.3242 last_time: 1.0181 data_time: 0.0238 last_data_time: 0.0243 lr: 0.0003 max_mem: 7830M\n","[09/03 16:06:21 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:06:22 d2.data.common]: Serializing the dataset using: \n","[09/03 16:06:22 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:06:22 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:06:22 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:06:22 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:06:27 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0127 s/iter. Inference: 0.1920 s/iter. Eval: 0.3064 s/iter. Total: 0.5111 s/iter. ETA=0:00:13\n","[09/03 16:06:32 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0083 s/iter. Inference: 0.1836 s/iter. Eval: 0.2416 s/iter. Total: 0.4338 s/iter. ETA=0:00:05\n","[09/03 16:06:37 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0072 s/iter. Inference: 0.1816 s/iter. Eval: 0.2252 s/iter. Total: 0.4144 s/iter. ETA=0:00:00\n","[09/03 16:06:38 d2.evaluation.evaluator]: Total inference time: 0:00:13.329160 (0.416536 s / iter per device, on 1 devices)\n","[09/03 16:06:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181650 s / iter per device, on 1 devices)\n","[09/03 16:06:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:06:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:06:38 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.022\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.072\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.009\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.060\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.270\n","[09/03 16:06:38 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 4.809 | 18.622 | 0.417 | 0.000 | 2.220 | 7.175 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:06:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.040\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.150\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.061\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.055\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.229\n","[09/03 16:06:38 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 3.971 | 15.032 | 0.412 | 0.000 | 0.303 | 6.109 |\n","[09/03 16:06:38 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: 4.8088,18.6217,0.4174,0.0000,2.2198,7.1753\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:06:38 d2.evaluation.testing]: copypaste: 3.9709,15.0315,0.4118,0.0000,0.3029,6.1087\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:06:49 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:06:50 d2.data.common]: Serializing the dataset using: \n","[09/03 16:06:50 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:06:50 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:06:50 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:06:50 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:06:54 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1809 s/iter. Eval: 0.1984 s/iter. Total: 0.3839 s/iter. ETA=0:00:09\n","[09/03 16:06:59 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0101 s/iter. Inference: 0.1829 s/iter. Eval: 0.2390 s/iter. Total: 0.4322 s/iter. ETA=0:00:06\n","[09/03 16:07:05 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0099 s/iter. Inference: 0.1857 s/iter. Eval: 0.2396 s/iter. Total: 0.4354 s/iter. ETA=0:00:01\n","[09/03 16:07:06 d2.evaluation.evaluator]: Total inference time: 0:00:13.835144 (0.432348 s / iter per device, on 1 devices)\n","[09/03 16:07:06 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184926 s / iter per device, on 1 devices)\n","[09/03 16:07:06 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:07:06 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:07:06 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.022\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.072\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.009\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.060\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.270\n","[09/03 16:07:06 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 4.809 | 18.622 | 0.417 | 0.000 | 2.220 | 7.175 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:07:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.040\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.150\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.061\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.055\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.151\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.229\n","[09/03 16:07:06 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:-----:|\n","| 3.971 | 15.032 | 0.412 | 0.000 | 0.303 | 6.109 |\n","[09/03 16:07:06 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: 4.8088,18.6217,0.4174,0.0000,2.2198,7.1753\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:07:06 d2.evaluation.testing]: copypaste: 3.9709,15.0315,0.4118,0.0000,0.3029,6.1087\n","Av. segm AP50 = 15.031502333423031\n","[09/03 16:07:21 d2.utils.events]: eta: 8 days, 1:50:17 iter: 159 total_loss: 2.3 loss_cls: 0.5278 loss_box_reg: 0.9033 loss_mask: 0.5485 loss_rpn_cls: 0.144 loss_rpn_loc: 0.1389 validation_loss: 2.305 time: 1.3314 last_time: 1.2356 data_time: 0.0268 last_data_time: 0.0372 lr: 0.0003 max_mem: 7830M\n","[09/03 16:07:50 d2.utils.events]: eta: 8 days, 4:45:18 iter: 179 total_loss: 2.217 loss_cls: 0.506 loss_box_reg: 0.8889 loss_mask: 0.5192 loss_rpn_cls: 0.1329 loss_rpn_loc: 0.1315 validation_loss: 2.305 time: 1.3403 last_time: 1.6382 data_time: 0.0254 last_data_time: 0.0099 lr: 0.0003 max_mem: 7833M\n","[09/03 16:08:17 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:08:18 d2.data.common]: Serializing the dataset using: \n","[09/03 16:08:18 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:08:18 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:08:18 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:08:18 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:08:23 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0049 s/iter. Inference: 0.1811 s/iter. Eval: 0.1992 s/iter. Total: 0.3851 s/iter. ETA=0:00:10\n","[09/03 16:08:28 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0054 s/iter. Inference: 0.1811 s/iter. Eval: 0.1970 s/iter. Total: 0.3837 s/iter. ETA=0:00:04\n","[09/03 16:08:33 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0095 s/iter. Inference: 0.1834 s/iter. Eval: 0.2304 s/iter. Total: 0.4235 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:08:34 d2.evaluation.evaluator]: Total inference time: 0:00:13.847327 (0.432729 s / iter per device, on 1 devices)\n","[09/03 16:08:34 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183280 s / iter per device, on 1 devices)\n","[09/03 16:08:34 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:08:34 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:08:34 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:08:34 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:08:34 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:08:34 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:08:34 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.300\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.029\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.134\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.085\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338\n","[09/03 16:08:34 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.194 | 29.976 | 2.867 | 0.000 | 3.557 | 13.423 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:08:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:08:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:08:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:08:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.068\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.227\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.019\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.072\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264\n","[09/03 16:08:35 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 6.826 | 22.743 | 1.901 | 0.000 | 0.821 | 10.508 |\n","[09/03 16:08:35 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: 9.1935,29.9763,2.8672,0.0000,3.5571,13.4226\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:08:35 d2.evaluation.testing]: copypaste: 6.8265,22.7428,1.9010,0.0000,0.8206,10.5080\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:08:43 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:08:44 d2.data.common]: Serializing the dataset using: \n","[09/03 16:08:44 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:08:44 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:08:44 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:08:44 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:08:50 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0110 s/iter. Inference: 0.1883 s/iter. Eval: 0.3075 s/iter. Total: 0.5069 s/iter. ETA=0:00:13\n","[09/03 16:08:55 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0086 s/iter. Inference: 0.1857 s/iter. Eval: 0.2437 s/iter. Total: 0.4384 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:09:00 d2.evaluation.evaluator]: Total inference time: 0:00:13.359382 (0.417481 s / iter per device, on 1 devices)\n","[09/03 16:09:00 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183428 s / iter per device, on 1 devices)\n","[09/03 16:09:00 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:09:00 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:09:00 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:09:00 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:09:00 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:09:00 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:09:00 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.300\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.029\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.134\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.085\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338\n","[09/03 16:09:00 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.194 | 29.976 | 2.867 | 0.000 | 3.557 | 13.423 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:09:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:09:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 16:09:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:09:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.068\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.227\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.019\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.105\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.072\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.180\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264\n","[09/03 16:09:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 6.826 | 22.743 | 1.901 | 0.000 | 0.821 | 10.508 |\n","[09/03 16:09:01 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: 9.1935,29.9763,2.8672,0.0000,3.5571,13.4226\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:09:01 d2.evaluation.testing]: copypaste: 6.8265,22.7428,1.9010,0.0000,0.8206,10.5080\n","Av. segm AP50 = 22.74282852601906\n","[09/03 16:09:03 d2.utils.events]: eta: 8 days, 4:44:50 iter: 199 total_loss: 2.17 loss_cls: 0.4976 loss_box_reg: 0.8579 loss_mask: 0.4947 loss_rpn_cls: 0.1443 loss_rpn_loc: 0.1713 validation_loss: 2.174 time: 1.3424 last_time: 1.6760 data_time: 0.0286 last_data_time: 0.0364 lr: 0.0003 max_mem: 7833M\n","[09/03 16:09:29 d2.utils.events]: eta: 8 days, 3:30:44 iter: 219 total_loss: 2.045 loss_cls: 0.4951 loss_box_reg: 0.8406 loss_mask: 0.4841 loss_rpn_cls: 0.1191 loss_rpn_loc: 0.1133 validation_loss: 2.174 time: 1.3367 last_time: 1.5004 data_time: 0.0379 last_data_time: 0.0147 lr: 0.0003 max_mem: 7833M\n","[09/03 16:09:57 d2.utils.events]: eta: 8 days, 3:12:56 iter: 239 total_loss: 2.053 loss_cls: 0.4643 loss_box_reg: 0.777 loss_mask: 0.4671 loss_rpn_cls: 0.1302 loss_rpn_loc: 0.1507 validation_loss: 2.174 time: 1.3439 last_time: 1.4052 data_time: 0.0389 last_data_time: 0.0222 lr: 0.0003 max_mem: 7833M\n","[09/03 16:10:12 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:10:14 d2.data.common]: Serializing the dataset using: \n","[09/03 16:10:14 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:10:14 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:10:14 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:10:14 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:10:19 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0120 s/iter. Inference: 0.1850 s/iter. Eval: 0.2555 s/iter. Total: 0.4525 s/iter. ETA=0:00:11\n","[09/03 16:10:24 d2.evaluation.evaluator]: Inference done 21/37. Dataloading: 0.0179 s/iter. Inference: 0.1903 s/iter. Eval: 0.2751 s/iter. Total: 0.4836 s/iter. ETA=0:00:07\n","[09/03 16:10:29 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0126 s/iter. Inference: 0.1855 s/iter. Eval: 0.2420 s/iter. Total: 0.4403 s/iter. ETA=0:00:01\n","[09/03 16:10:30 d2.evaluation.evaluator]: Total inference time: 0:00:13.976130 (0.436754 s / iter per device, on 1 devices)\n","[09/03 16:10:30 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184708 s / iter per device, on 1 devices)\n","[09/03 16:10:30 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:10:30 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:10:30 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:10:30 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:10:30 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:10:30 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:10:30 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.098\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.298\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.039\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.038\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.143\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.320\n","[09/03 16:10:30 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.775 | 29.839 | 3.929 | 0.000 | 3.776 | 14.260 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:10:30 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:10:31 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:10:31 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:10:31 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.075\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.024\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.085\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.064\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.257\n","[09/03 16:10:31 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 7.479 | 24.150 | 2.395 | 0.000 | 1.203 | 11.180 |\n","[09/03 16:10:31 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: 9.7751,29.8387,3.9285,0.0004,3.7756,14.2600\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:10:31 d2.evaluation.testing]: copypaste: 7.4794,24.1495,2.3947,0.0000,1.2031,11.1795\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:10:42 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:10:43 d2.data.common]: Serializing the dataset using: \n","[09/03 16:10:43 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:10:43 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:10:43 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:10:43 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:10:47 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1836 s/iter. Eval: 0.1949 s/iter. Total: 0.3832 s/iter. ETA=0:00:09\n","[09/03 16:10:53 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0065 s/iter. Inference: 0.1835 s/iter. Eval: 0.2246 s/iter. Total: 0.4148 s/iter. ETA=0:00:05\n","[09/03 16:10:58 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0104 s/iter. Inference: 0.1863 s/iter. Eval: 0.2433 s/iter. Total: 0.4403 s/iter. ETA=0:00:01\n","[09/03 16:10:59 d2.evaluation.evaluator]: Total inference time: 0:00:14.028514 (0.438391 s / iter per device, on 1 devices)\n","[09/03 16:10:59 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185666 s / iter per device, on 1 devices)\n","[09/03 16:10:59 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:10:59 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:10:59 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:10:59 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:10:59 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:10:59 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:10:59 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.098\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.298\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.039\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.038\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.143\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.102\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.320\n","[09/03 16:10:59 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.775 | 29.839 | 3.929 | 0.000 | 3.776 | 14.260 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:10:59 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:11:00 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:11:00 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:11:00 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.075\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.024\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.012\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.085\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.064\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.257\n","[09/03 16:11:00 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 7.479 | 24.150 | 2.395 | 0.000 | 1.203 | 11.180 |\n","[09/03 16:11:00 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: 9.7751,29.8387,3.9285,0.0004,3.7756,14.2600\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:11:00 d2.evaluation.testing]: copypaste: 7.4794,24.1495,2.3947,0.0000,1.2031,11.1795\n","Av. segm AP50 = 24.149527173114212\n","[09/03 16:11:15 d2.utils.events]: eta: 8 days, 4:43:25 iter: 259 total_loss: 2.022 loss_cls: 0.4921 loss_box_reg: 0.7623 loss_mask: 0.4863 loss_rpn_cls: 0.129 loss_rpn_loc: 0.1872 validation_loss: 2.077 time: 1.3524 last_time: 0.8667 data_time: 0.0382 last_data_time: 0.0251 lr: 0.0003 max_mem: 7833M\n","[09/03 16:11:44 d2.utils.events]: eta: 8 days, 4:42:57 iter: 279 total_loss: 2.042 loss_cls: 0.474 loss_box_reg: 0.7559 loss_mask: 0.4795 loss_rpn_cls: 0.1194 loss_rpn_loc: 0.2011 validation_loss: 2.077 time: 1.3570 last_time: 0.7393 data_time: 0.0323 last_data_time: 0.0337 lr: 0.0003 max_mem: 7833M\n","[09/03 16:12:08 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:12:09 d2.data.common]: Serializing the dataset using: \n","[09/03 16:12:09 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:12:09 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:12:09 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:12:09 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:12:16 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0070 s/iter. Inference: 0.1887 s/iter. Eval: 0.2991 s/iter. Total: 0.4948 s/iter. ETA=0:00:12\n","[09/03 16:12:21 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0061 s/iter. Inference: 0.1837 s/iter. Eval: 0.2305 s/iter. Total: 0.4205 s/iter. ETA=0:00:05\n","[09/03 16:12:26 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0061 s/iter. Inference: 0.1831 s/iter. Eval: 0.2299 s/iter. Total: 0.4194 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:12:26 d2.evaluation.evaluator]: Total inference time: 0:00:13.596311 (0.424885 s / iter per device, on 1 devices)\n","[09/03 16:12:26 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183070 s / iter per device, on 1 devices)\n","[09/03 16:12:26 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:12:26 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:12:26 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:12:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:12:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:12:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:12:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.057\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.126\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.132\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372\n","[09/03 16:12:26 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 13.801 | 40.978 | 5.741 | 0.000 | 6.909 | 19.697 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 16:12:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:12:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.26 seconds.\n","[09/03 16:12:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:12:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.309\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.030\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.018\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.145\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.292\n","[09/03 16:12:27 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.735 | 30.923 | 2.994 | 0.000 | 1.831 | 14.520 |\n","[09/03 16:12:27 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: 13.8012,40.9782,5.7414,0.0000,6.9091,19.6966\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:12:27 d2.evaluation.testing]: copypaste: 9.7349,30.9226,2.9943,0.0000,1.8305,14.5199\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:12:36 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:12:37 d2.data.common]: Serializing the dataset using: \n","[09/03 16:12:37 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:12:37 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:12:37 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:12:37 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:12:43 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0091 s/iter. Inference: 0.1921 s/iter. Eval: 0.3017 s/iter. Total: 0.5029 s/iter. ETA=0:00:13\n","[09/03 16:12:48 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0078 s/iter. Inference: 0.1892 s/iter. Eval: 0.2542 s/iter. Total: 0.4515 s/iter. ETA=0:00:06\n","[09/03 16:12:53 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0069 s/iter. Inference: 0.1857 s/iter. Eval: 0.2313 s/iter. Total: 0.4241 s/iter. ETA=0:00:00\n","[09/03 16:12:53 d2.evaluation.evaluator]: Total inference time: 0:00:13.594534 (0.424829 s / iter per device, on 1 devices)\n","[09/03 16:12:53 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185520 s / iter per device, on 1 devices)\n","[09/03 16:12:53 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:12:53 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:12:53 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:12:53 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:12:53 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:12:53 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:12:53 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.410\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.057\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.126\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.132\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372\n","[09/03 16:12:53 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 13.801 | 40.978 | 5.741 | 0.000 | 6.909 | 19.697 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:12:53 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:12:54 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 16:12:54 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:12:54 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.097\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.309\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.030\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.018\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.145\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.099\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.292\n","[09/03 16:12:54 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n","| 9.735 | 30.923 | 2.994 | 0.000 | 1.831 | 14.520 |\n","[09/03 16:12:54 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: 13.8012,40.9782,5.7414,0.0000,6.9091,19.6966\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:12:54 d2.evaluation.testing]: copypaste: 9.7349,30.9226,2.9943,0.0000,1.8305,14.5199\n","Av. segm AP50 = 30.922606309427042\n","[09/03 16:12:56 d2.utils.events]: eta: 8 days, 2:49:14 iter: 299 total_loss: 1.92 loss_cls: 0.4603 loss_box_reg: 0.756 loss_mask: 0.4773 loss_rpn_cls: 0.1216 loss_rpn_loc: 0.1071 validation_loss: 1.999 time: 1.3479 last_time: 1.0066 data_time: 0.0233 last_data_time: 0.0111 lr: 0.0003 max_mem: 7833M\n","[09/03 16:13:22 d2.utils.events]: eta: 8 days, 1:36:14 iter: 319 total_loss: 1.895 loss_cls: 0.4478 loss_box_reg: 0.706 loss_mask: 0.452 loss_rpn_cls: 0.1191 loss_rpn_loc: 0.1489 validation_loss: 1.999 time: 1.3433 last_time: 1.2054 data_time: 0.0335 last_data_time: 0.0180 lr: 0.0003 max_mem: 7833M\n","[09/03 16:13:50 d2.utils.events]: eta: 8 days, 2:27:13 iter: 339 total_loss: 1.87 loss_cls: 0.4424 loss_box_reg: 0.7246 loss_mask: 0.4548 loss_rpn_cls: 0.1113 loss_rpn_loc: 0.1713 validation_loss: 1.999 time: 1.3492 last_time: 1.2993 data_time: 0.0296 last_data_time: 0.0282 lr: 0.0003 max_mem: 7833M\n","[09/03 16:14:02 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:14:03 d2.data.common]: Serializing the dataset using: \n","[09/03 16:14:03 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:14:03 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:14:03 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:14:03 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:14:07 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1827 s/iter. Eval: 0.2015 s/iter. Total: 0.3889 s/iter. ETA=0:00:10\n","[09/03 16:14:12 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0101 s/iter. Inference: 0.1860 s/iter. Eval: 0.2353 s/iter. Total: 0.4317 s/iter. ETA=0:00:06\n","[09/03 16:14:18 d2.evaluation.evaluator]: Inference done 32/37. Dataloading: 0.0131 s/iter. Inference: 0.1873 s/iter. Eval: 0.2667 s/iter. Total: 0.4674 s/iter. ETA=0:00:02\n","[09/03 16:14:21 d2.evaluation.evaluator]: Total inference time: 0:00:15.548559 (0.485892 s / iter per device, on 1 devices)\n","[09/03 16:14:21 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.186461 s / iter per device, on 1 devices)\n","[09/03 16:14:21 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:14:21 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:14:21 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.158\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.061\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.221\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.131\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.366\n","[09/03 16:14:21 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 15.848 | 45.239 | 6.079 | 0.000 | 6.841 | 22.118 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:14:21 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.114\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.354\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.041\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.023\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.164\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295\n","[09/03 16:14:21 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 11.353 | 35.404 | 4.072 | 0.000 | 2.306 | 16.426 |\n","[09/03 16:14:21 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: 15.8481,45.2395,6.0787,0.0000,6.8408,22.1181\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:14:21 d2.evaluation.testing]: copypaste: 11.3534,35.4036,4.0724,0.0000,2.3064,16.4258\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:14:31 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:14:32 d2.data.common]: Serializing the dataset using: \n","[09/03 16:14:32 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:14:32 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:14:32 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:14:32 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:14:36 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1824 s/iter. Eval: 0.2003 s/iter. Total: 0.3875 s/iter. ETA=0:00:10\n","[09/03 16:14:41 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0057 s/iter. Inference: 0.2043 s/iter. Eval: 0.2013 s/iter. Total: 0.4116 s/iter. ETA=0:00:05\n","[09/03 16:14:47 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0072 s/iter. Inference: 0.1987 s/iter. Eval: 0.2368 s/iter. Total: 0.4429 s/iter. ETA=0:00:01\n","[09/03 16:14:48 d2.evaluation.evaluator]: Total inference time: 0:00:14.265407 (0.445794 s / iter per device, on 1 devices)\n","[09/03 16:14:48 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:06 (0.197314 s / iter per device, on 1 devices)\n","[09/03 16:14:48 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:14:48 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:14:48 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:14:48 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:14:48 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:14:48 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:14:48 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.158\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.452\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.061\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.221\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.131\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.163\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.366\n","[09/03 16:14:48 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 15.848 | 45.239 | 6.079 | 0.000 | 6.841 | 22.118 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:14:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:14:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:14:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:14:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.114\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.354\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.041\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.023\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.164\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.109\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295\n","[09/03 16:14:49 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 11.353 | 35.404 | 4.072 | 0.000 | 2.306 | 16.426 |\n","[09/03 16:14:49 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: 15.8481,45.2395,6.0787,0.0000,6.8408,22.1181\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:14:49 d2.evaluation.testing]: copypaste: 11.3534,35.4036,4.0724,0.0000,2.3064,16.4258\n","Av. segm AP50 = 35.403602755293086\n","[09/03 16:15:05 d2.utils.events]: eta: 8 days, 2:08:49 iter: 359 total_loss: 1.988 loss_cls: 0.4489 loss_box_reg: 0.7362 loss_mask: 0.4633 loss_rpn_cls: 0.1229 loss_rpn_loc: 0.1335 validation_loss: 1.956 time: 1.3471 last_time: 1.4043 data_time: 0.0252 last_data_time: 0.0241 lr: 0.0003 max_mem: 7833M\n","[09/03 16:15:34 d2.utils.events]: eta: 8 days, 2:43:43 iter: 379 total_loss: 1.902 loss_cls: 0.4596 loss_box_reg: 0.7077 loss_mask: 0.4605 loss_rpn_cls: 0.0989 loss_rpn_loc: 0.09422 validation_loss: 1.956 time: 1.3530 last_time: 1.8426 data_time: 0.0332 last_data_time: 0.0727 lr: 0.0003 max_mem: 7833M\n","[09/03 16:16:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:16:01 d2.data.common]: Serializing the dataset using: \n","[09/03 16:16:01 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:16:01 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:16:01 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:16:01 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:16:06 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0050 s/iter. Inference: 0.1860 s/iter. Eval: 0.2187 s/iter. Total: 0.4097 s/iter. ETA=0:00:10\n","[09/03 16:16:11 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0060 s/iter. Inference: 0.1813 s/iter. Eval: 0.2054 s/iter. Total: 0.3928 s/iter. ETA=0:00:05\n","[09/03 16:16:16 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0061 s/iter. Inference: 0.1813 s/iter. Eval: 0.2146 s/iter. Total: 0.4021 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:16:17 d2.evaluation.evaluator]: Total inference time: 0:00:13.044266 (0.407633 s / iter per device, on 1 devices)\n","[09/03 16:16:17 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181450 s / iter per device, on 1 devices)\n","[09/03 16:16:17 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:16:17 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:16:17 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:16:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:16:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:16:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:16:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.179\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.491\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.150\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.162\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.403\n","[09/03 16:16:17 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 17.880 | 49.099 | 9.596 | 0.000 | 9.450 | 25.210 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 16:16:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:16:18 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.28 seconds.\n","[09/03 16:16:18 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:16:18 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.381\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.053\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.034\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.193\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.123\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.113\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.329\n","[09/03 16:16:18 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 13.356 | 38.120 | 5.265 | 0.000 | 3.425 | 19.270 |\n","[09/03 16:16:18 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: 17.8797,49.0989,9.5959,0.0000,9.4502,25.2103\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:16:18 d2.evaluation.testing]: copypaste: 13.3557,38.1196,5.2645,0.0000,3.4250,19.2696\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:16:28 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:16:29 d2.data.common]: Serializing the dataset using: \n","[09/03 16:16:29 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:16:29 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:16:29 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:16:29 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:16:34 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0110 s/iter. Inference: 0.1917 s/iter. Eval: 0.3098 s/iter. Total: 0.5126 s/iter. ETA=0:00:13\n","[09/03 16:16:39 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0090 s/iter. Inference: 0.1889 s/iter. Eval: 0.2538 s/iter. Total: 0.4521 s/iter. ETA=0:00:06\n","[09/03 16:16:45 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0074 s/iter. Inference: 0.1858 s/iter. Eval: 0.2288 s/iter. Total: 0.4223 s/iter. ETA=0:00:00\n","[09/03 16:16:45 d2.evaluation.evaluator]: Total inference time: 0:00:13.578339 (0.424323 s / iter per device, on 1 devices)\n","[09/03 16:16:45 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185769 s / iter per device, on 1 devices)\n","[09/03 16:16:45 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:16:45 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:16:45 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.179\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.491\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.020\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.150\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.162\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.403\n","[09/03 16:16:45 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 17.880 | 49.099 | 9.596 | 0.000 | 9.450 | 25.210 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:16:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.381\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.053\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.034\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.193\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.123\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.113\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.329\n","[09/03 16:16:45 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 13.356 | 38.120 | 5.265 | 0.000 | 3.425 | 19.270 |\n","[09/03 16:16:45 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: 17.8797,49.0989,9.5959,0.0000,9.4502,25.2103\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:16:45 d2.evaluation.testing]: copypaste: 13.3557,38.1196,5.2645,0.0000,3.4250,19.2696\n","Av. segm AP50 = 38.11964331787113\n","[09/03 16:16:47 d2.utils.events]: eta: 8 days, 2:43:15 iter: 399 total_loss: 1.823 loss_cls: 0.426 loss_box_reg: 0.6899 loss_mask: 0.442 loss_rpn_cls: 0.1071 loss_rpn_loc: 0.08914 validation_loss: 1.869 time: 1.3488 last_time: 0.9782 data_time: 0.0300 last_data_time: 0.0384 lr: 0.0003 max_mem: 7833M\n","[09/03 16:17:14 d2.utils.events]: eta: 8 days, 2:54:55 iter: 419 total_loss: 1.814 loss_cls: 0.4306 loss_box_reg: 0.7148 loss_mask: 0.4452 loss_rpn_cls: 0.11 loss_rpn_loc: 0.1096 validation_loss: 1.869 time: 1.3479 last_time: 1.5112 data_time: 0.0281 last_data_time: 0.0116 lr: 0.0003 max_mem: 7833M\n","[09/03 16:17:43 d2.utils.events]: eta: 8 days, 2:58:06 iter: 439 total_loss: 1.725 loss_cls: 0.4067 loss_box_reg: 0.6606 loss_mask: 0.4392 loss_rpn_cls: 0.1015 loss_rpn_loc: 0.09461 validation_loss: 1.869 time: 1.3520 last_time: 1.1495 data_time: 0.0327 last_data_time: 0.0278 lr: 0.0003 max_mem: 7833M\n","[09/03 16:17:54 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:17:55 d2.data.common]: Serializing the dataset using: \n","[09/03 16:17:55 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:17:55 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:17:55 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:17:55 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:18:00 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0051 s/iter. Inference: 0.1802 s/iter. Eval: 0.1991 s/iter. Total: 0.3844 s/iter. ETA=0:00:09\n","[09/03 16:18:05 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0067 s/iter. Inference: 0.1816 s/iter. Eval: 0.2224 s/iter. Total: 0.4109 s/iter. ETA=0:00:05\n","[09/03 16:18:10 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0079 s/iter. Inference: 0.1845 s/iter. Eval: 0.2420 s/iter. Total: 0.4347 s/iter. ETA=0:00:01\n","[09/03 16:18:11 d2.evaluation.evaluator]: Total inference time: 0:00:13.827022 (0.432094 s / iter per device, on 1 devices)\n","[09/03 16:18:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183848 s / iter per device, on 1 devices)\n","[09/03 16:18:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:18:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:18:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:18:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:18:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:18:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:18:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.445\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.071\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.087\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.390\n","[09/03 16:18:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 15.573 | 44.470 | 7.128 | 0.000 | 8.687 | 21.613 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:18:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:18:12 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:18:12 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:18:12 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.118\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.353\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.044\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.117\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.134\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n","[09/03 16:18:12 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 11.773 | 35.338 | 4.374 | 0.000 | 3.126 | 16.853 |\n","[09/03 16:18:12 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: 15.5732,44.4697,7.1282,0.0000,8.6867,21.6133\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:18:12 d2.evaluation.testing]: copypaste: 11.7729,35.3380,4.3737,0.0000,3.1256,16.8534\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:18:22 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:18:23 d2.data.common]: Serializing the dataset using: \n","[09/03 16:18:23 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:18:23 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:18:23 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:18:23 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:18:28 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0049 s/iter. Inference: 0.1826 s/iter. Eval: 0.1983 s/iter. Total: 0.3859 s/iter. ETA=0:00:10\n","[09/03 16:18:33 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0056 s/iter. Inference: 0.1818 s/iter. Eval: 0.1984 s/iter. Total: 0.3860 s/iter. ETA=0:00:05\n","[09/03 16:18:38 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0071 s/iter. Inference: 0.1828 s/iter. Eval: 0.2407 s/iter. Total: 0.4308 s/iter. ETA=0:00:01\n","[09/03 16:18:39 d2.evaluation.evaluator]: Total inference time: 0:00:14.103355 (0.440730 s / iter per device, on 1 devices)\n","[09/03 16:18:39 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182893 s / iter per device, on 1 devices)\n","[09/03 16:18:39 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:18:39 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:18:40 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.445\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.071\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.087\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.017\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.178\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.390\n","[09/03 16:18:40 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 15.573 | 44.470 | 7.128 | 0.000 | 8.687 | 21.613 |\n","Loading and preparing results...\n","DONE (t=0.09s)\n","creating index...\n","index created!\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:18:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.118\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.353\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.044\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.117\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.134\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n","[09/03 16:18:40 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 11.773 | 35.338 | 4.374 | 0.000 | 3.126 | 16.853 |\n","[09/03 16:18:40 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: 15.5732,44.4697,7.1282,0.0000,8.6867,21.6133\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:18:40 d2.evaluation.testing]: copypaste: 11.7729,35.3380,4.3737,0.0000,3.1256,16.8534\n","Av. segm AP50 = 35.33797634268773\n","[09/03 16:18:54 d2.utils.events]: eta: 8 days, 2:24:25 iter: 459 total_loss: 1.939 loss_cls: 0.4337 loss_box_reg: 0.6957 loss_mask: 0.4433 loss_rpn_cls: 0.1191 loss_rpn_loc: 0.2479 validation_loss: 1.908 time: 1.3476 last_time: 1.8060 data_time: 0.0234 last_data_time: 0.0237 lr: 0.0003 max_mem: 7833M\n","[09/03 16:19:20 d2.utils.events]: eta: 8 days, 1:36:40 iter: 479 total_loss: 1.785 loss_cls: 0.4036 loss_box_reg: 0.6646 loss_mask: 0.4223 loss_rpn_cls: 0.1074 loss_rpn_loc: 0.1221 validation_loss: 1.908 time: 1.3468 last_time: 1.7211 data_time: 0.0253 last_data_time: 0.0266 lr: 0.0003 max_mem: 7833M\n","[09/03 16:19:50 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:19:51 d2.data.common]: Serializing the dataset using: \n","[09/03 16:19:51 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:19:51 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:19:51 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:19:51 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:19:57 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0126 s/iter. Inference: 0.1923 s/iter. Eval: 0.2468 s/iter. Total: 0.4518 s/iter. ETA=0:00:11\n","[09/03 16:20:03 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0080 s/iter. Inference: 0.1833 s/iter. Eval: 0.2091 s/iter. Total: 0.4006 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:20:07 d2.evaluation.evaluator]: Total inference time: 0:00:12.726050 (0.397689 s / iter per device, on 1 devices)\n","[09/03 16:20:07 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181234 s / iter per device, on 1 devices)\n","[09/03 16:20:07 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:20:07 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:20:07 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:20:07 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:20:07 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.074\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.239\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.140\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.158\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410\n","[09/03 16:20:08 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 16.922 | 46.202 | 7.395 | 0.000 | 8.802 | 23.948 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.24 seconds.\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:20:08 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.128\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.382\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.033\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.118\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.118\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n","[09/03 16:20:08 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 12.775 | 38.164 | 4.775 | 0.000 | 3.344 | 18.428 |\n","[09/03 16:20:08 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: 16.9221,46.2019,7.3945,0.0000,8.8023,23.9479\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:20:08 d2.evaluation.testing]: copypaste: 12.7747,38.1644,4.7747,0.0000,3.3437,18.4275\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:20:18 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:20:19 d2.data.common]: Serializing the dataset using: \n","[09/03 16:20:19 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:20:19 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:20:19 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:20:19 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:20:24 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0076 s/iter. Inference: 0.1910 s/iter. Eval: 0.2512 s/iter. Total: 0.4497 s/iter. ETA=0:00:11\n","[09/03 16:20:30 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0104 s/iter. Inference: 0.1893 s/iter. Eval: 0.2762 s/iter. Total: 0.4763 s/iter. ETA=0:00:07\n","[09/03 16:20:35 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0083 s/iter. Inference: 0.1847 s/iter. Eval: 0.2369 s/iter. Total: 0.4302 s/iter. ETA=0:00:00\n","[09/03 16:20:35 d2.evaluation.evaluator]: Total inference time: 0:00:13.789632 (0.430926 s / iter per device, on 1 devices)\n","[09/03 16:20:35 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184637 s / iter per device, on 1 devices)\n","[09/03 16:20:35 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:20:35 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:20:35 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:20:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:20:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:20:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:20:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.462\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.074\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.239\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.140\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.158\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.410\n","[09/03 16:20:35 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 16.922 | 46.202 | 7.395 | 0.000 | 8.802 | 23.948 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:20:36 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:20:36 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:20:36 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:20:36 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.128\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.382\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.033\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.015\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.118\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.118\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n","[09/03 16:20:36 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 12.775 | 38.164 | 4.775 | 0.000 | 3.344 | 18.428 |\n","[09/03 16:20:36 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: 16.9221,46.2019,7.3945,0.0000,8.8023,23.9479\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:20:36 d2.evaluation.testing]: copypaste: 12.7747,38.1644,4.7747,0.0000,3.3437,18.4275\n","Av. segm AP50 = 38.16437261209464\n","[09/03 16:20:38 d2.utils.events]: eta: 8 days, 2:06:10 iter: 499 total_loss: 1.711 loss_cls: 0.4132 loss_box_reg: 0.6779 loss_mask: 0.4377 loss_rpn_cls: 0.09367 loss_rpn_loc: 0.07488 validation_loss: 1.845 time: 1.3515 last_time: 1.3419 data_time: 0.0393 last_data_time: 0.0232 lr: 0.0003 max_mem: 7833M\n","[09/03 16:21:04 d2.utils.events]: eta: 8 days, 1:35:44 iter: 519 total_loss: 1.768 loss_cls: 0.4044 loss_box_reg: 0.6652 loss_mask: 0.4344 loss_rpn_cls: 0.1059 loss_rpn_loc: 0.1299 validation_loss: 1.845 time: 1.3504 last_time: 1.3988 data_time: 0.0267 last_data_time: 0.0196 lr: 0.0003 max_mem: 7833M\n","[09/03 16:21:30 d2.utils.events]: eta: 8 days, 2:03:39 iter: 539 total_loss: 1.715 loss_cls: 0.4128 loss_box_reg: 0.6663 loss_mask: 0.4271 loss_rpn_cls: 0.09619 loss_rpn_loc: 0.08509 validation_loss: 1.845 time: 1.3491 last_time: 1.2361 data_time: 0.0274 last_data_time: 0.0243 lr: 0.0003 max_mem: 7833M\n","[09/03 16:21:44 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:21:44 d2.data.common]: Serializing the dataset using: \n","[09/03 16:21:44 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:21:44 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:21:45 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:21:45 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:21:49 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0052 s/iter. Inference: 0.1810 s/iter. Eval: 0.1967 s/iter. Total: 0.3829 s/iter. ETA=0:00:09\n","[09/03 16:21:54 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0058 s/iter. Inference: 0.1815 s/iter. Eval: 0.2022 s/iter. Total: 0.3897 s/iter. ETA=0:00:05\n","[09/03 16:21:59 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0122 s/iter. Inference: 0.1858 s/iter. Eval: 0.2392 s/iter. Total: 0.4376 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:22:01 d2.evaluation.evaluator]: Total inference time: 0:00:13.905620 (0.434551 s / iter per device, on 1 devices)\n","[09/03 16:22:01 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185227 s / iter per device, on 1 devices)\n","[09/03 16:22:01 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:22:01 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:22:01 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.527\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.084\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.166\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.176\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441\n","[09/03 16:22:01 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.410 | 52.665 | 13.408 | 0.023 | 8.419 | 30.354 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:22:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.247\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.141\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374\n","[09/03 16:22:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 17.208 | 45.463 | 8.818 | 0.000 | 4.474 | 24.750 |\n","[09/03 16:22:01 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: 21.4101,52.6651,13.4077,0.0233,8.4186,30.3540\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:22:01 d2.evaluation.testing]: copypaste: 17.2078,45.4630,8.8177,0.0000,4.4738,24.7499\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:22:10 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:22:11 d2.data.common]: Serializing the dataset using: \n","[09/03 16:22:11 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:22:11 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:22:11 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:22:11 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:22:17 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0131 s/iter. Inference: 0.1924 s/iter. Eval: 0.2511 s/iter. Total: 0.4566 s/iter. ETA=0:00:11\n","[09/03 16:22:22 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0089 s/iter. Inference: 0.1872 s/iter. Eval: 0.2644 s/iter. Total: 0.4607 s/iter. ETA=0:00:06\n","[09/03 16:22:27 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0079 s/iter. Inference: 0.1851 s/iter. Eval: 0.2455 s/iter. Total: 0.4387 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:22:28 d2.evaluation.evaluator]: Total inference time: 0:00:14.269732 (0.445929 s / iter per device, on 1 devices)\n","[09/03 16:22:28 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184898 s / iter per device, on 1 devices)\n","[09/03 16:22:28 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:22:28 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:22:29 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.527\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.084\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.166\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.176\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441\n","[09/03 16:22:29 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.410 | 52.665 | 13.408 | 0.023 | 8.419 | 30.354 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.27 seconds.\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:22:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.455\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.247\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.141\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.276\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374\n","[09/03 16:22:29 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 17.208 | 45.463 | 8.818 | 0.000 | 4.474 | 24.750 |\n","[09/03 16:22:29 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: 21.4101,52.6651,13.4077,0.0233,8.4186,30.3540\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:22:29 d2.evaluation.testing]: copypaste: 17.2078,45.4630,8.8177,0.0000,4.4738,24.7499\n","Av. segm AP50 = 45.462964710947176\n","[09/03 16:22:47 d2.utils.events]: eta: 8 days, 2:05:02 iter: 559 total_loss: 1.8 loss_cls: 0.4105 loss_box_reg: 0.6508 loss_mask: 0.4278 loss_rpn_cls: 0.103 loss_rpn_loc: 0.1431 validation_loss: 1.799 time: 1.3534 last_time: 1.0419 data_time: 0.0349 last_data_time: 0.0589 lr: 0.0003 max_mem: 7833M\n","[09/03 16:23:18 d2.utils.events]: eta: 8 days, 2:54:49 iter: 579 total_loss: 1.692 loss_cls: 0.4153 loss_box_reg: 0.6568 loss_mask: 0.4165 loss_rpn_cls: 0.0991 loss_rpn_loc: 0.09304 validation_loss: 1.799 time: 1.3587 last_time: 0.7293 data_time: 0.0348 last_data_time: 0.0229 lr: 0.0003 max_mem: 7833M\n","[09/03 16:23:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:23:46 d2.data.common]: Serializing the dataset using: \n","[09/03 16:23:46 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:23:46 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:23:46 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:23:46 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:23:52 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0064 s/iter. Inference: 0.1832 s/iter. Eval: 0.1980 s/iter. Total: 0.3877 s/iter. ETA=0:00:10\n","[09/03 16:23:58 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0077 s/iter. Inference: 0.1825 s/iter. Eval: 0.2085 s/iter. Total: 0.3988 s/iter. ETA=0:00:05\n","[09/03 16:24:03 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0093 s/iter. Inference: 0.1850 s/iter. Eval: 0.2398 s/iter. Total: 0.4343 s/iter. ETA=0:00:00\n","[09/03 16:24:04 d2.evaluation.evaluator]: Total inference time: 0:00:13.998163 (0.437443 s / iter per device, on 1 devices)\n","[09/03 16:24:04 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184918 s / iter per device, on 1 devices)\n","[09/03 16:24:04 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:24:04 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:24:04 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.526\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.303\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450\n","[09/03 16:24:04 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.423 | 52.620 | 12.487 | 0.066 | 8.621 | 30.330 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:24:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.170\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.448\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.093\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.146\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.144\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n","[09/03 16:24:04 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 16.963 | 44.800 | 9.282 | 0.000 | 4.831 | 24.344 |\n","[09/03 16:24:04 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: 21.4230,52.6199,12.4873,0.0660,8.6210,30.3296\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:24:04 d2.evaluation.testing]: copypaste: 16.9626,44.7997,9.2817,0.0000,4.8306,24.3437\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:24:13 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:24:14 d2.data.common]: Serializing the dataset using: \n","[09/03 16:24:14 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:24:14 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:24:14 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:24:14 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:24:20 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0181 s/iter. Inference: 0.1938 s/iter. Eval: 0.2990 s/iter. Total: 0.5109 s/iter. ETA=0:00:13\n","[09/03 16:24:25 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0123 s/iter. Inference: 0.1898 s/iter. Eval: 0.2626 s/iter. Total: 0.4653 s/iter. ETA=0:00:06\n","[09/03 16:24:30 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0095 s/iter. Inference: 0.1860 s/iter. Eval: 0.2357 s/iter. Total: 0.4316 s/iter. ETA=0:00:00\n","[09/03 16:24:31 d2.evaluation.evaluator]: Total inference time: 0:00:13.937875 (0.435559 s / iter per device, on 1 devices)\n","[09/03 16:24:31 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185810 s / iter per device, on 1 devices)\n","[09/03 16:24:31 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:24:31 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:24:31 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:24:31 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:24:31 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:24:31 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:24:31 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.526\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.303\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450\n","[09/03 16:24:31 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.423 | 52.620 | 12.487 | 0.066 | 8.621 | 30.330 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:24:32 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:24:32 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.24 seconds.\n","[09/03 16:24:32 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:24:32 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.170\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.448\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.093\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.048\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.018\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.146\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.144\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n","[09/03 16:24:32 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 16.963 | 44.800 | 9.282 | 0.000 | 4.831 | 24.344 |\n","[09/03 16:24:32 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: 21.4230,52.6199,12.4873,0.0660,8.6210,30.3296\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:24:32 d2.evaluation.testing]: copypaste: 16.9626,44.7997,9.2817,0.0000,4.8306,24.3437\n","Av. segm AP50 = 44.79973920920976\n","[09/03 16:24:32 d2.utils.events]: eta: 8 days, 2:38:34 iter: 599 total_loss: 1.846 loss_cls: 0.4096 loss_box_reg: 0.6501 loss_mask: 0.4269 loss_rpn_cls: 0.1109 loss_rpn_loc: 0.2228 validation_loss: 1.773 time: 1.3589 last_time: 1.7710 data_time: 0.0306 last_data_time: 0.0249 lr: 0.0003 max_mem: 7833M\n","[09/03 16:24:59 d2.utils.events]: eta: 8 days, 2:17:18 iter: 619 total_loss: 1.66 loss_cls: 0.3942 loss_box_reg: 0.6481 loss_mask: 0.4195 loss_rpn_cls: 0.09756 loss_rpn_loc: 0.1063 validation_loss: 1.773 time: 1.3583 last_time: 1.1656 data_time: 0.0304 last_data_time: 0.0324 lr: 0.0003 max_mem: 7833M\n","[09/03 16:25:25 d2.utils.events]: eta: 8 days, 2:16:50 iter: 639 total_loss: 1.67 loss_cls: 0.3853 loss_box_reg: 0.6297 loss_mask: 0.3988 loss_rpn_cls: 0.08988 loss_rpn_loc: 0.09415 validation_loss: 1.773 time: 1.3572 last_time: 1.2198 data_time: 0.0307 last_data_time: 0.0141 lr: 0.0003 max_mem: 7833M\n","[09/03 16:25:39 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:25:39 d2.data.common]: Serializing the dataset using: \n","[09/03 16:25:39 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:25:39 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:25:39 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:25:39 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:25:44 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0049 s/iter. Inference: 0.1818 s/iter. Eval: 0.1938 s/iter. Total: 0.3805 s/iter. ETA=0:00:09\n","[09/03 16:25:49 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0081 s/iter. Inference: 0.1835 s/iter. Eval: 0.2319 s/iter. Total: 0.4238 s/iter. ETA=0:00:05\n","[09/03 16:25:55 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0097 s/iter. Inference: 0.1851 s/iter. Eval: 0.2376 s/iter. Total: 0.4327 s/iter. ETA=0:00:00\n","[09/03 16:25:55 d2.evaluation.evaluator]: Total inference time: 0:00:13.809126 (0.431535 s / iter per device, on 1 devices)\n","[09/03 16:25:55 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184789 s / iter per device, on 1 devices)\n","[09/03 16:25:55 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:25:55 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:25:55 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:25:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:25:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:25:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:25:56 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463\n","[09/03 16:25:56 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.090 | 56.921 | 16.804 | 0.157 | 10.191 | 33.658 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:25:56 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:25:56 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:25:56 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:25:56 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.500\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.046\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.155\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389\n","[09/03 16:25:56 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.489 | 49.957 | 12.045 | 0.000 | 4.624 | 27.784 |\n","[09/03 16:25:56 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: 24.0896,56.9214,16.8042,0.1568,10.1906,33.6579\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:25:56 d2.evaluation.testing]: copypaste: 19.4886,49.9569,12.0448,0.0000,4.6238,27.7842\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:26:05 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:26:06 d2.data.common]: Serializing the dataset using: \n","[09/03 16:26:06 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:26:06 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:26:06 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:26:06 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:26:11 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0061 s/iter. Inference: 0.1831 s/iter. Eval: 0.1961 s/iter. Total: 0.3854 s/iter. ETA=0:00:10\n","[09/03 16:26:16 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0061 s/iter. Inference: 0.1818 s/iter. Eval: 0.2145 s/iter. Total: 0.4026 s/iter. ETA=0:00:05\n","[09/03 16:26:22 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0074 s/iter. Inference: 0.1837 s/iter. Eval: 0.2366 s/iter. Total: 0.4280 s/iter. ETA=0:00:00\n","[09/03 16:26:23 d2.evaluation.evaluator]: Total inference time: 0:00:13.932026 (0.435376 s / iter per device, on 1 devices)\n","[09/03 16:26:23 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183650 s / iter per device, on 1 devices)\n","[09/03 16:26:23 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:26:23 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:26:23 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.241\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.463\n","[09/03 16:26:23 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.090 | 56.921 | 16.804 | 0.157 | 10.191 | 33.658 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.17 seconds.\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:26:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.500\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.046\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.278\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.155\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389\n","[09/03 16:26:23 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.489 | 49.957 | 12.045 | 0.000 | 4.624 | 27.784 |\n","[09/03 16:26:23 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: 24.0896,56.9214,16.8042,0.1568,10.1906,33.6579\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:26:23 d2.evaluation.testing]: copypaste: 19.4886,49.9569,12.0448,0.0000,4.6238,27.7842\n","Av. segm AP50 = 49.95693225767316\n","[09/03 16:26:41 d2.utils.events]: eta: 8 days, 2:23:59 iter: 659 total_loss: 1.676 loss_cls: 0.3721 loss_box_reg: 0.6173 loss_mask: 0.4164 loss_rpn_cls: 0.0979 loss_rpn_loc: 0.1329 validation_loss: 1.729 time: 1.3593 last_time: 1.8288 data_time: 0.0372 last_data_time: 0.0688 lr: 0.0003 max_mem: 7833M\n","[09/03 16:27:07 d2.utils.events]: eta: 8 days, 2:15:54 iter: 679 total_loss: 1.697 loss_cls: 0.3748 loss_box_reg: 0.627 loss_mask: 0.4192 loss_rpn_cls: 0.1005 loss_rpn_loc: 0.1364 validation_loss: 1.729 time: 1.3580 last_time: 1.4438 data_time: 0.0296 last_data_time: 0.0133 lr: 0.0003 max_mem: 7833M\n","[09/03 16:27:35 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:27:36 d2.data.common]: Serializing the dataset using: \n","[09/03 16:27:36 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:27:36 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:27:36 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:27:36 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:27:41 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0051 s/iter. Inference: 0.1823 s/iter. Eval: 0.2324 s/iter. Total: 0.4199 s/iter. ETA=0:00:10\n","[09/03 16:27:47 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0055 s/iter. Inference: 0.1810 s/iter. Eval: 0.2060 s/iter. Total: 0.3927 s/iter. ETA=0:00:04\n","[09/03 16:27:52 d2.evaluation.evaluator]: Total inference time: 0:00:12.970294 (0.405322 s / iter per device, on 1 devices)\n","[09/03 16:27:52 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181049 s / iter per device, on 1 devices)\n","[09/03 16:27:52 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:27:52 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:27:52 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:27:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:27:52 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:27:52 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:27:52 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.175\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.220\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461\n","[09/03 16:27:52 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.210 | 56.851 | 15.567 | 0.056 | 11.197 | 32.237 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:27:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:27:53 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.22 seconds.\n","[09/03 16:27:53 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:27:53 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.185\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.101\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.055\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.149\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.388\n","[09/03 16:27:53 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 18.547 | 48.874 | 10.052 | 0.024 | 5.450 | 26.368 |\n","[09/03 16:27:53 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: 23.2098,56.8510,15.5667,0.0561,11.1973,32.2371\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:27:53 d2.evaluation.testing]: copypaste: 18.5474,48.8736,10.0524,0.0236,5.4500,26.3678\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:28:03 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:28:03 d2.data.common]: Serializing the dataset using: \n","[09/03 16:28:03 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:28:03 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:28:03 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:28:03 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:28:08 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0136 s/iter. Inference: 0.1868 s/iter. Eval: 0.2583 s/iter. Total: 0.4587 s/iter. ETA=0:00:11\n","[09/03 16:28:14 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0102 s/iter. Inference: 0.1896 s/iter. Eval: 0.2652 s/iter. Total: 0.4653 s/iter. ETA=0:00:06\n","[09/03 16:28:19 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0083 s/iter. Inference: 0.1857 s/iter. Eval: 0.2374 s/iter. Total: 0.4316 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:28:19 d2.evaluation.evaluator]: Total inference time: 0:00:13.780877 (0.430652 s / iter per device, on 1 devices)\n","[09/03 16:28:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185273 s / iter per device, on 1 devices)\n","[09/03 16:28:19 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:28:19 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:28:19 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:28:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.569\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.175\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.355\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.220\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461\n","[09/03 16:28:20 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.210 | 56.851 | 15.567 | 0.056 | 11.197 | 32.237 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:28:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.185\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.101\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.055\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.149\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.294\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.388\n","[09/03 16:28:20 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 18.547 | 48.874 | 10.052 | 0.024 | 5.450 | 26.368 |\n","[09/03 16:28:20 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: 23.2098,56.8510,15.5667,0.0561,11.1973,32.2371\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:28:20 d2.evaluation.testing]: copypaste: 18.5474,48.8736,10.0524,0.0236,5.4500,26.3678\n","Av. segm AP50 = 48.8736093524891\n","[09/03 16:28:20 d2.utils.events]: eta: 8 days, 2:15:26 iter: 699 total_loss: 1.617 loss_cls: 0.3888 loss_box_reg: 0.6047 loss_mask: 0.4154 loss_rpn_cls: 0.08715 loss_rpn_loc: 0.0977 validation_loss: 1.747 time: 1.3592 last_time: 1.6603 data_time: 0.0280 last_data_time: 0.0230 lr: 0.0003 max_mem: 7833M\n","[09/03 16:28:47 d2.utils.events]: eta: 8 days, 2:14:58 iter: 719 total_loss: 1.624 loss_cls: 0.3801 loss_box_reg: 0.6325 loss_mask: 0.4069 loss_rpn_cls: 0.09758 loss_rpn_loc: 0.09575 validation_loss: 1.747 time: 1.3584 last_time: 1.1466 data_time: 0.0279 last_data_time: 0.0213 lr: 0.0003 max_mem: 7833M\n","[09/03 16:29:15 d2.utils.events]: eta: 8 days, 2:22:07 iter: 739 total_loss: 1.69 loss_cls: 0.3729 loss_box_reg: 0.6132 loss_mask: 0.4036 loss_rpn_cls: 0.09737 loss_rpn_loc: 0.1568 validation_loss: 1.747 time: 1.3598 last_time: 1.6125 data_time: 0.0311 last_data_time: 0.0205 lr: 0.0003 max_mem: 7833M\n","[09/03 16:29:28 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:29:28 d2.data.common]: Serializing the dataset using: \n","[09/03 16:29:28 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:29:28 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:29:28 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:29:28 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:29:33 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0044 s/iter. Inference: 0.1805 s/iter. Eval: 0.1931 s/iter. Total: 0.3780 s/iter. ETA=0:00:09\n","[09/03 16:29:38 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0069 s/iter. Inference: 0.1821 s/iter. Eval: 0.2055 s/iter. Total: 0.3946 s/iter. ETA=0:00:05\n","[09/03 16:29:43 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0120 s/iter. Inference: 0.1859 s/iter. Eval: 0.2402 s/iter. Total: 0.4385 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:29:45 d2.evaluation.evaluator]: Total inference time: 0:00:14.050840 (0.439089 s / iter per device, on 1 devices)\n","[09/03 16:29:45 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185370 s / iter per device, on 1 devices)\n","[09/03 16:29:45 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:29:45 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:29:45 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.153\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.105\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.215\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n","[09/03 16:29:45 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.949 | 57.008 | 15.317 | 0.154 | 10.513 | 33.307 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:29:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.050\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.156\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.164\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385\n","[09/03 16:29:45 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.600 | 49.476 | 12.004 | 0.000 | 5.025 | 27.967 |\n","[09/03 16:29:45 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: 23.9493,57.0076,15.3171,0.1540,10.5130,33.3073\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:29:45 d2.evaluation.testing]: copypaste: 19.6000,49.4765,12.0045,0.0000,5.0253,27.9669\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:29:54 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:29:55 d2.data.common]: Serializing the dataset using: \n","[09/03 16:29:55 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:29:55 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:29:55 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:29:55 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:30:01 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0141 s/iter. Inference: 0.2003 s/iter. Eval: 0.3107 s/iter. Total: 0.5252 s/iter. ETA=0:00:13\n","[09/03 16:30:06 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0095 s/iter. Inference: 0.1882 s/iter. Eval: 0.2345 s/iter. Total: 0.4328 s/iter. ETA=0:00:05\n","[09/03 16:30:11 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0081 s/iter. Inference: 0.1857 s/iter. Eval: 0.2333 s/iter. Total: 0.4275 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:30:12 d2.evaluation.evaluator]: Total inference time: 0:00:13.853459 (0.432921 s / iter per device, on 1 devices)\n","[09/03 16:30:12 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185701 s / iter per device, on 1 devices)\n","[09/03 16:30:12 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:30:12 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:30:12 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.153\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.105\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.215\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n","[09/03 16:30:12 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 23.949 | 57.008 | 15.317 | 0.154 | 10.513 | 33.307 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.24 seconds.\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:30:12 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.196\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.050\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.156\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.164\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.385\n","[09/03 16:30:12 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.600 | 49.476 | 12.004 | 0.000 | 5.025 | 27.967 |\n","[09/03 16:30:12 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: 23.9493,57.0076,15.3171,0.1540,10.5130,33.3073\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:30:12 d2.evaluation.testing]: copypaste: 19.6000,49.4765,12.0045,0.0000,5.0253,27.9669\n","Av. segm AP50 = 49.47648707788182\n","[09/03 16:30:26 d2.utils.events]: eta: 8 days, 2:14:02 iter: 759 total_loss: 1.642 loss_cls: 0.3787 loss_box_reg: 0.6215 loss_mask: 0.4126 loss_rpn_cls: 0.08666 loss_rpn_loc: 0.06213 validation_loss: 1.737 time: 1.3588 last_time: 1.5009 data_time: 0.0294 last_data_time: 0.0255 lr: 0.0003 max_mem: 7833M\n","[09/03 16:30:57 d2.utils.events]: eta: 8 days, 2:46:29 iter: 779 total_loss: 1.66 loss_cls: 0.3827 loss_box_reg: 0.6269 loss_mask: 0.4038 loss_rpn_cls: 0.08466 loss_rpn_loc: 0.08937 validation_loss: 1.737 time: 1.3633 last_time: 1.4465 data_time: 0.0323 last_data_time: 0.0236 lr: 0.0003 max_mem: 7833M\n","[09/03 16:31:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:31:26 d2.data.common]: Serializing the dataset using: \n","[09/03 16:31:26 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:31:26 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:31:26 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:31:26 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:31:31 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0133 s/iter. Inference: 0.1907 s/iter. Eval: 0.2945 s/iter. Total: 0.4984 s/iter. ETA=0:00:12\n","[09/03 16:31:36 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0092 s/iter. Inference: 0.1852 s/iter. Eval: 0.2539 s/iter. Total: 0.4489 s/iter. ETA=0:00:06\n","[09/03 16:31:42 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0076 s/iter. Inference: 0.1827 s/iter. Eval: 0.2297 s/iter. Total: 0.4203 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:31:42 d2.evaluation.evaluator]: Total inference time: 0:00:13.521850 (0.422558 s / iter per device, on 1 devices)\n","[09/03 16:31:42 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182679 s / iter per device, on 1 devices)\n","[09/03 16:31:42 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:31:42 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:31:42 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.339\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.217\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466\n","[09/03 16:31:42 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.196 | 55.446 | 17.250 | 0.010 | 9.480 | 33.876 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:31:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.127\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.047\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.158\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.172\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396\n","[09/03 16:31:42 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.534 | 48.884 | 12.742 | 0.009 | 4.725 | 28.002 |\n","[09/03 16:31:42 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: 24.1964,55.4459,17.2504,0.0096,9.4800,33.8756\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:31:42 d2.evaluation.testing]: copypaste: 19.5336,48.8842,12.7419,0.0093,4.7249,28.0021\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:31:53 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:31:54 d2.data.common]: Serializing the dataset using: \n","[09/03 16:31:54 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:31:54 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:31:54 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:31:54 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:31:58 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0045 s/iter. Inference: 0.1817 s/iter. Eval: 0.1978 s/iter. Total: 0.3840 s/iter. ETA=0:00:09\n","[09/03 16:32:03 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0064 s/iter. Inference: 0.1846 s/iter. Eval: 0.2209 s/iter. Total: 0.4120 s/iter. ETA=0:00:05\n","[09/03 16:32:09 d2.evaluation.evaluator]: Inference done 33/37. Dataloading: 0.0140 s/iter. Inference: 0.1878 s/iter. Eval: 0.2485 s/iter. Total: 0.4504 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:32:10 d2.evaluation.evaluator]: Total inference time: 0:00:14.217715 (0.444304 s / iter per device, on 1 devices)\n","[09/03 16:32:10 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.186784 s / iter per device, on 1 devices)\n","[09/03 16:32:10 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:32:10 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:32:10 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:32:10 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:32:10 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:32:10 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:32:10 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.339\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.217\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466\n","[09/03 16:32:10 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.196 | 55.446 | 17.250 | 0.010 | 9.480 | 33.876 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:32:10 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:32:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:32:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:32:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.195\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.489\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.127\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.047\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.158\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.172\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.396\n","[09/03 16:32:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 19.534 | 48.884 | 12.742 | 0.009 | 4.725 | 28.002 |\n","[09/03 16:32:11 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: 24.1964,55.4459,17.2504,0.0096,9.4800,33.8756\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:32:11 d2.evaluation.testing]: copypaste: 19.5336,48.8842,12.7419,0.0093,4.7249,28.0021\n","Av. segm AP50 = 48.88420431081571\n","[09/03 16:32:11 d2.utils.events]: eta: 8 days, 2:49:40 iter: 799 total_loss: 1.626 loss_cls: 0.3726 loss_box_reg: 0.6052 loss_mask: 0.403 loss_rpn_cls: 0.09343 loss_rpn_loc: 0.1297 validation_loss: 1.722 time: 1.3647 last_time: 1.7122 data_time: 0.0275 last_data_time: 0.0213 lr: 0.0003 max_mem: 7833M\n","[09/03 16:32:38 d2.utils.events]: eta: 8 days, 2:49:12 iter: 819 total_loss: 1.747 loss_cls: 0.3731 loss_box_reg: 0.6305 loss_mask: 0.3949 loss_rpn_cls: 0.09816 loss_rpn_loc: 0.1991 validation_loss: 1.722 time: 1.3652 last_time: 1.5352 data_time: 0.0308 last_data_time: 0.0477 lr: 0.0003 max_mem: 7833M\n","[09/03 16:33:08 d2.utils.events]: eta: 8 days, 3:20:20 iter: 839 total_loss: 1.613 loss_cls: 0.3823 loss_box_reg: 0.6158 loss_mask: 0.3937 loss_rpn_cls: 0.0821 loss_rpn_loc: 0.07694 validation_loss: 1.722 time: 1.3684 last_time: 1.3035 data_time: 0.0380 last_data_time: 0.0673 lr: 0.0003 max_mem: 7833M\n","[09/03 16:33:21 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:33:22 d2.data.common]: Serializing the dataset using: \n","[09/03 16:33:22 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:33:22 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:33:22 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:33:22 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:33:27 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0045 s/iter. Inference: 0.1797 s/iter. Eval: 0.1986 s/iter. Total: 0.3829 s/iter. ETA=0:00:09\n","[09/03 16:33:32 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0054 s/iter. Inference: 0.1792 s/iter. Eval: 0.1964 s/iter. Total: 0.3812 s/iter. ETA=0:00:04\n","[09/03 16:33:37 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0076 s/iter. Inference: 0.1801 s/iter. Eval: 0.2235 s/iter. Total: 0.4113 s/iter. ETA=0:00:00\n","[09/03 16:33:38 d2.evaluation.evaluator]: Total inference time: 0:00:13.352071 (0.417252 s / iter per device, on 1 devices)\n","[09/03 16:33:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180059 s / iter per device, on 1 devices)\n","[09/03 16:33:38 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:33:38 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:33:38 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:33:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:33:38 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[09/03 16:33:38 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:33:38 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.260\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.115\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.191\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:33:38 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.965 | 59.774 | 18.317 | 0.594 | 11.503 | 35.844 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 16:33:38 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:33:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.19 seconds.\n","[09/03 16:33:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:33:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.213\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.057\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.166\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408\n","[09/03 16:33:39 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.294 | 52.823 | 13.586 | 0.000 | 5.672 | 30.106 |\n","[09/03 16:33:39 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: 25.9651,59.7740,18.3166,0.5941,11.5028,35.8436\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:33:39 d2.evaluation.testing]: copypaste: 21.2936,52.8225,13.5864,0.0000,5.6720,30.1055\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:33:47 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:33:48 d2.data.common]: Serializing the dataset using: \n","[09/03 16:33:48 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:33:48 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:33:48 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:33:48 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:33:54 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0169 s/iter. Inference: 0.1976 s/iter. Eval: 0.3063 s/iter. Total: 0.5208 s/iter. ETA=0:00:13\n","[09/03 16:33:59 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0114 s/iter. Inference: 0.1880 s/iter. Eval: 0.2436 s/iter. Total: 0.4433 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:34:04 d2.evaluation.evaluator]: Total inference time: 0:00:13.424793 (0.419525 s / iter per device, on 1 devices)\n","[09/03 16:34:04 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184472 s / iter per device, on 1 devices)\n","[09/03 16:34:04 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:34:04 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:34:04 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:34:04 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:34:04 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:34:04 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:34:04 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.260\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.115\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.191\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.235\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:34:04 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.965 | 59.774 | 18.317 | 0.594 | 11.503 | 35.844 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:34:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:34:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:34:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:34:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.213\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.136\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.057\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.166\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408\n","[09/03 16:34:05 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.294 | 52.823 | 13.586 | 0.000 | 5.672 | 30.106 |\n","[09/03 16:34:05 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: 25.9651,59.7740,18.3166,0.5941,11.5028,35.8436\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:34:05 d2.evaluation.testing]: copypaste: 21.2936,52.8225,13.5864,0.0000,5.6720,30.1055\n","Av. segm AP50 = 52.82254725018408\n","[09/03 16:34:21 d2.utils.events]: eta: 8 days, 3:19:52 iter: 859 total_loss: 1.691 loss_cls: 0.4085 loss_box_reg: 0.6349 loss_mask: 0.4169 loss_rpn_cls: 0.0911 loss_rpn_loc: 0.08267 validation_loss: 1.716 time: 1.3670 last_time: 1.5669 data_time: 0.0256 last_data_time: 0.0334 lr: 0.0003 max_mem: 7833M\n","[09/03 16:34:50 d2.utils.events]: eta: 8 days, 3:28:59 iter: 879 total_loss: 1.512 loss_cls: 0.3647 loss_box_reg: 0.593 loss_mask: 0.3912 loss_rpn_cls: 0.08065 loss_rpn_loc: 0.09135 validation_loss: 1.716 time: 1.3689 last_time: 1.4182 data_time: 0.0328 last_data_time: 0.0312 lr: 0.0003 max_mem: 7833M\n","[09/03 16:35:18 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:35:19 d2.data.common]: Serializing the dataset using: \n","[09/03 16:35:19 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:35:19 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:35:19 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:35:19 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:35:24 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0098 s/iter. Inference: 0.1863 s/iter. Eval: 0.2751 s/iter. Total: 0.4712 s/iter. ETA=0:00:12\n","[09/03 16:35:29 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0103 s/iter. Inference: 0.1846 s/iter. Eval: 0.2526 s/iter. Total: 0.4480 s/iter. ETA=0:00:06\n","[09/03 16:35:34 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0081 s/iter. Inference: 0.1804 s/iter. Eval: 0.2208 s/iter. Total: 0.4097 s/iter. ETA=0:00:00\n","[09/03 16:35:35 d2.evaluation.evaluator]: Total inference time: 0:00:13.174132 (0.411692 s / iter per device, on 1 devices)\n","[09/03 16:35:35 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180399 s / iter per device, on 1 devices)\n","[09/03 16:35:35 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:35:35 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:35:35 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.190\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472\n","[09/03 16:35:35 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.984 | 57.010 | 18.220 | 0.004 | 11.167 | 34.886 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:35:35 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.129\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.291\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399\n","[09/03 16:35:35 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 20.486 | 50.515 | 12.871 | 0.000 | 5.769 | 29.110 |\n","[09/03 16:35:35 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: 24.9839,57.0095,18.2200,0.0035,11.1671,34.8862\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:35:35 d2.evaluation.testing]: copypaste: 20.4862,50.5154,12.8706,0.0000,5.7691,29.1099\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:35:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:35:46 d2.data.common]: Serializing the dataset using: \n","[09/03 16:35:46 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:35:46 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:35:46 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:35:46 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:35:50 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1786 s/iter. Eval: 0.1891 s/iter. Total: 0.3724 s/iter. ETA=0:00:09\n","[09/03 16:35:56 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0064 s/iter. Inference: 0.1797 s/iter. Eval: 0.2059 s/iter. Total: 0.3923 s/iter. ETA=0:00:05\n","[09/03 16:36:01 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0102 s/iter. Inference: 0.1818 s/iter. Eval: 0.2253 s/iter. Total: 0.4175 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:36:02 d2.evaluation.evaluator]: Total inference time: 0:00:13.334239 (0.416695 s / iter per device, on 1 devices)\n","[09/03 16:36:02 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181403 s / iter per device, on 1 devices)\n","[09/03 16:36:02 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:36:02 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:36:02 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.182\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.112\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.349\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.190\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472\n","[09/03 16:36:02 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.984 | 57.010 | 18.220 | 0.004 | 11.167 | 34.886 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:36:02 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.129\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.291\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399\n","[09/03 16:36:02 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 20.486 | 50.515 | 12.871 | 0.000 | 5.769 | 29.110 |\n","[09/03 16:36:02 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: 24.9839,57.0095,18.2200,0.0035,11.1671,34.8862\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:36:02 d2.evaluation.testing]: copypaste: 20.4862,50.5154,12.8706,0.0000,5.7691,29.1099\n","Av. segm AP50 = 50.51540436990187\n","[09/03 16:36:02 d2.utils.events]: eta: 8 days, 3:58:10 iter: 899 total_loss: 1.569 loss_cls: 0.3686 loss_box_reg: 0.5999 loss_mask: 0.3939 loss_rpn_cls: 0.0877 loss_rpn_loc: 0.08843 validation_loss: 1.651 time: 1.3698 last_time: 1.5099 data_time: 0.0261 last_data_time: 0.0235 lr: 0.0003 max_mem: 7833M\n","[09/03 16:36:31 d2.utils.events]: eta: 8 days, 4:27:50 iter: 919 total_loss: 1.726 loss_cls: 0.3559 loss_box_reg: 0.589 loss_mask: 0.3928 loss_rpn_cls: 0.09478 loss_rpn_loc: 0.2026 validation_loss: 1.651 time: 1.3716 last_time: 1.7536 data_time: 0.0279 last_data_time: 0.0658 lr: 0.0003 max_mem: 7833M\n","[09/03 16:36:58 d2.utils.events]: eta: 8 days, 4:18:28 iter: 939 total_loss: 1.573 loss_cls: 0.3571 loss_box_reg: 0.5982 loss_mask: 0.3945 loss_rpn_cls: 0.0897 loss_rpn_loc: 0.1207 validation_loss: 1.651 time: 1.3710 last_time: 1.7282 data_time: 0.0306 last_data_time: 0.0718 lr: 0.0003 max_mem: 7833M\n","[09/03 16:37:11 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:37:12 d2.data.common]: Serializing the dataset using: \n","[09/03 16:37:12 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:37:12 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:37:12 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:37:12 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:37:18 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0052 s/iter. Inference: 0.1877 s/iter. Eval: 0.2128 s/iter. Total: 0.4058 s/iter. ETA=0:00:10\n","[09/03 16:37:23 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0056 s/iter. Inference: 0.1794 s/iter. Eval: 0.1934 s/iter. Total: 0.3787 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:37:27 d2.evaluation.evaluator]: Total inference time: 0:00:12.166402 (0.380200 s / iter per device, on 1 devices)\n","[09/03 16:37:27 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.178191 s / iter per device, on 1 devices)\n","[09/03 16:37:27 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:37:27 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:37:27 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:37:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:37:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:37:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:37:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.588\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.188\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.116\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.192\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[09/03 16:37:28 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.853 | 58.778 | 18.762 | 0.008 | 11.624 | 35.887 |\n","Loading and preparing results...\n","DONE (t=0.09s)\n","creating index...\n","index created!\n","[09/03 16:37:28 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:37:28 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.24 seconds.\n","[09/03 16:37:28 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:37:28 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.519\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:37:28 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.223 | 51.888 | 14.612 | 0.002 | 5.954 | 30.100 |\n","[09/03 16:37:28 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: 25.8534,58.7783,18.7618,0.0083,11.6235,35.8869\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:37:28 d2.evaluation.testing]: copypaste: 21.2230,51.8879,14.6122,0.0018,5.9539,30.0996\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:37:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:37:39 d2.data.common]: Serializing the dataset using: \n","[09/03 16:37:39 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:37:39 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:37:39 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:37:39 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:37:43 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0097 s/iter. Inference: 0.1781 s/iter. Eval: 0.2067 s/iter. Total: 0.3944 s/iter. ETA=0:00:10\n","[09/03 16:37:49 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0126 s/iter. Inference: 0.1873 s/iter. Eval: 0.2575 s/iter. Total: 0.4576 s/iter. ETA=0:00:06\n","[09/03 16:37:54 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0102 s/iter. Inference: 0.1823 s/iter. Eval: 0.2320 s/iter. Total: 0.4248 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:37:55 d2.evaluation.evaluator]: Total inference time: 0:00:13.625650 (0.425802 s / iter per device, on 1 devices)\n","[09/03 16:37:55 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182188 s / iter per device, on 1 devices)\n","[09/03 16:37:55 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:37:55 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:37:55 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.259\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.588\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.188\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.116\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.192\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[09/03 16:37:55 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.853 | 58.778 | 18.762 | 0.008 | 11.624 | 35.887 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:37:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.519\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.301\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.171\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:37:55 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.223 | 51.888 | 14.612 | 0.002 | 5.954 | 30.100 |\n","[09/03 16:37:55 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: 25.8534,58.7783,18.7618,0.0083,11.6235,35.8869\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:37:55 d2.evaluation.testing]: copypaste: 21.2230,51.8879,14.6122,0.0018,5.9539,30.0996\n","Av. segm AP50 = 51.88793436326219\n","[09/03 16:38:09 d2.utils.events]: eta: 8 days, 3:17:31 iter: 959 total_loss: 1.518 loss_cls: 0.3501 loss_box_reg: 0.5899 loss_mask: 0.3918 loss_rpn_cls: 0.07312 loss_rpn_loc: 0.07119 validation_loss: 1.676 time: 1.3699 last_time: 1.2808 data_time: 0.0296 last_data_time: 0.0230 lr: 0.0003 max_mem: 7833M\n","[09/03 16:38:37 d2.utils.events]: eta: 8 days, 3:34:34 iter: 979 total_loss: 1.533 loss_cls: 0.3494 loss_box_reg: 0.5719 loss_mask: 0.3857 loss_rpn_cls: 0.08636 loss_rpn_loc: 0.1136 validation_loss: 1.676 time: 1.3708 last_time: 1.6618 data_time: 0.0298 last_data_time: 0.0185 lr: 0.0003 max_mem: 7833M\n","[09/03 16:39:03 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:39:04 d2.data.common]: Serializing the dataset using: \n","[09/03 16:39:04 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:39:04 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:39:04 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:39:04 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:39:08 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0050 s/iter. Inference: 0.1772 s/iter. Eval: 0.1786 s/iter. Total: 0.3608 s/iter. ETA=0:00:09\n","[09/03 16:39:13 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0057 s/iter. Inference: 0.1778 s/iter. Eval: 0.1819 s/iter. Total: 0.3656 s/iter. ETA=0:00:04\n","[09/03 16:39:19 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0064 s/iter. Inference: 0.1782 s/iter. Eval: 0.2149 s/iter. Total: 0.3997 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:39:19 d2.evaluation.evaluator]: Total inference time: 0:00:12.938499 (0.404328 s / iter per device, on 1 devices)\n","[09/03 16:39:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.178231 s / iter per device, on 1 devices)\n","[09/03 16:39:19 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:39:19 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:39:19 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:39:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:39:19 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:39:19 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:39:19 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.592\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.192\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:39:19 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.188 | 59.196 | 20.195 | 0.445 | 11.967 | 36.091 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:39:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:39:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:39:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:39:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.530\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.073\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.172\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:39:20 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.137 | 53.015 | 14.725 | 0.014 | 7.320 | 31.122 |\n","[09/03 16:39:20 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: 26.1879,59.1964,20.1954,0.4451,11.9674,36.0912\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:39:20 d2.evaluation.testing]: copypaste: 22.1368,53.0146,14.7254,0.0143,7.3202,31.1222\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:39:29 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:39:29 d2.data.common]: Serializing the dataset using: \n","[09/03 16:39:29 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:39:29 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:39:29 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:39:29 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:39:35 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0136 s/iter. Inference: 0.1908 s/iter. Eval: 0.2827 s/iter. Total: 0.4871 s/iter. ETA=0:00:12\n","[09/03 16:39:40 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0089 s/iter. Inference: 0.1826 s/iter. Eval: 0.2266 s/iter. Total: 0.4184 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:39:45 d2.evaluation.evaluator]: Total inference time: 0:00:12.704047 (0.397001 s / iter per device, on 1 devices)\n","[09/03 16:39:45 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180194 s / iter per device, on 1 devices)\n","[09/03 16:39:45 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:39:45 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:39:45 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.592\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.192\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:39:45 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.188 | 59.196 | 20.195 | 0.445 | 11.967 | 36.091 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:39:45 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.530\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.073\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.172\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:39:45 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.137 | 53.015 | 14.725 | 0.014 | 7.320 | 31.122 |\n","[09/03 16:39:45 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: 26.1879,59.1964,20.1954,0.4451,11.9674,36.0912\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:39:45 d2.evaluation.testing]: copypaste: 22.1368,53.0146,14.7254,0.0143,7.3202,31.1222\n","Av. segm AP50 = 53.014594362353925\n","[09/03 16:39:47 d2.utils.events]: eta: 8 days, 3:16:35 iter: 999 total_loss: 1.562 loss_cls: 0.3625 loss_box_reg: 0.5877 loss_mask: 0.3972 loss_rpn_cls: 0.08916 loss_rpn_loc: 0.102 validation_loss: 1.653 time: 1.3696 last_time: 1.1970 data_time: 0.0311 last_data_time: 0.1097 lr: 0.0003 max_mem: 7833M\n","[09/03 16:40:14 d2.utils.events]: eta: 8 days, 3:16:07 iter: 1019 total_loss: 1.603 loss_cls: 0.3536 loss_box_reg: 0.5891 loss_mask: 0.4002 loss_rpn_cls: 0.09027 loss_rpn_loc: 0.1275 validation_loss: 1.653 time: 1.3690 last_time: 1.2721 data_time: 0.0295 last_data_time: 0.0225 lr: 0.0003 max_mem: 7833M\n","[09/03 16:40:42 d2.utils.events]: eta: 8 days, 3:25:13 iter: 1039 total_loss: 1.521 loss_cls: 0.3427 loss_box_reg: 0.5874 loss_mask: 0.3917 loss_rpn_cls: 0.08033 loss_rpn_loc: 0.07789 validation_loss: 1.653 time: 1.3691 last_time: 1.1562 data_time: 0.0265 last_data_time: 0.0337 lr: 0.0003 max_mem: 7833M\n","[09/03 16:40:55 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:40:56 d2.data.common]: Serializing the dataset using: \n","[09/03 16:40:56 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:40:56 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:40:56 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:40:56 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:41:00 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1757 s/iter. Eval: 0.1765 s/iter. Total: 0.3568 s/iter. ETA=0:00:09\n","[09/03 16:41:05 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0105 s/iter. Inference: 0.1793 s/iter. Eval: 0.2058 s/iter. Total: 0.3957 s/iter. ETA=0:00:05\n","[09/03 16:41:11 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0107 s/iter. Inference: 0.1801 s/iter. Eval: 0.2117 s/iter. Total: 0.4028 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:41:11 d2.evaluation.evaluator]: Total inference time: 0:00:12.970796 (0.405337 s / iter per device, on 1 devices)\n","[09/03 16:41:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180107 s / iter per device, on 1 devices)\n","[09/03 16:41:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:41:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:41:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.592\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.105\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.182\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.474\n","[09/03 16:41:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.117 | 59.176 | 16.557 | 0.004 | 10.533 | 34.741 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:41:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.521\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.305\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.170\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404\n","[09/03 16:41:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.180 | 52.056 | 13.770 | 0.004 | 5.763 | 30.032 |\n","[09/03 16:41:11 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: 25.1173,59.1761,16.5567,0.0037,10.5334,34.7412\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:41:11 d2.evaluation.testing]: copypaste: 21.1805,52.0562,13.7703,0.0041,5.7630,30.0323\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:41:20 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:41:21 d2.data.common]: Serializing the dataset using: \n","[09/03 16:41:21 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:41:21 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:41:21 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:41:21 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:41:26 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1785 s/iter. Eval: 0.1724 s/iter. Total: 0.3557 s/iter. ETA=0:00:09\n","[09/03 16:41:31 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0058 s/iter. Inference: 0.1777 s/iter. Eval: 0.1764 s/iter. Total: 0.3601 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:41:36 d2.evaluation.evaluator]: Total inference time: 0:00:11.921801 (0.372556 s / iter per device, on 1 devices)\n","[09/03 16:41:36 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.177285 s / iter per device, on 1 devices)\n","[09/03 16:41:36 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:41:36 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:41:36 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:41:36 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:41:36 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:41:36 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:41:36 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.251\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.592\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.105\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.182\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.222\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.474\n","[09/03 16:41:36 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.117 | 59.176 | 16.557 | 0.004 | 10.533 | 34.741 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:41:37 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:41:37 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.23 seconds.\n","[09/03 16:41:37 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:41:37 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.521\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.300\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.305\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.170\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.404\n","[09/03 16:41:37 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.180 | 52.056 | 13.770 | 0.004 | 5.763 | 30.032 |\n","[09/03 16:41:37 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: 25.1173,59.1761,16.5567,0.0037,10.5334,34.7412\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:41:37 d2.evaluation.testing]: copypaste: 21.1805,52.0562,13.7703,0.0041,5.7630,30.0323\n","Av. segm AP50 = 52.05623494765278\n","[09/03 16:41:51 d2.utils.events]: eta: 8 days, 3:24:45 iter: 1059 total_loss: 1.533 loss_cls: 0.3462 loss_box_reg: 0.5806 loss_mask: 0.3855 loss_rpn_cls: 0.08694 loss_rpn_loc: 0.08542 validation_loss: 1.684 time: 1.3687 last_time: 1.3613 data_time: 0.0277 last_data_time: 0.0221 lr: 0.0003 max_mem: 7833M\n","[09/03 16:42:18 d2.utils.events]: eta: 8 days, 3:24:17 iter: 1079 total_loss: 1.628 loss_cls: 0.3754 loss_box_reg: 0.6027 loss_mask: 0.3973 loss_rpn_cls: 0.0881 loss_rpn_loc: 0.1029 validation_loss: 1.684 time: 1.3682 last_time: 1.1318 data_time: 0.0309 last_data_time: 0.0171 lr: 0.0003 max_mem: 7833M\n","[09/03 16:42:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:42:46 d2.data.common]: Serializing the dataset using: \n","[09/03 16:42:46 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:42:46 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:42:46 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:42:46 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:42:51 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0125 s/iter. Inference: 0.1794 s/iter. Eval: 0.2396 s/iter. Total: 0.4315 s/iter. ETA=0:00:11\n","[09/03 16:42:56 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0152 s/iter. Inference: 0.1829 s/iter. Eval: 0.2512 s/iter. Total: 0.4495 s/iter. ETA=0:00:06\n","[09/03 16:43:01 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0107 s/iter. Inference: 0.1779 s/iter. Eval: 0.2093 s/iter. Total: 0.3981 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:43:01 d2.evaluation.evaluator]: Total inference time: 0:00:12.815151 (0.400473 s / iter per device, on 1 devices)\n","[09/03 16:43:01 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.177889 s / iter per device, on 1 devices)\n","[09/03 16:43:01 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:43:01 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:43:01 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.187\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.207\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n","[09/03 16:43:01 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.554 | 56.803 | 17.095 | 0.028 | 9.700 | 34.193 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.12 seconds.\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:43:01 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.143\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.168\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.397\n","[09/03 16:43:01 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.031 | 50.468 | 14.307 | 0.010 | 6.093 | 29.943 |\n","[09/03 16:43:01 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: 24.5536,56.8030,17.0947,0.0280,9.6995,34.1926\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:43:01 d2.evaluation.testing]: copypaste: 21.0310,50.4684,14.3073,0.0095,6.0933,29.9431\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:43:11 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:43:12 d2.data.common]: Serializing the dataset using: \n","[09/03 16:43:12 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:43:12 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:43:12 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:43:12 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:43:16 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1759 s/iter. Eval: 0.1755 s/iter. Total: 0.3562 s/iter. ETA=0:00:09\n","[09/03 16:43:22 d2.evaluation.evaluator]: Inference done 26/37. Dataloading: 0.0056 s/iter. Inference: 0.1766 s/iter. Eval: 0.1758 s/iter. Total: 0.3582 s/iter. ETA=0:00:03\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:43:27 d2.evaluation.evaluator]: Total inference time: 0:00:12.565462 (0.392671 s / iter per device, on 1 devices)\n","[09/03 16:43:27 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.178680 s / iter per device, on 1 devices)\n","[09/03 16:43:27 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:43:27 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:43:27 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.171\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.342\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.187\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.207\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n","[09/03 16:43:27 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.554 | 56.803 | 17.095 | 0.028 | 9.700 | 34.193 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:43:27 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.143\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.299\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.168\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.165\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.397\n","[09/03 16:43:27 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.031 | 50.468 | 14.307 | 0.010 | 6.093 | 29.943 |\n","[09/03 16:43:27 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: 24.5536,56.8030,17.0947,0.0280,9.6995,34.1926\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:43:27 d2.evaluation.testing]: copypaste: 21.0310,50.4684,14.3073,0.0095,6.0933,29.9431\n","Av. segm AP50 = 50.468389280139036\n","[09/03 16:43:27 d2.utils.events]: eta: 8 days, 3:31:45 iter: 1099 total_loss: 1.627 loss_cls: 0.3655 loss_box_reg: 0.5638 loss_mask: 0.3765 loss_rpn_cls: 0.0917 loss_rpn_loc: 0.1479 validation_loss: 1.632 time: 1.3684 last_time: 1.6570 data_time: 0.0283 last_data_time: 0.0204 lr: 0.0003 max_mem: 7833M\n","[09/03 16:43:53 d2.utils.events]: eta: 8 days, 2:55:22 iter: 1119 total_loss: 1.559 loss_cls: 0.3107 loss_box_reg: 0.5686 loss_mask: 0.3709 loss_rpn_cls: 0.08462 loss_rpn_loc: 0.1227 validation_loss: 1.632 time: 1.3668 last_time: 0.9072 data_time: 0.0229 last_data_time: 0.0335 lr: 0.0003 max_mem: 7833M\n","[09/03 16:44:21 d2.utils.events]: eta: 8 days, 3:04:08 iter: 1139 total_loss: 1.528 loss_cls: 0.3525 loss_box_reg: 0.5889 loss_mask: 0.3891 loss_rpn_cls: 0.07803 loss_rpn_loc: 0.1019 validation_loss: 1.632 time: 1.3675 last_time: 1.7170 data_time: 0.0304 last_data_time: 0.0175 lr: 0.0003 max_mem: 7833M\n","[09/03 16:44:34 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:44:34 d2.data.common]: Serializing the dataset using: \n","[09/03 16:44:34 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:44:34 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:44:34 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:44:34 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:44:40 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0204 s/iter. Inference: 0.1955 s/iter. Eval: 0.3681 s/iter. Total: 0.5841 s/iter. ETA=0:00:15\n","[09/03 16:44:45 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0116 s/iter. Inference: 0.1897 s/iter. Eval: 0.3105 s/iter. Total: 0.5122 s/iter. ETA=0:00:07\n","[09/03 16:44:51 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0092 s/iter. Inference: 0.1843 s/iter. Eval: 0.2565 s/iter. Total: 0.4503 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:44:51 d2.evaluation.evaluator]: Total inference time: 0:00:14.411546 (0.450361 s / iter per device, on 1 devices)\n","[09/03 16:44:51 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184164 s / iter per device, on 1 devices)\n","[09/03 16:44:51 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:44:51 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:44:51 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.600\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.190\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:44:51 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.209 | 60.038 | 19.425 | 0.093 | 11.885 | 35.969 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:44:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.534\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.065\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.182\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412\n","[09/03 16:44:51 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.351 | 53.381 | 16.271 | 0.008 | 6.477 | 31.546 |\n","[09/03 16:44:51 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: 26.2090,60.0375,19.4248,0.0929,11.8851,35.9693\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:44:51 d2.evaluation.testing]: copypaste: 22.3509,53.3808,16.2706,0.0085,6.4775,31.5458\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:45:02 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:45:03 d2.data.common]: Serializing the dataset using: \n","[09/03 16:45:03 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:45:03 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:45:03 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:45:03 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:45:07 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1812 s/iter. Eval: 0.1982 s/iter. Total: 0.3841 s/iter. ETA=0:00:09\n","[09/03 16:45:12 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0073 s/iter. Inference: 0.1842 s/iter. Eval: 0.2210 s/iter. Total: 0.4127 s/iter. ETA=0:00:05\n","[09/03 16:45:17 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0095 s/iter. Inference: 0.1871 s/iter. Eval: 0.2325 s/iter. Total: 0.4293 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:45:19 d2.evaluation.evaluator]: Total inference time: 0:00:13.638404 (0.426200 s / iter per device, on 1 devices)\n","[09/03 16:45:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.186276 s / iter per device, on 1 devices)\n","[09/03 16:45:19 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:45:19 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:45:19 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.600\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.190\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n","[09/03 16:45:19 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.209 | 60.038 | 19.425 | 0.093 | 11.885 | 35.969 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:45:19 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.534\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.163\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.065\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.173\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.182\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412\n","[09/03 16:45:19 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.351 | 53.381 | 16.271 | 0.008 | 6.477 | 31.546 |\n","[09/03 16:45:19 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: 26.2090,60.0375,19.4248,0.0929,11.8851,35.9693\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:45:19 d2.evaluation.testing]: copypaste: 22.3509,53.3808,16.2706,0.0085,6.4775,31.5458\n","Av. segm AP50 = 53.38084161793859\n","[09/03 16:45:35 d2.utils.events]: eta: 8 days, 2:55:05 iter: 1159 total_loss: 1.576 loss_cls: 0.3458 loss_box_reg: 0.5677 loss_mask: 0.3889 loss_rpn_cls: 0.0814 loss_rpn_loc: 0.0943 validation_loss: 1.658 time: 1.3669 last_time: 1.3091 data_time: 0.0318 last_data_time: 0.0336 lr: 0.0003 max_mem: 7833M\n","[09/03 16:46:03 d2.utils.events]: eta: 8 days, 3:03:11 iter: 1179 total_loss: 1.519 loss_cls: 0.34 loss_box_reg: 0.5812 loss_mask: 0.3828 loss_rpn_cls: 0.07865 loss_rpn_loc: 0.09471 validation_loss: 1.658 time: 1.3672 last_time: 1.3011 data_time: 0.0276 last_data_time: 0.0266 lr: 0.0003 max_mem: 7833M\n","[09/03 16:46:34 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:46:35 d2.data.common]: Serializing the dataset using: \n","[09/03 16:46:35 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:46:35 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:46:35 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:46:35 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:46:39 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0050 s/iter. Inference: 0.1781 s/iter. Eval: 0.1899 s/iter. Total: 0.3730 s/iter. ETA=0:00:09\n","[09/03 16:46:44 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0074 s/iter. Inference: 0.1846 s/iter. Eval: 0.2462 s/iter. Total: 0.4385 s/iter. ETA=0:00:06\n","[09/03 16:46:49 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0078 s/iter. Inference: 0.1833 s/iter. Eval: 0.2391 s/iter. Total: 0.4304 s/iter. ETA=0:00:01\n","[09/03 16:46:50 d2.evaluation.evaluator]: Total inference time: 0:00:13.657590 (0.426800 s / iter per device, on 1 devices)\n","[09/03 16:46:50 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182601 s / iter per device, on 1 devices)\n","[09/03 16:46:51 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:46:51 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:46:51 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.265\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.594\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[09/03 16:46:51 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.537 | 59.367 | 19.840 | 0.210 | 11.675 | 36.796 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:46:51 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.523\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.073\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415\n","[09/03 16:46:51 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.141 | 52.264 | 16.849 | 0.007 | 7.336 | 31.133 |\n","[09/03 16:46:51 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: 26.5375,59.3673,19.8401,0.2100,11.6745,36.7955\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:46:51 d2.evaluation.testing]: copypaste: 22.1406,52.2645,16.8487,0.0069,7.3364,31.1330\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:47:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:47:01 d2.data.common]: Serializing the dataset using: \n","[09/03 16:47:01 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:47:01 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:47:01 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:47:01 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:47:06 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0050 s/iter. Inference: 0.1785 s/iter. Eval: 0.1929 s/iter. Total: 0.3764 s/iter. ETA=0:00:09\n","[09/03 16:47:12 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0058 s/iter. Inference: 0.1793 s/iter. Eval: 0.1958 s/iter. Total: 0.3811 s/iter. ETA=0:00:04\n","[09/03 16:47:17 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0078 s/iter. Inference: 0.1810 s/iter. Eval: 0.2176 s/iter. Total: 0.4066 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:47:17 d2.evaluation.evaluator]: Total inference time: 0:00:13.139010 (0.410594 s / iter per device, on 1 devices)\n","[09/03 16:47:17 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180977 s / iter per device, on 1 devices)\n","[09/03 16:47:17 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:47:17 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:47:17 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:47:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:47:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:47:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:47:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.265\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.594\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.198\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.368\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.214\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486\n","[09/03 16:47:17 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.537 | 59.367 | 19.840 | 0.210 | 11.675 | 36.796 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:47:18 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:47:18 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.18 seconds.\n","[09/03 16:47:18 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:47:18 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.221\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.523\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.168\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.073\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.311\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.415\n","[09/03 16:47:18 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.141 | 52.264 | 16.849 | 0.007 | 7.336 | 31.133 |\n","[09/03 16:47:18 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: 26.5375,59.3673,19.8401,0.2100,11.6745,36.7955\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:47:18 d2.evaluation.testing]: copypaste: 22.1406,52.2645,16.8487,0.0069,7.3364,31.1330\n","Av. segm AP50 = 52.26445953459472\n","[09/03 16:47:18 d2.utils.events]: eta: 8 days, 4:21:26 iter: 1199 total_loss: 1.49 loss_cls: 0.3306 loss_box_reg: 0.5603 loss_mask: 0.3813 loss_rpn_cls: 0.07334 loss_rpn_loc: 0.08911 validation_loss: 1.632 time: 1.3701 last_time: 1.5850 data_time: 0.0316 last_data_time: 0.0209 lr: 0.0003 max_mem: 7833M\n","[09/03 16:47:46 d2.utils.events]: eta: 8 days, 5:21:50 iter: 1219 total_loss: 1.598 loss_cls: 0.3545 loss_box_reg: 0.5831 loss_mask: 0.3782 loss_rpn_cls: 0.08559 loss_rpn_loc: 0.1006 validation_loss: 1.632 time: 1.3710 last_time: 1.9020 data_time: 0.0321 last_data_time: 0.0826 lr: 0.0003 max_mem: 7833M\n","[09/03 16:48:13 d2.utils.events]: eta: 8 days, 5:21:22 iter: 1239 total_loss: 1.59 loss_cls: 0.3492 loss_box_reg: 0.562 loss_mask: 0.395 loss_rpn_cls: 0.08785 loss_rpn_loc: 0.0994 validation_loss: 1.632 time: 1.3700 last_time: 1.1257 data_time: 0.0329 last_data_time: 0.0121 lr: 0.0003 max_mem: 7834M\n","[09/03 16:48:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:48:26 d2.data.common]: Serializing the dataset using: \n","[09/03 16:48:26 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:48:26 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:48:26 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:48:26 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:48:32 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0125 s/iter. Inference: 0.1881 s/iter. Eval: 0.2986 s/iter. Total: 0.4992 s/iter. ETA=0:00:12\n","[09/03 16:48:37 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0109 s/iter. Inference: 0.1865 s/iter. Eval: 0.2599 s/iter. Total: 0.4576 s/iter. ETA=0:00:06\n","[09/03 16:48:42 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0090 s/iter. Inference: 0.1840 s/iter. Eval: 0.2406 s/iter. Total: 0.4339 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:48:43 d2.evaluation.evaluator]: Total inference time: 0:00:13.921822 (0.435057 s / iter per device, on 1 devices)\n","[09/03 16:48:43 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183870 s / iter per device, on 1 devices)\n","[09/03 16:48:43 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:48:43 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:48:43 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.591\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.213\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.380\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485\n","[09/03 16:48:43 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.151 | 59.114 | 21.282 | 0.114 | 11.738 | 37.148 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:48:43 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.541\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.175\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.081\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.178\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.204\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417\n","[09/03 16:48:43 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.916 | 54.059 | 17.516 | 0.000 | 8.085 | 32.060 |\n","[09/03 16:48:43 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: 27.1507,59.1137,21.2825,0.1145,11.7382,37.1483\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:48:43 d2.evaluation.testing]: copypaste: 22.9156,54.0586,17.5164,0.0000,8.0848,32.0600\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:48:54 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:48:54 d2.data.common]: Serializing the dataset using: \n","[09/03 16:48:54 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:48:54 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:48:54 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:48:54 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:48:59 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0049 s/iter. Inference: 0.1814 s/iter. Eval: 0.2014 s/iter. Total: 0.3877 s/iter. ETA=0:00:10\n","[09/03 16:49:05 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0090 s/iter. Inference: 0.1864 s/iter. Eval: 0.2354 s/iter. Total: 0.4310 s/iter. ETA=0:00:06\n","[09/03 16:49:10 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0104 s/iter. Inference: 0.1855 s/iter. Eval: 0.2395 s/iter. Total: 0.4356 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:49:11 d2.evaluation.evaluator]: Total inference time: 0:00:13.931112 (0.435347 s / iter per device, on 1 devices)\n","[09/03 16:49:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185200 s / iter per device, on 1 devices)\n","[09/03 16:49:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:49:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:49:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.591\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.213\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.380\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.255\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485\n","[09/03 16:49:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.151 | 59.114 | 21.282 | 0.114 | 11.738 | 37.148 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:49:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.229\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.541\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.175\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.081\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.178\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.322\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.204\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.417\n","[09/03 16:49:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.916 | 54.059 | 17.516 | 0.000 | 8.085 | 32.060 |\n","[09/03 16:49:11 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: 27.1507,59.1137,21.2825,0.1145,11.7382,37.1483\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:49:11 d2.evaluation.testing]: copypaste: 22.9156,54.0586,17.5164,0.0000,8.0848,32.0600\n","Av. segm AP50 = 54.05863094767708\n","[09/03 16:49:30 d2.utils.events]: eta: 8 days, 5:20:53 iter: 1259 total_loss: 1.574 loss_cls: 0.3572 loss_box_reg: 0.5501 loss_mask: 0.3686 loss_rpn_cls: 0.08865 loss_rpn_loc: 0.1714 validation_loss: 1.634 time: 1.3714 last_time: 1.7483 data_time: 0.0274 last_data_time: 0.0231 lr: 0.0003 max_mem: 7834M\n","[09/03 16:49:58 d2.utils.events]: eta: 8 days, 5:36:07 iter: 1279 total_loss: 1.458 loss_cls: 0.3481 loss_box_reg: 0.5666 loss_mask: 0.3889 loss_rpn_cls: 0.07469 loss_rpn_loc: 0.06694 validation_loss: 1.634 time: 1.3717 last_time: 1.7999 data_time: 0.0321 last_data_time: 0.0439 lr: 0.0003 max_mem: 7834M\n","[09/03 16:50:24 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:50:24 d2.data.common]: Serializing the dataset using: \n","[09/03 16:50:24 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:50:24 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:50:24 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:50:24 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:50:29 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1810 s/iter. Eval: 0.1950 s/iter. Total: 0.3808 s/iter. ETA=0:00:09\n","[09/03 16:50:34 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0064 s/iter. Inference: 0.1836 s/iter. Eval: 0.2214 s/iter. Total: 0.4116 s/iter. ETA=0:00:05\n","[09/03 16:50:39 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0077 s/iter. Inference: 0.1844 s/iter. Eval: 0.2299 s/iter. Total: 0.4222 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:50:40 d2.evaluation.evaluator]: Total inference time: 0:00:13.492390 (0.421637 s / iter per device, on 1 devices)\n","[09/03 16:50:40 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184169 s / iter per device, on 1 devices)\n","[09/03 16:50:40 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:50:40 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:50:40 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.258\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.591\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.121\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.186\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478\n","[09/03 16:50:40 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.788 | 59.058 | 18.856 | 0.809 | 12.113 | 35.523 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:50:40 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:50:41 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.525\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.070\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.411\n","[09/03 16:50:41 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.872 | 52.461 | 15.614 | 0.004 | 7.004 | 30.658 |\n","[09/03 16:50:41 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: 25.7878,59.0580,18.8561,0.8088,12.1133,35.5231\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:50:41 d2.evaluation.testing]: copypaste: 21.8716,52.4610,15.6143,0.0045,7.0042,30.6579\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:50:51 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:50:52 d2.data.common]: Serializing the dataset using: \n","[09/03 16:50:52 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:50:52 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:50:52 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:50:52 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:50:57 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1804 s/iter. Eval: 0.1992 s/iter. Total: 0.3844 s/iter. ETA=0:00:09\n","[09/03 16:51:02 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0057 s/iter. Inference: 0.1805 s/iter. Eval: 0.1992 s/iter. Total: 0.3856 s/iter. ETA=0:00:05\n","[09/03 16:51:07 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0089 s/iter. Inference: 0.1833 s/iter. Eval: 0.2311 s/iter. Total: 0.4235 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:51:08 d2.evaluation.evaluator]: Total inference time: 0:00:13.836921 (0.432404 s / iter per device, on 1 devices)\n","[09/03 16:51:08 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183350 s / iter per device, on 1 devices)\n","[09/03 16:51:08 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:51:08 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:51:08 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:51:08 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:51:08 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:51:08 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:51:08 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.258\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.591\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.189\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.121\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.355\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.186\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478\n","[09/03 16:51:08 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.788 | 59.058 | 18.856 | 0.809 | 12.113 | 35.523 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:51:09 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:51:09 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:51:09 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:51:09 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.525\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.156\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.070\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.307\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.167\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.411\n","[09/03 16:51:09 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.872 | 52.461 | 15.614 | 0.004 | 7.004 | 30.658 |\n","[09/03 16:51:09 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: 25.7878,59.0580,18.8561,0.8088,12.1133,35.5231\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:51:09 d2.evaluation.testing]: copypaste: 21.8716,52.4610,15.6143,0.0045,7.0042,30.6579\n","Av. segm AP50 = 52.46096060842943\n","[09/03 16:51:09 d2.utils.events]: eta: 8 days, 5:26:48 iter: 1299 total_loss: 1.552 loss_cls: 0.3395 loss_box_reg: 0.5838 loss_mask: 0.3842 loss_rpn_cls: 0.08395 loss_rpn_loc: 0.1398 validation_loss: 1.659 time: 1.3703 last_time: 1.2218 data_time: 0.0285 last_data_time: 0.0185 lr: 0.0003 max_mem: 7834M\n","[09/03 16:51:34 d2.utils.events]: eta: 8 days, 5:26:20 iter: 1319 total_loss: 1.578 loss_cls: 0.3361 loss_box_reg: 0.5667 loss_mask: 0.3814 loss_rpn_cls: 0.07998 loss_rpn_loc: 0.1318 validation_loss: 1.659 time: 1.3688 last_time: 1.2106 data_time: 0.0308 last_data_time: 0.0290 lr: 0.0003 max_mem: 7834M\n","[09/03 16:51:59 d2.utils.events]: eta: 8 days, 4:07:36 iter: 1339 total_loss: 1.541 loss_cls: 0.3507 loss_box_reg: 0.5819 loss_mask: 0.3835 loss_rpn_cls: 0.0809 loss_rpn_loc: 0.09548 validation_loss: 1.659 time: 1.3671 last_time: 1.4947 data_time: 0.0278 last_data_time: 0.0313 lr: 0.0003 max_mem: 7834M\n","[09/03 16:52:13 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:52:13 d2.data.common]: Serializing the dataset using: \n","[09/03 16:52:13 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:52:13 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:52:13 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:52:13 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:52:18 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0061 s/iter. Inference: 0.1823 s/iter. Eval: 0.2120 s/iter. Total: 0.4004 s/iter. ETA=0:00:10\n","[09/03 16:52:23 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0119 s/iter. Inference: 0.1864 s/iter. Eval: 0.2597 s/iter. Total: 0.4585 s/iter. ETA=0:00:06\n","[09/03 16:52:29 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0091 s/iter. Inference: 0.1840 s/iter. Eval: 0.2299 s/iter. Total: 0.4235 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:52:29 d2.evaluation.evaluator]: Total inference time: 0:00:13.575793 (0.424244 s / iter per device, on 1 devices)\n","[09/03 16:52:29 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183872 s / iter per device, on 1 devices)\n","[09/03 16:52:29 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:52:29 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:52:29 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:52:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:52:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:52:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:52:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.611\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.379\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.234\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 16:52:29 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.457 | 61.097 | 21.057 | 0.355 | 12.502 | 37.793 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:52:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:52:30 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 16:52:30 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:52:30 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.230\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.084\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.176\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420\n","[09/03 16:52:30 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.016 | 53.777 | 17.186 | 0.004 | 8.365 | 32.075 |\n","[09/03 16:52:30 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: 27.4565,61.0970,21.0574,0.3554,12.5025,37.7926\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:52:30 d2.evaluation.testing]: copypaste: 23.0156,53.7771,17.1863,0.0041,8.3654,32.0748\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:52:40 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:52:41 d2.data.common]: Serializing the dataset using: \n","[09/03 16:52:41 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:52:41 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:52:41 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:52:41 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:52:46 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0051 s/iter. Inference: 0.1815 s/iter. Eval: 0.1973 s/iter. Total: 0.3840 s/iter. ETA=0:00:09\n","[09/03 16:52:51 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0073 s/iter. Inference: 0.1818 s/iter. Eval: 0.2146 s/iter. Total: 0.4040 s/iter. ETA=0:00:05\n","[09/03 16:52:57 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0090 s/iter. Inference: 0.1854 s/iter. Eval: 0.2403 s/iter. Total: 0.4350 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:52:58 d2.evaluation.evaluator]: Total inference time: 0:00:13.900962 (0.434405 s / iter per device, on 1 devices)\n","[09/03 16:52:58 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185006 s / iter per device, on 1 devices)\n","[09/03 16:52:58 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:52:58 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:52:58 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.611\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.379\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.234\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 16:52:58 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.457 | 61.097 | 21.057 | 0.355 | 12.502 | 37.793 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:52:58 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.230\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.538\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.172\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.084\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.321\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.176\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.323\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420\n","[09/03 16:52:58 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.016 | 53.777 | 17.186 | 0.004 | 8.365 | 32.075 |\n","[09/03 16:52:58 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: 27.4565,61.0970,21.0574,0.3554,12.5025,37.7926\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:52:58 d2.evaluation.testing]: copypaste: 23.0156,53.7771,17.1863,0.0041,8.3654,32.0748\n","Av. segm AP50 = 53.77712404747186\n","[09/03 16:53:12 d2.utils.events]: eta: 8 days, 4:07:08 iter: 1359 total_loss: 1.554 loss_cls: 0.3314 loss_box_reg: 0.5326 loss_mask: 0.3789 loss_rpn_cls: 0.09437 loss_rpn_loc: 0.1348 validation_loss: 1.63 time: 1.3666 last_time: 1.0271 data_time: 0.0329 last_data_time: 0.0131 lr: 0.0003 max_mem: 7834M\n","[09/03 16:53:39 d2.utils.events]: eta: 8 days, 3:49:07 iter: 1379 total_loss: 1.443 loss_cls: 0.3369 loss_box_reg: 0.5591 loss_mask: 0.3662 loss_rpn_cls: 0.07234 loss_rpn_loc: 0.07069 validation_loss: 1.63 time: 1.3665 last_time: 1.5525 data_time: 0.0265 last_data_time: 0.0359 lr: 0.0003 max_mem: 7834M\n","[09/03 16:54:06 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:54:07 d2.data.common]: Serializing the dataset using: \n","[09/03 16:54:07 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:54:07 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:54:07 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:54:07 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:54:13 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0188 s/iter. Inference: 0.1912 s/iter. Eval: 0.2534 s/iter. Total: 0.4634 s/iter. ETA=0:00:12\n","[09/03 16:54:18 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0097 s/iter. Inference: 0.1828 s/iter. Eval: 0.2106 s/iter. Total: 0.4033 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:54:23 d2.evaluation.evaluator]: Total inference time: 0:00:12.822611 (0.400707 s / iter per device, on 1 devices)\n","[09/03 16:54:23 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181192 s / iter per device, on 1 devices)\n","[09/03 16:54:23 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:54:23 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:54:23 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:54:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:54:23 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[09/03 16:54:23 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:54:23 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461\n","[09/03 16:54:23 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.313 | 56.992 | 16.649 | 0.067 | 12.028 | 33.283 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 16:54:23 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:54:24 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.22 seconds.\n","[09/03 16:54:24 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:54:24 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.519\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.148\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.082\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:54:24 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.215 | 51.932 | 14.786 | 0.000 | 8.230 | 29.523 |\n","[09/03 16:54:24 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: 24.3128,56.9925,16.6489,0.0669,12.0285,33.2831\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:54:24 d2.evaluation.testing]: copypaste: 21.2148,51.9318,14.7857,0.0000,8.2297,29.5233\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:54:34 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:54:35 d2.data.common]: Serializing the dataset using: \n","[09/03 16:54:35 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:54:35 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:54:35 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:54:35 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:54:39 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1811 s/iter. Eval: 0.1934 s/iter. Total: 0.3792 s/iter. ETA=0:00:09\n","[09/03 16:54:45 d2.evaluation.evaluator]: Inference done 20/37. Dataloading: 0.0176 s/iter. Inference: 0.1927 s/iter. Eval: 0.2976 s/iter. Total: 0.5082 s/iter. ETA=0:00:08\n","[09/03 16:54:50 d2.evaluation.evaluator]: Inference done 33/37. Dataloading: 0.0138 s/iter. Inference: 0.1889 s/iter. Eval: 0.2574 s/iter. Total: 0.4603 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:54:51 d2.evaluation.evaluator]: Total inference time: 0:00:14.494136 (0.452942 s / iter per device, on 1 devices)\n","[09/03 16:54:51 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:06 (0.187804 s / iter per device, on 1 devices)\n","[09/03 16:54:52 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:54:52 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:54:52 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.184\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461\n","[09/03 16:54:52 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 24.313 | 56.992 | 16.649 | 0.067 | 12.028 | 33.283 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:54:52 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.212\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.519\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.148\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.082\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.021\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.169\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407\n","[09/03 16:54:52 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 21.215 | 51.932 | 14.786 | 0.000 | 8.230 | 29.523 |\n","[09/03 16:54:52 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: 24.3128,56.9925,16.6489,0.0669,12.0285,33.2831\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:54:52 d2.evaluation.testing]: copypaste: 21.2148,51.9318,14.7857,0.0000,8.2297,29.5233\n","Av. segm AP50 = 51.93179176017267\n","[09/03 16:54:52 d2.utils.events]: eta: 8 days, 3:48:38 iter: 1399 total_loss: 1.65 loss_cls: 0.3477 loss_box_reg: 0.5729 loss_mask: 0.3858 loss_rpn_cls: 0.08232 loss_rpn_loc: 0.1473 validation_loss: 1.627 time: 1.3662 last_time: 1.4030 data_time: 0.0255 last_data_time: 0.0254 lr: 0.0003 max_mem: 7834M\n","[09/03 16:55:19 d2.utils.events]: eta: 8 days, 3:30:33 iter: 1419 total_loss: 1.377 loss_cls: 0.3241 loss_box_reg: 0.533 loss_mask: 0.3689 loss_rpn_cls: 0.08345 loss_rpn_loc: 0.08296 validation_loss: 1.627 time: 1.3662 last_time: 1.5991 data_time: 0.0311 last_data_time: 0.0137 lr: 0.0003 max_mem: 7834M\n","[09/03 16:55:48 d2.utils.events]: eta: 8 days, 3:47:42 iter: 1439 total_loss: 1.496 loss_cls: 0.3272 loss_box_reg: 0.5681 loss_mask: 0.3824 loss_rpn_cls: 0.07791 loss_rpn_loc: 0.09801 validation_loss: 1.627 time: 1.3670 last_time: 1.1652 data_time: 0.0283 last_data_time: 0.0212 lr: 0.0003 max_mem: 7835M\n","[09/03 16:56:03 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:56:04 d2.data.common]: Serializing the dataset using: \n","[09/03 16:56:04 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:56:04 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:56:04 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:56:04 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:56:08 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1812 s/iter. Eval: 0.1961 s/iter. Total: 0.3820 s/iter. ETA=0:00:09\n","[09/03 16:56:13 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0137 s/iter. Inference: 0.1841 s/iter. Eval: 0.2337 s/iter. Total: 0.4317 s/iter. ETA=0:00:06\n","[09/03 16:56:18 d2.evaluation.evaluator]: Inference done 33/37. Dataloading: 0.0158 s/iter. Inference: 0.1856 s/iter. Eval: 0.2396 s/iter. Total: 0.4414 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:56:20 d2.evaluation.evaluator]: Total inference time: 0:00:13.896742 (0.434273 s / iter per device, on 1 devices)\n","[09/03 16:56:20 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184328 s / iter per device, on 1 devices)\n","[09/03 16:56:20 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:56:20 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:56:20 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.593\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.114\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.188\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.243\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468\n","[09/03 16:56:20 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.360 | 59.260 | 18.024 | 0.009 | 11.351 | 34.733 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:56:20 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.523\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.074\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.174\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.414\n","[09/03 16:56:20 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.551 | 52.316 | 16.200 | 0.002 | 7.406 | 31.708 |\n","[09/03 16:56:20 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: 25.3598,59.2605,18.0235,0.0085,11.3511,34.7334\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:56:20 d2.evaluation.testing]: copypaste: 22.5507,52.3158,16.1997,0.0024,7.4062,31.7082\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:56:30 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:56:31 d2.data.common]: Serializing the dataset using: \n","[09/03 16:56:31 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:56:31 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:56:31 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:56:31 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:56:36 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1815 s/iter. Eval: 0.2011 s/iter. Total: 0.3874 s/iter. ETA=0:00:10\n","[09/03 16:56:41 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0060 s/iter. Inference: 0.1810 s/iter. Eval: 0.1953 s/iter. Total: 0.3824 s/iter. ETA=0:00:04\n","[09/03 16:56:46 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0074 s/iter. Inference: 0.1852 s/iter. Eval: 0.2525 s/iter. Total: 0.4453 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:56:48 d2.evaluation.evaluator]: Total inference time: 0:00:14.478821 (0.452463 s / iter per device, on 1 devices)\n","[09/03 16:56:48 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.184637 s / iter per device, on 1 devices)\n","[09/03 16:56:48 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:56:48 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:56:48 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.593\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.114\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.188\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.243\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468\n","[09/03 16:56:48 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 25.360 | 59.260 | 18.024 | 0.009 | 11.351 | 34.733 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:56:48 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.226\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.523\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.162\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.074\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.317\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.174\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.414\n","[09/03 16:56:48 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 22.551 | 52.316 | 16.200 | 0.002 | 7.406 | 31.708 |\n","[09/03 16:56:48 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: 25.3598,59.2605,18.0235,0.0085,11.3511,34.7334\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:56:48 d2.evaluation.testing]: copypaste: 22.5507,52.3158,16.1997,0.0024,7.4062,31.7082\n","Av. segm AP50 = 52.315775543753\n","[09/03 16:57:02 d2.utils.events]: eta: 8 days, 5:23:00 iter: 1459 total_loss: 1.464 loss_cls: 0.325 loss_box_reg: 0.5508 loss_mask: 0.3609 loss_rpn_cls: 0.07599 loss_rpn_loc: 0.1019 validation_loss: 1.606 time: 1.3682 last_time: 1.1438 data_time: 0.0393 last_data_time: 0.0902 lr: 0.0003 max_mem: 7835M\n","[09/03 16:57:28 d2.utils.events]: eta: 8 days, 5:45:37 iter: 1479 total_loss: 1.486 loss_cls: 0.3267 loss_box_reg: 0.5666 loss_mask: 0.3891 loss_rpn_cls: 0.07332 loss_rpn_loc: 0.05615 validation_loss: 1.606 time: 1.3673 last_time: 0.7938 data_time: 0.0278 last_data_time: 0.0347 lr: 0.0003 max_mem: 7835M\n","[09/03 16:57:54 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:57:55 d2.data.common]: Serializing the dataset using: \n","[09/03 16:57:55 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:57:55 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:57:55 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:57:55 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:58:00 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0118 s/iter. Inference: 0.1900 s/iter. Eval: 0.3036 s/iter. Total: 0.5054 s/iter. ETA=0:00:13\n","[09/03 16:58:06 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0127 s/iter. Inference: 0.1872 s/iter. Eval: 0.2653 s/iter. Total: 0.4657 s/iter. ETA=0:00:06\n","[09/03 16:58:11 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0094 s/iter. Inference: 0.1832 s/iter. Eval: 0.2333 s/iter. Total: 0.4263 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:58:11 d2.evaluation.evaluator]: Total inference time: 0:00:13.719508 (0.428735 s / iter per device, on 1 devices)\n","[09/03 16:58:11 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183235 s / iter per device, on 1 devices)\n","[09/03 16:58:11 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:58:11 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:58:11 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.621\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.206\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.133\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481\n","[09/03 16:58:11 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.873 | 62.087 | 20.631 | 0.081 | 13.282 | 37.818 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:58:11 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.558\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n","[09/03 16:58:11 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.695 | 55.760 | 16.855 | 0.001 | 8.553 | 32.742 |\n","[09/03 16:58:11 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: 27.8732,62.0874,20.6307,0.0814,13.2820,37.8180\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:58:11 d2.evaluation.testing]: copypaste: 23.6952,55.7605,16.8551,0.0006,8.5533,32.7416\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:58:22 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:58:22 d2.data.common]: Serializing the dataset using: \n","[09/03 16:58:22 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:58:22 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:58:22 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:58:22 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:58:27 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1810 s/iter. Eval: 0.1937 s/iter. Total: 0.3794 s/iter. ETA=0:00:09\n","[09/03 16:58:32 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0082 s/iter. Inference: 0.1827 s/iter. Eval: 0.2185 s/iter. Total: 0.4096 s/iter. ETA=0:00:05\n","[09/03 16:58:37 d2.evaluation.evaluator]: Inference done 33/37. Dataloading: 0.0127 s/iter. Inference: 0.1861 s/iter. Eval: 0.2433 s/iter. Total: 0.4423 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:58:38 d2.evaluation.evaluator]: Total inference time: 0:00:13.983945 (0.436998 s / iter per device, on 1 devices)\n","[09/03 16:58:38 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185270 s / iter per device, on 1 devices)\n","[09/03 16:58:39 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 16:58:39 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 16:58:39 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.621\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.206\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.133\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.378\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481\n","[09/03 16:58:39 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.873 | 62.087 | 20.631 | 0.081 | 13.282 | 37.818 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 16:58:39 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.237\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.558\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.169\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.216\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.419\n","[09/03 16:58:39 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.695 | 55.760 | 16.855 | 0.001 | 8.553 | 32.742 |\n","[09/03 16:58:39 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: 27.8732,62.0874,20.6307,0.0814,13.2820,37.8180\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 16:58:39 d2.evaluation.testing]: copypaste: 23.6952,55.7605,16.8551,0.0006,8.5533,32.7416\n","Av. segm AP50 = 55.76049961526432\n","[09/03 16:58:41 d2.utils.events]: eta: 8 days, 4:48:49 iter: 1499 total_loss: 1.506 loss_cls: 0.3319 loss_box_reg: 0.5557 loss_mask: 0.3624 loss_rpn_cls: 0.09076 loss_rpn_loc: 0.1586 validation_loss: 1.583 time: 1.3662 last_time: 1.2498 data_time: 0.0285 last_data_time: 0.0192 lr: 0.0003 max_mem: 7835M\n","[09/03 16:59:11 d2.utils.events]: eta: 8 days, 6:20:39 iter: 1519 total_loss: 1.515 loss_cls: 0.3167 loss_box_reg: 0.554 loss_mask: 0.3833 loss_rpn_cls: 0.07608 loss_rpn_loc: 0.08302 validation_loss: 1.583 time: 1.3679 last_time: 0.8397 data_time: 0.0305 last_data_time: 0.0253 lr: 0.0003 max_mem: 7835M\n","[09/03 16:59:36 d2.utils.events]: eta: 8 days, 5:29:56 iter: 1539 total_loss: 1.51 loss_cls: 0.3302 loss_box_reg: 0.5616 loss_mask: 0.3741 loss_rpn_cls: 0.0775 loss_rpn_loc: 0.1686 validation_loss: 1.583 time: 1.3664 last_time: 1.4616 data_time: 0.0275 last_data_time: 0.0461 lr: 0.0003 max_mem: 7835M\n","[09/03 16:59:48 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 16:59:49 d2.data.common]: Serializing the dataset using: \n","[09/03 16:59:49 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 16:59:49 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 16:59:49 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 16:59:49 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 16:59:54 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0045 s/iter. Inference: 0.1812 s/iter. Eval: 0.1951 s/iter. Total: 0.3808 s/iter. ETA=0:00:09\n","[09/03 17:00:00 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0054 s/iter. Inference: 0.1804 s/iter. Eval: 0.1960 s/iter. Total: 0.3820 s/iter. ETA=0:00:04\n","[09/03 17:00:05 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0061 s/iter. Inference: 0.1816 s/iter. Eval: 0.2210 s/iter. Total: 0.4089 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:00:05 d2.evaluation.evaluator]: Total inference time: 0:00:13.274231 (0.414820 s / iter per device, on 1 devices)\n","[09/03 17:00:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181583 s / iter per device, on 1 devices)\n","[09/03 17:00:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:00:05 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:00:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:00:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.609\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.132\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.495\n","[09/03 17:00:06 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.186 | 60.933 | 19.391 | 0.016 | 13.221 | 37.246 |\n","Loading and preparing results...\n","DONE (t=0.08s)\n","creating index...\n","index created!\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.21 seconds.\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:00:06 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.555\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.165\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.323\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n","[09/03 17:00:06 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.297 | 55.453 | 16.492 | 0.003 | 8.912 | 32.331 |\n","[09/03 17:00:06 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: 27.1858,60.9334,19.3910,0.0156,13.2209,37.2465\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:00:06 d2.evaluation.testing]: copypaste: 23.2969,55.4534,16.4922,0.0029,8.9123,32.3313\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:00:15 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:00:15 d2.data.common]: Serializing the dataset using: \n","[09/03 17:00:15 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:00:15 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:00:15 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:00:16 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:00:21 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0205 s/iter. Inference: 0.1937 s/iter. Eval: 0.3126 s/iter. Total: 0.5268 s/iter. ETA=0:00:13\n","[09/03 17:00:27 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0118 s/iter. Inference: 0.1866 s/iter. Eval: 0.2402 s/iter. Total: 0.4389 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:00:32 d2.evaluation.evaluator]: Total inference time: 0:00:13.411853 (0.419120 s / iter per device, on 1 devices)\n","[09/03 17:00:32 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183901 s / iter per device, on 1 devices)\n","[09/03 17:00:32 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:00:32 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:00:32 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.609\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.194\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.132\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.197\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.237\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.495\n","[09/03 17:00:32 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.186 | 60.933 | 19.391 | 0.016 | 13.221 | 37.246 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:00:32 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.233\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.555\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.165\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.323\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.199\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n","[09/03 17:00:32 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.297 | 55.453 | 16.492 | 0.003 | 8.912 | 32.331 |\n","[09/03 17:00:32 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: 27.1858,60.9334,19.3910,0.0156,13.2209,37.2465\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:00:32 d2.evaluation.testing]: copypaste: 23.2969,55.4534,16.4922,0.0029,8.9123,32.3313\n","Av. segm AP50 = 55.45339515018338\n","[09/03 17:00:44 d2.utils.events]: eta: 8 days, 4:17:48 iter: 1559 total_loss: 1.477 loss_cls: 0.3175 loss_box_reg: 0.5499 loss_mask: 0.3611 loss_rpn_cls: 0.08421 loss_rpn_loc: 0.0757 validation_loss: 1.601 time: 1.3647 last_time: 1.3567 data_time: 0.0298 last_data_time: 0.0179 lr: 0.0003 max_mem: 7835M\n","[09/03 17:01:12 d2.utils.events]: eta: 8 days, 3:09:21 iter: 1579 total_loss: 1.476 loss_cls: 0.3241 loss_box_reg: 0.5508 loss_mask: 0.3782 loss_rpn_cls: 0.06827 loss_rpn_loc: 0.08298 validation_loss: 1.601 time: 1.3647 last_time: 1.2189 data_time: 0.0353 last_data_time: 0.0211 lr: 0.0003 max_mem: 7835M\n","[09/03 17:01:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:01:39 d2.data.common]: Serializing the dataset using: \n","[09/03 17:01:39 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:01:39 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:01:39 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:01:39 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:01:44 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0050 s/iter. Inference: 0.1812 s/iter. Eval: 0.1958 s/iter. Total: 0.3821 s/iter. ETA=0:00:09\n","[09/03 17:01:49 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0102 s/iter. Inference: 0.1803 s/iter. Eval: 0.2112 s/iter. Total: 0.4019 s/iter. ETA=0:00:05\n","[09/03 17:01:54 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0115 s/iter. Inference: 0.1830 s/iter. Eval: 0.2291 s/iter. Total: 0.4238 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:01:55 d2.evaluation.evaluator]: Total inference time: 0:00:13.560020 (0.423751 s / iter per device, on 1 devices)\n","[09/03 17:01:55 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182766 s / iter per device, on 1 devices)\n","[09/03 17:01:55 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:01:55 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:01:55 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.628\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.379\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.195\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.383\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n","[09/03 17:01:55 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.795 | 62.847 | 21.713 | 0.110 | 13.435 | 37.897 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:01:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.561\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.331\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n","[09/03 17:01:55 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.831 | 56.073 | 18.011 | 0.008 | 8.800 | 33.123 |\n","[09/03 17:01:55 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: 27.7948,62.8475,21.7128,0.1103,13.4353,37.8975\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:01:55 d2.evaluation.testing]: copypaste: 23.8314,56.0730,18.0112,0.0077,8.8005,33.1231\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:02:04 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:02:05 d2.data.common]: Serializing the dataset using: \n","[09/03 17:02:05 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:02:05 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:02:05 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:02:06 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:02:11 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1853 s/iter. Eval: 0.2018 s/iter. Total: 0.3918 s/iter. ETA=0:00:10\n","[09/03 17:02:16 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0062 s/iter. Inference: 0.1802 s/iter. Eval: 0.1872 s/iter. Total: 0.3739 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:02:21 d2.evaluation.evaluator]: Total inference time: 0:00:12.533778 (0.391681 s / iter per device, on 1 devices)\n","[09/03 17:02:21 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180022 s / iter per device, on 1 devices)\n","[09/03 17:02:21 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:02:21 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:02:21 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:02:21 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:02:21 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.08 seconds.\n","[09/03 17:02:21 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:02:21 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.278\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.628\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.217\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.134\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.379\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.195\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.383\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n","[09/03 17:02:21 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.795 | 62.847 | 21.713 | 0.110 | 13.435 | 37.897 |\n","Loading and preparing results...\n","DONE (t=0.09s)\n","creating index...\n","index created!\n","[09/03 17:02:22 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:02:22 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.23 seconds.\n","[09/03 17:02:22 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:02:22 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.561\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.180\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.331\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.177\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429\n","[09/03 17:02:22 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.831 | 56.073 | 18.011 | 0.008 | 8.800 | 33.123 |\n","[09/03 17:02:22 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: 27.7948,62.8475,21.7128,0.1103,13.4353,37.8975\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:02:22 d2.evaluation.testing]: copypaste: 23.8314,56.0730,18.0112,0.0077,8.8005,33.1231\n","Av. segm AP50 = 56.072952658003516\n","[09/03 17:02:27 d2.utils.events]: eta: 8 days, 3:22:31 iter: 1599 total_loss: 1.44 loss_cls: 0.3344 loss_box_reg: 0.5364 loss_mask: 0.3709 loss_rpn_cls: 0.07271 loss_rpn_loc: 0.09479 validation_loss: 1.603 time: 1.3643 last_time: 0.9475 data_time: 0.0292 last_data_time: 0.0144 lr: 0.0003 max_mem: 7835M\n","[09/03 17:02:53 d2.utils.events]: eta: 8 days, 3:22:02 iter: 1619 total_loss: 1.391 loss_cls: 0.325 loss_box_reg: 0.5465 loss_mask: 0.3634 loss_rpn_cls: 0.08157 loss_rpn_loc: 0.07712 validation_loss: 1.603 time: 1.3636 last_time: 1.6245 data_time: 0.0280 last_data_time: 0.0429 lr: 0.0003 max_mem: 7835M\n","[09/03 17:03:20 d2.utils.events]: eta: 8 days, 3:08:12 iter: 1639 total_loss: 1.523 loss_cls: 0.3241 loss_box_reg: 0.5514 loss_mask: 0.3718 loss_rpn_cls: 0.07861 loss_rpn_loc: 0.1096 validation_loss: 1.603 time: 1.3632 last_time: 1.7033 data_time: 0.0282 last_data_time: 0.0538 lr: 0.0003 max_mem: 7835M\n","[09/03 17:03:33 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:03:34 d2.data.common]: Serializing the dataset using: \n","[09/03 17:03:34 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:03:34 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:03:34 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:03:34 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:03:40 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0128 s/iter. Inference: 0.1879 s/iter. Eval: 0.2478 s/iter. Total: 0.4485 s/iter. ETA=0:00:11\n","[09/03 17:03:45 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0086 s/iter. Inference: 0.1790 s/iter. Eval: 0.1991 s/iter. Total: 0.3869 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:03:49 d2.evaluation.evaluator]: Total inference time: 0:00:12.032238 (0.376007 s / iter per device, on 1 devices)\n","[09/03 17:03:49 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.176969 s / iter per device, on 1 devices)\n","[09/03 17:03:49 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:03:49 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:03:49 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:03:49 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:03:49 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:03:49 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:03:49 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.616\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.200\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.249\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 17:03:49 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.688 | 61.613 | 19.974 | 0.074 | 14.572 | 37.743 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 17:03:50 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:03:50 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.22 seconds.\n","[09/03 17:03:50 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:03:50 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.561\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.090\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n","[09/03 17:03:50 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.248 | 56.088 | 18.437 | 0.031 | 8.973 | 33.559 |\n","[09/03 17:03:50 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: 27.6882,61.6130,19.9741,0.0739,14.5716,37.7429\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:03:50 d2.evaluation.testing]: copypaste: 24.2482,56.0880,18.4372,0.0313,8.9732,33.5589\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:04:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:04:01 d2.data.common]: Serializing the dataset using: \n","[09/03 17:04:01 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:04:01 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:04:01 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:04:01 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:04:05 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1785 s/iter. Eval: 0.1820 s/iter. Total: 0.3651 s/iter. ETA=0:00:09\n","[09/03 17:04:11 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0111 s/iter. Inference: 0.1835 s/iter. Eval: 0.2253 s/iter. Total: 0.4200 s/iter. ETA=0:00:05\n","[09/03 17:04:16 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0109 s/iter. Inference: 0.1830 s/iter. Eval: 0.2179 s/iter. Total: 0.4122 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:04:16 d2.evaluation.evaluator]: Total inference time: 0:00:13.206342 (0.412698 s / iter per device, on 1 devices)\n","[09/03 17:04:16 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182774 s / iter per device, on 1 devices)\n","[09/03 17:04:16 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:04:16 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:04:17 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.277\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.616\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.200\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.249\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 17:04:17 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.688 | 61.613 | 19.974 | 0.074 | 14.572 | 37.743 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:04:17 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.242\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.561\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.184\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.090\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.336\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n","[09/03 17:04:17 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.248 | 56.088 | 18.437 | 0.031 | 8.973 | 33.559 |\n","[09/03 17:04:17 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: 27.6882,61.6130,19.9741,0.0739,14.5716,37.7429\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:04:17 d2.evaluation.testing]: copypaste: 24.2482,56.0880,18.4372,0.0313,8.9732,33.5589\n","Av. segm AP50 = 56.088035478860974\n","[09/03 17:04:31 d2.utils.events]: eta: 8 days, 2:41:15 iter: 1659 total_loss: 1.491 loss_cls: 0.3377 loss_box_reg: 0.5654 loss_mask: 0.3748 loss_rpn_cls: 0.07544 loss_rpn_loc: 0.1262 validation_loss: 1.571 time: 1.3618 last_time: 1.0992 data_time: 0.0324 last_data_time: 0.0153 lr: 0.0003 max_mem: 7835M\n","[09/03 17:04:59 d2.utils.events]: eta: 8 days, 3:07:16 iter: 1679 total_loss: 1.333 loss_cls: 0.3183 loss_box_reg: 0.5361 loss_mask: 0.3595 loss_rpn_cls: 0.06642 loss_rpn_loc: 0.062 validation_loss: 1.571 time: 1.3626 last_time: 1.7154 data_time: 0.0367 last_data_time: 0.0219 lr: 0.0003 max_mem: 7835M\n","[09/03 17:05:26 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:05:27 d2.data.common]: Serializing the dataset using: \n","[09/03 17:05:27 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:05:27 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:05:27 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:05:27 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:05:31 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1757 s/iter. Eval: 0.1722 s/iter. Total: 0.3525 s/iter. ETA=0:00:09\n","[09/03 17:05:36 d2.evaluation.evaluator]: Inference done 26/37. Dataloading: 0.0055 s/iter. Inference: 0.1750 s/iter. Eval: 0.1751 s/iter. Total: 0.3558 s/iter. ETA=0:00:03\n","[09/03 17:05:41 d2.evaluation.evaluator]: Inference done 37/37. Dataloading: 0.0074 s/iter. Inference: 0.1773 s/iter. Eval: 0.2068 s/iter. Total: 0.3918 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:05:41 d2.evaluation.evaluator]: Total inference time: 0:00:12.687160 (0.396474 s / iter per device, on 1 devices)\n","[09/03 17:05:42 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.177306 s / iter per device, on 1 devices)\n","[09/03 17:05:42 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:05:42 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:05:42 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.143\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.393\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.499\n","[09/03 17:05:42 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.908 | 61.530 | 21.130 | 0.418 | 14.299 | 37.591 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:05:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.236\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.559\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.325\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427\n","[09/03 17:05:42 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.631 | 55.914 | 17.444 | 0.001 | 9.617 | 32.496 |\n","[09/03 17:05:42 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: 27.9077,61.5298,21.1295,0.4180,14.2994,37.5913\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:05:42 d2.evaluation.testing]: copypaste: 23.6313,55.9138,17.4441,0.0013,9.6174,32.4957\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:05:51 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:05:52 d2.data.common]: Serializing the dataset using: \n","[09/03 17:05:52 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:05:52 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:05:52 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:05:52 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:05:57 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0129 s/iter. Inference: 0.1836 s/iter. Eval: 0.2688 s/iter. Total: 0.4653 s/iter. ETA=0:00:12\n","[09/03 17:06:02 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0086 s/iter. Inference: 0.1795 s/iter. Eval: 0.2118 s/iter. Total: 0.4002 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:06:07 d2.evaluation.evaluator]: Total inference time: 0:00:12.346033 (0.385814 s / iter per device, on 1 devices)\n","[09/03 17:06:07 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.178055 s / iter per device, on 1 devices)\n","[09/03 17:06:07 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:06:07 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:06:07 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.211\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.143\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.376\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.200\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.393\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.267\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.499\n","[09/03 17:06:07 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.908 | 61.530 | 21.130 | 0.418 | 14.299 | 37.591 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:06:07 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.236\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.559\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.174\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.325\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.212\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427\n","[09/03 17:06:07 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.631 | 55.914 | 17.444 | 0.001 | 9.617 | 32.496 |\n","[09/03 17:06:07 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: 27.9077,61.5298,21.1295,0.4180,14.2994,37.5913\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:06:07 d2.evaluation.testing]: copypaste: 23.6313,55.9138,17.4441,0.0013,9.6174,32.4957\n","Av. segm AP50 = 55.91376744821952\n","[09/03 17:06:07 d2.utils.events]: eta: 8 days, 3:16:18 iter: 1699 total_loss: 1.489 loss_cls: 0.3317 loss_box_reg: 0.5131 loss_mask: 0.3495 loss_rpn_cls: 0.08409 loss_rpn_loc: 0.157 validation_loss: 1.611 time: 1.3623 last_time: 1.2631 data_time: 0.0316 last_data_time: 0.0863 lr: 0.0003 max_mem: 7835M\n","[09/03 17:06:32 d2.utils.events]: eta: 8 days, 2:22:45 iter: 1719 total_loss: 1.516 loss_cls: 0.3094 loss_box_reg: 0.5469 loss_mask: 0.3683 loss_rpn_cls: 0.07836 loss_rpn_loc: 0.1658 validation_loss: 1.611 time: 1.3606 last_time: 1.2878 data_time: 0.0323 last_data_time: 0.0269 lr: 0.0003 max_mem: 7835M\n","[09/03 17:07:00 d2.utils.events]: eta: 8 days, 2:32:09 iter: 1739 total_loss: 1.334 loss_cls: 0.3223 loss_box_reg: 0.5212 loss_mask: 0.361 loss_rpn_cls: 0.06889 loss_rpn_loc: 0.06287 validation_loss: 1.611 time: 1.3610 last_time: 1.4921 data_time: 0.0311 last_data_time: 0.0259 lr: 0.0003 max_mem: 7835M\n","[09/03 17:07:12 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:07:13 d2.data.common]: Serializing the dataset using: \n","[09/03 17:07:13 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:07:13 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:07:13 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:07:13 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:07:18 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1797 s/iter. Eval: 0.2010 s/iter. Total: 0.3853 s/iter. ETA=0:00:10\n","[09/03 17:07:23 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0090 s/iter. Inference: 0.1806 s/iter. Eval: 0.2165 s/iter. Total: 0.4062 s/iter. ETA=0:00:05\n","[09/03 17:07:28 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0101 s/iter. Inference: 0.1836 s/iter. Eval: 0.2453 s/iter. Total: 0.4392 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:07:29 d2.evaluation.evaluator]: Total inference time: 0:00:13.966829 (0.436463 s / iter per device, on 1 devices)\n","[09/03 17:07:29 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183154 s / iter per device, on 1 devices)\n","[09/03 17:07:29 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:07:29 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:07:30 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.633\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483\n","[09/03 17:07:30 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.956 | 63.285 | 20.911 | 0.254 | 14.064 | 37.702 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:07:30 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.573\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.092\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.180\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.231\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422\n","[09/03 17:07:30 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.381 | 57.292 | 18.067 | 0.025 | 9.224 | 33.455 |\n","[09/03 17:07:30 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: 27.9556,63.2854,20.9110,0.2537,14.0638,37.7018\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:07:30 d2.evaluation.testing]: copypaste: 24.3808,57.2916,18.0672,0.0248,9.2242,33.4547\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:07:39 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:07:40 d2.data.common]: Serializing the dataset using: \n","[09/03 17:07:40 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:07:40 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:07:40 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:07:40 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:07:46 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0121 s/iter. Inference: 0.1950 s/iter. Eval: 0.2660 s/iter. Total: 0.4732 s/iter. ETA=0:00:12\n","[09/03 17:07:51 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0077 s/iter. Inference: 0.1847 s/iter. Eval: 0.2179 s/iter. Total: 0.4106 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:07:56 d2.evaluation.evaluator]: Total inference time: 0:00:13.162265 (0.411321 s / iter per device, on 1 devices)\n","[09/03 17:07:56 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183595 s / iter per device, on 1 devices)\n","[09/03 17:07:56 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:07:56 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:07:56 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:07:56 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:07:56 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:07:56 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:07:56 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.633\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.209\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.384\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.483\n","[09/03 17:07:56 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 27.956 | 63.285 | 20.911 | 0.254 | 14.064 | 37.702 |\n","Loading and preparing results...\n","DONE (t=0.09s)\n","creating index...\n","index created!\n","[09/03 17:07:57 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:07:57 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.25 seconds.\n","[09/03 17:07:57 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:07:57 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.573\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.181\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.092\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.335\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.180\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.333\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.231\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422\n","[09/03 17:07:57 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.381 | 57.292 | 18.067 | 0.025 | 9.224 | 33.455 |\n","[09/03 17:07:57 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: 27.9556,63.2854,20.9110,0.2537,14.0638,37.7018\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:07:57 d2.evaluation.testing]: copypaste: 24.3808,57.2916,18.0672,0.0248,9.2242,33.4547\n","Av. segm AP50 = 57.29163335143232\n","[09/03 17:08:17 d2.utils.events]: eta: 8 days, 2:31:41 iter: 1759 total_loss: 1.447 loss_cls: 0.3029 loss_box_reg: 0.532 loss_mask: 0.3662 loss_rpn_cls: 0.0799 loss_rpn_loc: 0.1155 validation_loss: 1.621 time: 1.3607 last_time: 1.6620 data_time: 0.0281 last_data_time: 0.0194 lr: 0.0003 max_mem: 7835M\n","[09/03 17:08:46 d2.utils.events]: eta: 8 days, 2:14:01 iter: 1779 total_loss: 1.417 loss_cls: 0.3232 loss_box_reg: 0.5492 loss_mask: 0.3826 loss_rpn_cls: 0.06728 loss_rpn_loc: 0.08789 validation_loss: 1.621 time: 1.3617 last_time: 1.6135 data_time: 0.0314 last_data_time: 0.0554 lr: 0.0003 max_mem: 7835M\n","[09/03 17:09:13 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:09:14 d2.data.common]: Serializing the dataset using: \n","[09/03 17:09:14 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:09:14 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:09:14 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:09:14 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:09:18 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0046 s/iter. Inference: 0.1788 s/iter. Eval: 0.1871 s/iter. Total: 0.3704 s/iter. ETA=0:00:09\n","[09/03 17:09:23 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0054 s/iter. Inference: 0.1776 s/iter. Eval: 0.1847 s/iter. Total: 0.3679 s/iter. ETA=0:00:04\n","[09/03 17:09:28 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0072 s/iter. Inference: 0.1798 s/iter. Eval: 0.2123 s/iter. Total: 0.3994 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:09:29 d2.evaluation.evaluator]: Total inference time: 0:00:12.965203 (0.405163 s / iter per device, on 1 devices)\n","[09/03 17:09:29 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.179732 s / iter per device, on 1 devices)\n","[09/03 17:09:29 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:09:29 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:09:29 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:09:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:09:29 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 17:09:29 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:09:29 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.623\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.388\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.205\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.389\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.500\n","[09/03 17:09:29 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.531 | 62.320 | 21.929 | 0.016 | 14.531 | 38.776 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 17:09:29 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:09:30 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.19 seconds.\n","[09/03 17:09:30 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:09:30 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.559\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.186\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.213\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437\n","[09/03 17:09:30 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.641 | 55.941 | 18.561 | 0.001 | 9.559 | 34.055 |\n","[09/03 17:09:30 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: 28.5313,62.3202,21.9290,0.0165,14.5314,38.7761\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:09:30 d2.evaluation.testing]: copypaste: 24.6413,55.9414,18.5614,0.0009,9.5587,34.0550\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:09:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:09:39 d2.data.common]: Serializing the dataset using: \n","[09/03 17:09:39 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:09:39 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:09:39 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:09:39 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:09:45 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0206 s/iter. Inference: 0.1924 s/iter. Eval: 0.2947 s/iter. Total: 0.5076 s/iter. ETA=0:00:13\n","[09/03 17:09:50 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0128 s/iter. Inference: 0.1859 s/iter. Eval: 0.2341 s/iter. Total: 0.4329 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:09:55 d2.evaluation.evaluator]: Total inference time: 0:00:13.065720 (0.408304 s / iter per device, on 1 devices)\n","[09/03 17:09:55 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183117 s / iter per device, on 1 devices)\n","[09/03 17:09:55 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:09:55 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:09:55 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.285\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.623\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.219\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.388\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.025\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.205\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.389\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.008\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.500\n","[09/03 17:09:55 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.531 | 62.320 | 21.929 | 0.016 | 14.531 | 38.776 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:09:55 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.246\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.559\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.341\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.186\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.213\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437\n","[09/03 17:09:55 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.641 | 55.941 | 18.561 | 0.001 | 9.559 | 34.055 |\n","[09/03 17:09:55 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: 28.5313,62.3202,21.9290,0.0165,14.5314,38.7761\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:09:55 d2.evaluation.testing]: copypaste: 24.6413,55.9414,18.5614,0.0009,9.5587,34.0550\n","Av. segm AP50 = 55.94141696121704\n","[09/03 17:09:55 d2.utils.events]: eta: 8 days, 2:03:46 iter: 1799 total_loss: 1.469 loss_cls: 0.3251 loss_box_reg: 0.5147 loss_mask: 0.362 loss_rpn_cls: 0.07162 loss_rpn_loc: 0.1122 validation_loss: 1.574 time: 1.3614 last_time: 1.0990 data_time: 0.0299 last_data_time: 0.0372 lr: 0.0003 max_mem: 7835M\n","[09/03 17:10:21 d2.utils.events]: eta: 8 days, 1:57:14 iter: 1819 total_loss: 1.493 loss_cls: 0.3307 loss_box_reg: 0.5343 loss_mask: 0.3632 loss_rpn_cls: 0.07955 loss_rpn_loc: 0.09886 validation_loss: 1.574 time: 1.3607 last_time: 1.6015 data_time: 0.0274 last_data_time: 0.0123 lr: 0.0003 max_mem: 7835M\n","[09/03 17:10:48 d2.utils.events]: eta: 8 days, 0:22:03 iter: 1839 total_loss: 1.468 loss_cls: 0.3007 loss_box_reg: 0.5286 loss_mask: 0.3653 loss_rpn_cls: 0.07036 loss_rpn_loc: 0.1188 validation_loss: 1.574 time: 1.3600 last_time: 1.7085 data_time: 0.0280 last_data_time: 0.0160 lr: 0.0003 max_mem: 7835M\n","[09/03 17:10:58 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:10:59 d2.data.common]: Serializing the dataset using: \n","[09/03 17:10:59 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:10:59 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:10:59 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:10:59 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:11:04 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0047 s/iter. Inference: 0.1801 s/iter. Eval: 0.1940 s/iter. Total: 0.3789 s/iter. ETA=0:00:09\n","[09/03 17:11:09 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0058 s/iter. Inference: 0.1790 s/iter. Eval: 0.1923 s/iter. Total: 0.3772 s/iter. ETA=0:00:04\n","[09/03 17:11:15 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0071 s/iter. Inference: 0.1813 s/iter. Eval: 0.2240 s/iter. Total: 0.4125 s/iter. ETA=0:00:00\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:11:15 d2.evaluation.evaluator]: Total inference time: 0:00:13.440717 (0.420022 s / iter per device, on 1 devices)\n","[09/03 17:11:15 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181492 s / iter per device, on 1 devices)\n","[09/03 17:11:15 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:11:15 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:11:16 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.268\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.622\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.178\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.137\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.194\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471\n","[09/03 17:11:16 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.770 | 62.156 | 17.842 | 0.495 | 13.744 | 35.984 |\n","Loading and preparing results...\n","DONE (t=0.06s)\n","creating index...\n","index created!\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:11:16 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.087\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.226\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416\n","[09/03 17:11:16 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.220 | 54.925 | 17.598 | 0.000 | 8.702 | 31.917 |\n","[09/03 17:11:16 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: 26.7704,62.1561,17.8420,0.4948,13.7439,35.9843\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:11:16 d2.evaluation.testing]: copypaste: 23.2200,54.9253,17.5983,0.0000,8.7017,31.9167\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:11:25 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:11:26 d2.data.common]: Serializing the dataset using: \n","[09/03 17:11:26 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:11:26 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:11:26 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:11:26 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:11:32 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0107 s/iter. Inference: 0.1891 s/iter. Eval: 0.2989 s/iter. Total: 0.4987 s/iter. ETA=0:00:12\n","[09/03 17:11:37 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0086 s/iter. Inference: 0.1843 s/iter. Eval: 0.2301 s/iter. Total: 0.4233 s/iter. ETA=0:00:05\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:11:42 d2.evaluation.evaluator]: Total inference time: 0:00:13.010628 (0.406582 s / iter per device, on 1 devices)\n","[09/03 17:11:42 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.182019 s / iter per device, on 1 devices)\n","[09/03 17:11:42 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:11:42 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:11:42 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.04 seconds.\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.268\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.622\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.178\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.137\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.194\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471\n","[09/03 17:11:42 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 26.770 | 62.156 | 17.842 | 0.495 | 13.744 | 35.984 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:11:42 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.232\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.176\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.087\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.319\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.179\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.226\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416\n","[09/03 17:11:42 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.220 | 54.925 | 17.598 | 0.000 | 8.702 | 31.917 |\n","[09/03 17:11:42 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: 26.7704,62.1561,17.8420,0.4948,13.7439,35.9843\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:11:42 d2.evaluation.testing]: copypaste: 23.2200,54.9253,17.5983,0.0000,8.7017,31.9167\n","Av. segm AP50 = 54.92526882302933\n","[09/03 17:11:55 d2.utils.events]: eta: 7 days, 23:03:37 iter: 1859 total_loss: 1.397 loss_cls: 0.2942 loss_box_reg: 0.5265 loss_mask: 0.3572 loss_rpn_cls: 0.07805 loss_rpn_loc: 0.1051 validation_loss: 1.607 time: 1.3581 last_time: 1.5203 data_time: 0.0303 last_data_time: 0.0247 lr: 0.0003 max_mem: 7835M\n","[09/03 17:12:22 d2.utils.events]: eta: 7 days, 22:40:49 iter: 1879 total_loss: 1.39 loss_cls: 0.3147 loss_box_reg: 0.5372 loss_mask: 0.3593 loss_rpn_cls: 0.07161 loss_rpn_loc: 0.074 validation_loss: 1.607 time: 1.3582 last_time: 1.4282 data_time: 0.0344 last_data_time: 0.0360 lr: 0.0003 max_mem: 7835M\n","[09/03 17:12:47 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:12:48 d2.data.common]: Serializing the dataset using: \n","[09/03 17:12:48 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:12:48 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:12:48 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:12:48 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:12:53 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0054 s/iter. Inference: 0.1836 s/iter. Eval: 0.2448 s/iter. Total: 0.4338 s/iter. ETA=0:00:11\n","[09/03 17:12:58 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0073 s/iter. Inference: 0.1826 s/iter. Eval: 0.2267 s/iter. Total: 0.4171 s/iter. ETA=0:00:05\n","[09/03 17:13:03 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0079 s/iter. Inference: 0.1859 s/iter. Eval: 0.2521 s/iter. Total: 0.4465 s/iter. ETA=0:00:01\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:13:05 d2.evaluation.evaluator]: Total inference time: 0:00:14.192053 (0.443502 s / iter per device, on 1 devices)\n","[09/03 17:13:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185251 s / iter per device, on 1 devices)\n","[09/03 17:13:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:13:05 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:13:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.06 seconds.\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.629\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 17:13:05 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.002 | 62.922 | 20.224 | 0.545 | 15.188 | 37.661 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.14 seconds.\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:13:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.098\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.328\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.228\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425\n","[09/03 17:13:05 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.988 | 56.764 | 18.345 | 0.071 | 9.804 | 32.780 |\n","[09/03 17:13:05 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: 28.0024,62.9222,20.2243,0.5454,15.1880,37.6614\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:13:05 d2.evaluation.testing]: copypaste: 23.9879,56.7636,18.3447,0.0714,9.8038,32.7800\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:13:14 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:13:15 d2.data.common]: Serializing the dataset using: \n","[09/03 17:13:15 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:13:15 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:13:15 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:13:15 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["[09/03 17:13:21 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0064 s/iter. Inference: 0.1862 s/iter. Eval: 0.2575 s/iter. Total: 0.4501 s/iter. ETA=0:00:11\n","[09/03 17:13:26 d2.evaluation.evaluator]: Inference done 25/37. Dataloading: 0.0067 s/iter. Inference: 0.1817 s/iter. Eval: 0.2127 s/iter. Total: 0.4013 s/iter. ETA=0:00:04\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:13:31 d2.evaluation.evaluator]: Total inference time: 0:00:12.995319 (0.406104 s / iter per device, on 1 devices)\n","[09/03 17:13:31 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.180922 s / iter per device, on 1 devices)\n","[09/03 17:13:31 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:13:31 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:13:32 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.629\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.152\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.023\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.198\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.273\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.491\n","[09/03 17:13:32 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.002 | 62.922 | 20.224 | 0.545 | 15.188 | 37.661 |\n","Loading and preparing results...\n","DONE (t=0.07s)\n","creating index...\n","index created!\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.22 seconds.\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:13:32 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.098\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.328\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.022\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.181\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.228\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425\n","[09/03 17:13:32 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 23.988 | 56.764 | 18.345 | 0.071 | 9.804 | 32.780 |\n","[09/03 17:13:32 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: 28.0024,62.9222,20.2243,0.5454,15.1880,37.6614\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:13:32 d2.evaluation.testing]: copypaste: 23.9879,56.7636,18.3447,0.0714,9.8038,32.7800\n","Av. segm AP50 = 56.76364865203141\n","[09/03 17:13:32 d2.utils.events]: eta: 7 days, 22:10:24 iter: 1899 total_loss: 1.344 loss_cls: 0.3103 loss_box_reg: 0.5105 loss_mask: 0.3532 loss_rpn_cls: 0.06906 loss_rpn_loc: 0.06878 validation_loss: 1.622 time: 1.3572 last_time: 0.8079 data_time: 0.0234 last_data_time: 0.0235 lr: 0.0003 max_mem: 7835M\n","[09/03 17:14:00 d2.utils.events]: eta: 7 days, 21:58:33 iter: 1919 total_loss: 1.464 loss_cls: 0.3168 loss_box_reg: 0.5262 loss_mask: 0.3514 loss_rpn_cls: 0.06889 loss_rpn_loc: 0.1108 validation_loss: 1.622 time: 1.3574 last_time: 1.6179 data_time: 0.0256 last_data_time: 0.0111 lr: 0.0003 max_mem: 7835M\n","[09/03 17:14:27 d2.utils.events]: eta: 7 days, 22:09:29 iter: 1939 total_loss: 1.472 loss_cls: 0.318 loss_box_reg: 0.5266 loss_mask: 0.3665 loss_rpn_cls: 0.07984 loss_rpn_loc: 0.1217 validation_loss: 1.622 time: 1.3573 last_time: 1.2739 data_time: 0.0300 last_data_time: 0.0218 lr: 0.0003 max_mem: 7835M\n","[09/03 17:14:40 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:14:41 d2.data.common]: Serializing the dataset using: \n","[09/03 17:14:41 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:14:41 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:14:41 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:14:41 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:14:46 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0135 s/iter. Inference: 0.1875 s/iter. Eval: 0.3034 s/iter. Total: 0.5045 s/iter. ETA=0:00:13\n","[09/03 17:14:51 d2.evaluation.evaluator]: Inference done 22/37. Dataloading: 0.0137 s/iter. Inference: 0.1892 s/iter. Eval: 0.2813 s/iter. Total: 0.4848 s/iter. ETA=0:00:07\n","[09/03 17:14:57 d2.evaluation.evaluator]: Inference done 35/37. Dataloading: 0.0109 s/iter. Inference: 0.1858 s/iter. Eval: 0.2554 s/iter. Total: 0.4525 s/iter. ETA=0:00:00\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:14:58 d2.evaluation.evaluator]: Total inference time: 0:00:14.438606 (0.451206 s / iter per device, on 1 devices)\n","[09/03 17:14:58 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.185548 s / iter per device, on 1 devices)\n","[09/03 17:14:58 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:14:58 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:14:58 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.07 seconds.\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.301\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.637\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.248\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.153\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.213\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[09/03 17:14:58 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 30.145 | 63.652 | 24.812 | 0.236 | 15.294 | 40.507 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.16 seconds.\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:14:58 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.02 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.260\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.215\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.194\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442\n","[09/03 17:14:58 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 25.968 | 57.211 | 21.483 | 0.003 | 9.608 | 35.681 |\n","[09/03 17:14:58 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: 30.1448,63.6519,24.8116,0.2364,15.2941,40.5074\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:14:58 d2.evaluation.testing]: copypaste: 25.9678,57.2109,21.4834,0.0033,9.6076,35.6806\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:15:09 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:15:09 d2.data.common]: Serializing the dataset using: \n","[09/03 17:15:09 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:15:09 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:15:09 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:15:10 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:15:14 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1821 s/iter. Eval: 0.2050 s/iter. Total: 0.3920 s/iter. ETA=0:00:10\n","[09/03 17:15:19 d2.evaluation.evaluator]: Inference done 23/37. Dataloading: 0.0071 s/iter. Inference: 0.1836 s/iter. Eval: 0.2312 s/iter. Total: 0.4220 s/iter. ETA=0:00:05\n","[09/03 17:15:25 d2.evaluation.evaluator]: Inference done 34/37. Dataloading: 0.0087 s/iter. Inference: 0.1843 s/iter. Eval: 0.2484 s/iter. Total: 0.4418 s/iter. ETA=0:00:01\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:15:26 d2.evaluation.evaluator]: Total inference time: 0:00:14.067863 (0.439621 s / iter per device, on 1 devices)\n","[09/03 17:15:26 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.183958 s / iter per device, on 1 devices)\n","[09/03 17:15:26 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:15:26 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:15:26 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.01s)\n","creating index...\n","index created!\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.301\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.637\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.248\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.153\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.213\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.016\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n","[09/03 17:15:26 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 30.145 | 63.652 | 24.812 | 0.236 | 15.294 | 40.507 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:15:26 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.260\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.215\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.357\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.194\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.232\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442\n","[09/03 17:15:26 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 25.968 | 57.211 | 21.483 | 0.003 | 9.608 | 35.681 |\n","[09/03 17:15:26 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: 30.1448,63.6519,24.8116,0.2364,15.2941,40.5074\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:15:26 d2.evaluation.testing]: copypaste: 25.9678,57.2109,21.4834,0.0033,9.6076,35.6806\n","Av. segm AP50 = 57.210856978743976\n","[09/03 17:15:40 d2.utils.events]: eta: 7 days, 23:12:59 iter: 1959 total_loss: 1.504 loss_cls: 0.3013 loss_box_reg: 0.5263 loss_mask: 0.3614 loss_rpn_cls: 0.08409 loss_rpn_loc: 0.194 validation_loss: 1.559 time: 1.3574 last_time: 0.9781 data_time: 0.0271 last_data_time: 0.0280 lr: 0.0003 max_mem: 7835M\n","[09/03 17:16:04 d2.utils.events]: eta: 7 days, 21:54:13 iter: 1979 total_loss: 1.47 loss_cls: 0.3096 loss_box_reg: 0.5216 loss_mask: 0.3773 loss_rpn_cls: 0.08468 loss_rpn_loc: 0.1092 validation_loss: 1.559 time: 1.3554 last_time: 1.2793 data_time: 0.0213 last_data_time: 0.0215 lr: 0.0003 max_mem: 7835M\n","[09/03 17:16:31 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:16:32 d2.data.common]: Serializing the dataset using: \n","[09/03 17:16:32 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:16:32 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:16:32 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:16:32 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:16:38 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0273 s/iter. Inference: 0.1917 s/iter. Eval: 0.2714 s/iter. Total: 0.4904 s/iter. ETA=0:00:12\n","[09/03 17:16:43 d2.evaluation.evaluator]: Inference done 26/37. Dataloading: 0.0128 s/iter. Inference: 0.1800 s/iter. Eval: 0.1977 s/iter. Total: 0.3908 s/iter. ETA=0:00:04\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:16:47 d2.evaluation.evaluator]: Total inference time: 0:00:11.983185 (0.374475 s / iter per device, on 1 devices)\n","[09/03 17:16:47 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.176931 s / iter per device, on 1 devices)\n","[09/03 17:16:47 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:16:47 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:16:47 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.201\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.499\n","[09/03 17:16:47 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.019 | 60.746 | 20.977 | 0.000 | 13.791 | 38.196 |\n","Loading and preparing results...\n","DONE (t=0.04s)\n","creating index...\n","index created!\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.13 seconds.\n","[09/03 17:16:47 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:16:48 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n","[09/03 17:16:48 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.335 | 55.440 | 18.621 | 0.000 | 8.925 | 33.739 |\n","[09/03 17:16:48 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: 28.0189,60.7461,20.9773,0.0000,13.7914,38.1958\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:16:48 d2.evaluation.testing]: copypaste: 24.3348,55.4397,18.6209,0.0000,8.9247,33.7389\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n","/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:16:59 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: []\n","[09/03 17:16:59 d2.data.common]: Serializing the dataset using: \n","[09/03 17:16:59 d2.data.common]: Serializing 37 elements to byte tensors and concatenating them all ...\n","[09/03 17:16:59 d2.data.common]: Serialized dataset takes 0.73 MiB\n","WARNING [09/03 17:16:59 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n","[09/03 17:16:59 d2.evaluation.evaluator]: Start inference on 37 batches\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:17:04 d2.evaluation.evaluator]: Inference done 11/37. Dataloading: 0.0048 s/iter. Inference: 0.1781 s/iter. Eval: 0.1789 s/iter. Total: 0.3618 s/iter. ETA=0:00:09\n","[09/03 17:17:09 d2.evaluation.evaluator]: Inference done 24/37. Dataloading: 0.0131 s/iter. Inference: 0.1801 s/iter. Eval: 0.1979 s/iter. Total: 0.3912 s/iter. ETA=0:00:05\n","[09/03 17:17:14 d2.evaluation.evaluator]: Inference done 36/37. Dataloading: 0.0163 s/iter. Inference: 0.1818 s/iter. Eval: 0.2050 s/iter. Total: 0.4035 s/iter. ETA=0:00:00\n"]},{"output_type":"stream","name":"stderr","text":["/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," self.pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:17:15 d2.evaluation.evaluator]: Total inference time: 0:00:12.918895 (0.403715 s / iter per device, on 1 devices)\n","[09/03 17:17:15 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:05 (0.181518 s / iter per device, on 1 devices)\n","[09/03 17:17:15 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...\n","[09/03 17:17:15 d2.evaluation.coco_evaluation]: Saving results to eval/coco_instances_results.json\n","[09/03 17:17:15 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...\n","Loading and preparing results...\n","DONE (t=0.00s)\n","creating index...\n","index created!\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.210\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.138\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.026\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.201\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.247\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.499\n","[09/03 17:17:15 d2.evaluation.coco_evaluation]: Evaluation results for bbox: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:------:|:------:|\n","| 28.019 | 60.746 | 20.977 | 0.000 | 13.791 | 38.196 |\n","Loading and preparing results...\n","DONE (t=0.05s)\n","creating index...\n","index created!\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.15 seconds.\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: Accumulating evaluation results...\n","[09/03 17:17:15 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.243\n"," Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554\n"," Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.186\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\n"," Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.024\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.183\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.209\n"," Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.433\n","[09/03 17:17:15 d2.evaluation.coco_evaluation]: Evaluation results for segm: \n","| AP | AP50 | AP75 | APs | APm | APl |\n","|:------:|:------:|:------:|:-----:|:-----:|:------:|\n","| 24.335 | 55.440 | 18.621 | 0.000 | 8.925 | 33.739 |\n","[09/03 17:17:15 d2.engine.defaults]: Evaluation results for ParacouMS_val in csv format:\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: Task: bbox\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: 28.0189,60.7461,20.9773,0.0000,13.7914,38.1958\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: Task: segm\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl\n","[09/03 17:17:15 d2.evaluation.testing]: copypaste: 24.3348,55.4397,18.6209,0.0000,8.9247,33.7389\n","Av. segm AP50 = 55.4397423585387\n","Early stopping occurs in iter 1749, max ap is 57.29163335143232\n","[09/03 17:17:15 d2.utils.events]: eta: 7 days, 21:53:45 iter: 1999 total_loss: 1.359 loss_cls: 0.3233 loss_box_reg: 0.5175 loss_mask: 0.3559 loss_rpn_cls: 0.07567 loss_rpn_loc: 0.07523 validation_loss: 1.574 time: 1.3556 last_time: 1.6510 data_time: 0.0235 last_data_time: 0.0216 lr: 0.0003 max_mem: 7835M\n","[09/03 17:17:15 d2.engine.hooks]: Overall training speed: 1998 iterations in 0:45:08 (1.3556 s / it)\n","[09/03 17:17:15 d2.engine.hooks]: Total training time: 1:15:49 (0:30:41 on hooks)\n","[09/03 17:17:30 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from /content/drive/MyDrive/WORK/detectree2/models/240903_ParacouMS/model_35.pth ...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/fvcore/common/checkpoint.py:252: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," return torch.load(f, map_location=torch.device(\"cpu\"))\n"]},{"output_type":"stream","name":"stdout","text":["[09/03 17:17:31 d2.engine.hooks]: Loading scheduler from state_dict ...\n","[09/03 17:17:31 d2.utils.events]: eta: 7 days, 21:53:44 iter: 2000 total_loss: 1.359 loss_cls: 0.3233 loss_box_reg: 0.5175 loss_mask: 0.3559 loss_rpn_cls: 0.07567 loss_rpn_loc: 0.07523 validation_loss: 1.574 time: 1.3556 last_time: 1.6510 data_time: 0.0235 last_data_time: 0.0216 lr: 0.0003 max_mem: 7835M\n"]}],"source":["trainer = MyTrainer(cfg, patience=5)\n","#trainer.resume_or_load(resume=False)\n","trainer.resume_or_load(resume=True)\n","trainer.train()"]},{"cell_type":"markdown","metadata":{"id":"Yg6IrLI-lLuZ"},"source":["## Plot the loss"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":678,"status":"ok","timestamp":1725385711360,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"6KQxaS3o0Rv6","outputId":"e3e4fb2a-9e24-41a7-dcc1-5246091f0ace"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["### Plot training and validation loss on the same plot to check how the training has gone\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","#out_dir = \"/content/drive/Shareddrives/detectree2/models/230103_resize_full\"\n","experiment_folder = out_dir\n","\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'validation_loss' in x],\n"," [x['validation_loss'] for x in experiment_metrics if 'validation_loss' in x], label='Total Validation Loss', color='red')\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'total_loss' in x],\n"," [x['total_loss'] for x in experiment_metrics if 'total_loss' in x], label='Total Training Loss')\n","\n","plt.legend(loc='upper right')\n","plt.title('Comparison of the training and validation loss of detectree2')\n","plt.ylabel('Total Loss')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"uobNPgiyZBfZ"},"source":["### How did the AP50 change through time?\n","\n","Early stopping means that if the AP50 stops increasing after the ```patience``` interval, training will terminate and the best model will be saved."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":646,"status":"ok","timestamp":1724350356411,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"vsgvjxQVXwsH","outputId":"08b44bc2-5709-4169-800f-1540101ce080"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlMAAAHHCAYAAACbXt0gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWqklEQVR4nO3dd1gUV/828HtpC1KlrgQEW6RI0KAipmAEg703bGBN7MYSNVFR8yS2mNg18TF2I2rUn7FgiC2JEgtqrGCJXQGVagOE8/7hu/Ow7gILQxG9P9e1l+6ZMzvfM9tupq1CCCFARERERMViUN4FEBEREVVkDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTVGQKhQJTp04t7zJkW7t2LTw8PGBsbAwbG5siz3/9+nUoFAp8++23JV9cGZo6dSoUCkWx5l21ahUUCgWuX79eskW9YsrjNa9+fa1atUpqK8pzVRo1N2nSBE2aNCnRx9THwYMHoVAocPDgwTJfdkmR+3lTltTv6xMnTpR3KRUGw1QxXL16FZ988gmqV68OU1NTWFlZ4b333sP8+fPx9OnT8i6P9BAXF4fw8HDUqFEDy5cvx48//phv3927d5dreHzy5AmmTp1aob9IqOK4cOECpk6d+toH5LJUlM8bdWA2MDDArVu3tKanp6fDzMwMCoUCw4YNK82yi0UdfNU3Q0NDODo6onPnzrh48WKRHkvf71p3d3coFAoMHz4833q2bNkitanDoqmpKe7cuaM1T5MmTVCnTp0i1WpUpN6EXbt2oUuXLlAqlejTpw/q1KmDrKws/PXXXxg3bhzOnz9f4BvldfD06VMYGVXsl87BgweRm5uL+fPno2bNmgX23b17NxYvXlxugerJkyeYNm0aAJTKVoFJkyZhwoQJxZq3d+/e6N69O5RKZQlXRbrIea70deHCBUybNg1NmjSBu7u7xrTffvutVJf9uirK542aUqnEzz//jM8//1yjfevWraVRYokbMWIEGjRogOzsbJw5cwbLli3DwYMHce7cOahUqkLnL8537fLlyzFx4kQ4OzvrVWNmZiZmzpyJhQsXFmuMeVXsb8Qydu3aNXTv3h1ubm7Yv38/qlSpIk0bOnQorly5gl27dpVjhaUnNzcXWVlZMDU1hampaXmXI1tSUhIAvPKb24vj8ePHMDc317u/kZFRscOxoaEhDA0NizUvFZ2c56okmJiYlNuyK7LifN60bNlSZ5jasGEDWrVqhV9++aUkSyxxH3zwATp37izdr127NgYPHow1a9Zojellxfmu9fb2Rnx8PGbOnIkFCxboVWPdunWLHMDyw918RTB79mw8evQIK1as0Hhy1WrWrImRI0dK958/f46vvvoKNWrUgFKphLu7O7744gtkZmZqzOfu7o7WrVvj4MGDqF+/PszMzODj4yPt1tm6dSt8fHxgamoKPz8/nDp1SmP+8PBwWFhY4N9//0VISAjMzc3h7OyM6dOnQwih0ffbb79F48aNYWdnBzMzM/j5+Wls/lRTb0Jev349vL29oVQqERUVJU3Lu5UmIyMDo0aNgru7O5RKJRwdHdGsWTOcPHlS4zE3b94MPz8/mJmZwd7eHr169dLaxKoey507d9C+fXtYWFjAwcEBY8eORU5OTj7PjKYlS5ZINTs7O2Po0KFITU3VWN8REREAAAcHhwKPLQkPD8fixYulcatvL/vxxx+l57lBgwY4fvy4Vp+4uDh07twZtra2MDU1Rf369bFjx44Cx3L9+nU4ODgAAKZNmyYtX12ven1dvXoVLVu2hKWlJXr27AkA+PPPP9GlSxdUrVoVSqUSrq6u+Oyzz7R2Res6Dkf9/G/fvh116tSBUqmEt7e39BpQ03XMlPr1/Ndff6Fhw4YwNTVF9erVsWbNGq3xnTlzBoGBgTAzM4OLiwv+85//YOXKlXodh3XmzBmEh4dLuwBUKhX69euHhw8f6hzflStXEB4eDhsbG1hbW6Nv37548uSJRt/MzEx89tlncHBwgKWlJdq2bYvbt28XWAcAJCYmwsjISNqCmFd8fDwUCgUWLVoEAEhOTsbYsWPh4+MDCwsLWFlZoUWLFvjnn38KXY6u50rfmm/cuIEhQ4agdu3aMDMzg52dHbp06aKxnletWoUuXboAAD766CPp9ab+LNJ1zFRSUhL69+8PJycnmJqawtfXF6tXr9bok/f4Qn3eK/rS5zMlISEBffv2hYuLC5RKJapUqYJ27dppjPvEiRMICQmBvb09zMzMUK1aNfTr10+vGkry8yavHj164PTp04iLi9MYy/79+9GjRw+t/llZWZgyZQr8/PxgbW0Nc3NzfPDBBzhw4IBW340bN8LPzw+WlpawsrKCj48P5s+fX2A9KSkpaNiwIVxcXBAfH19o/S/74IMPALzYdVeYon7XAi/Wc58+fbB8+XLcvXtXr5q++OIL5OTkYObMmXr1Lwi3TBXBr7/+iurVq6Nx48Z69R8wYABWr16Nzp07Y8yYMTh69ChmzJiBixcvYtu2bRp9r1y5gh49euCTTz5Br1698O2336JNmzZYtmwZvvjiCwwZMgQAMGPGDHTt2hXx8fEwMPhfFs7JyUHz5s3RqFEjzJ49G1FRUYiIiMDz588xffp0qd/8+fPRtm1b9OzZE1lZWdi4cSO6dOmCnTt3olWrVho17d+/H5s2bcKwYcNgb2+vtclf7dNPP8WWLVswbNgweHl54eHDh/jrr79w8eJFvPvuuwBefEj37dsXDRo0wIwZM5CYmIj58+fj8OHDOHXqlMZfbDk5OQgJCYG/vz++/fZb/P7775g7dy5q1KiBwYMHF7jOp06dimnTpiE4OBiDBw9GfHw8li5diuPHj+Pw4cMwNjbGvHnzsGbNGmzbtg1Lly6FhYUF3nnnHZ2P98knn+Du3buIjo7G2rVrdfbZsGEDMjIy8Mknn0ChUGD27Nno2LEj/v33XxgbGwMAzp8/j/feew9vvfUWJkyYAHNzc2zatAnt27fHL7/8gg4dOuh8bAcHByxduhSDBw9Ghw4d0LFjRwDQqPf58+cICQnB+++/j2+//RaVKlUC8OKL5smTJxg8eDDs7Oxw7NgxLFy4ELdv38bmzZsLXI8A8Ndff2Hr1q0YMmQILC0tsWDBAnTq1Ak3b96EnZ1dgfNeuXIFnTt3Rv/+/REWFoaffvoJ4eHh8PPzg7e3NwDgzp070hf2xIkTYW5ujv/+97967zKMjo7Gv//+i759+0KlUkmb/c+fP4+///5bK3R07doV1apVw4wZM3Dy5En897//haOjI2bNmiX1GTBgANatW4cePXqgcePG2L9/v9b7QhcnJycEBgZi06ZN0henWmRkJAwNDaWQ8u+//2L79u3o0qULqlWrhsTERPzwww8IDAzEhQsXivwXsr41Hz9+HEeOHEH37t3h4uKC69evY+nSpWjSpAkuXLiASpUq4cMPP8SIESOwYMECfPHFF/D09AQA6d+XPX36FE2aNMGVK1cwbNgwVKtWDZs3b0Z4eDhSU1O1vvD0ea/oS9/PlE6dOuH8+fMYPnw43N3dkZSUhOjoaNy8eVO6//HHH8PBwQETJkyAjY0Nrl+/rtfutJL+vMnrww8/hIuLCzZs2CB9hkdGRsLCwkLn85ueno7//ve/CA0NxcCBA5GRkYEVK1YgJCQEx44dQ926dQG8eN+EhoYiKChIeu1fvHgRhw8f1nq+1B48eIBmzZohOTkZhw4dQo0aNQqt/2Xq8Fq5cuVC+xb1u1btyy+/xJo1a/TeOlWtWjUpgE2YMEHe1ilBeklLSxMARLt27fTqf/r0aQFADBgwQKN97NixAoDYv3+/1Obm5iYAiCNHjkhte/fuFQCEmZmZuHHjhtT+ww8/CADiwIEDUltYWJgAIIYPHy615ebmilatWgkTExNx//59qf3Jkyca9WRlZYk6deqIpk2barQDEAYGBuL8+fNaYwMgIiIipPvW1tZi6NCh+a6LrKws4ejoKOrUqSOePn0qte/cuVMAEFOmTNEay/Tp0zUeo169esLPzy/fZQghRFJSkjAxMREff/yxyMnJkdoXLVokAIiffvpJaouIiBAANNZNfoYOHSp0vVWuXbsmAAg7OzuRnJwstf/f//2fACB+/fVXqS0oKEj4+PiIZ8+eSW25ubmicePGolatWgUu//79+1rrXE29viZMmKA17eXnWgghZsyYIRQKhcZrSr0u8gIgTExMxJUrV6S2f/75RwAQCxculNpWrlwpAIhr165JberX8x9//CG1JSUlCaVSKcaMGSO1DR8+XCgUCnHq1Cmp7eHDh8LW1lbrMXXRNb6ff/5Za9nq8fXr10+jb4cOHYSdnZ10X/2eHTJkiEa/Hj165Lv+81K/N8+ePavR7uXlpfH+evbsmcbrU4gXryWlUqnxule/vlauXKk1luLUrGt9xcTECABizZo1UtvmzZu1PmPUAgMDRWBgoHR/3rx5AoBYt26d1JaVlSUCAgKEhYWFSE9P1xiLPu8VXQ4cOKBRk76fKSkpKQKAmDNnTr6PvW3bNgFAHD9+vMAaXlZanzd5+44dO1bUrFlTmtagQQPRt29fIcSL92jez93nz5+LzMxMjcdKSUkRTk5OGq/9kSNHCisrK/H8+fN8a1C/r48fPy7u3bsnvL29RfXq1cX169cLrV/9XP3000/i/v374u7duyIqKkrUrFlTKBQKcezYsQLnL+p3rRAvPnNatWolhBCib9++wtTUVNy9e1ejns2bN+sc39WrV4WRkZEYMWKEND0wMFB4e3vrvXwhhOBuPj2lp6cDACwtLfXqv3v3bgDA6NGjNdrHjBkDAFr7e728vBAQECDd9/f3BwA0bdoUVatW1Wr/999/tZaZ98wO9W6arKws/P7771K7mZmZ9P+UlBSkpaXhgw8+0NolBwCBgYHw8vIqZKQvjgM4evRovptWT5w4gaSkJAwZMkTjeKtWrVrBw8ND53Fmn376qcb9Dz74QOeY8/r999+RlZWFUaNGaWy1GzhwIKysrErteLZu3bpp/LWl3pytrjc5ORn79+9H165dkZGRgQcPHuDBgwd4+PAhQkJCcPnyZZ1nlBSFri12eZ/rx48f48GDB2jcuDGEEFq7inUJDg7W+Av0nXfegZWVVaHPA/Di9axeD8CLLWy1a9fWmDcqKgoBAQHSX8wAYGtrK+2mLEze8T179gwPHjxAo0aNAEDn61nXa+rhw4fSe1v9nh0xYoRGv1GjRulVT8eOHWFkZITIyEip7dy5c7hw4QK6desmtSmVSun1mZOTg4cPH8LCwgK1a9fWWXdBilJz3vWVnZ2Nhw8fombNmrCxsSnycvMuX6VSITQ0VGozNjbGiBEj8OjRIxw6dEijf2HvFX3p+5liZmYGExMTHDx4ECkpKTofS70Fa+fOncjOzta7hrL4vOnRoweuXLmC48ePS//q2sUHvDh+UX1MW25uLpKTk/H8+XPUr19f4/m1sbHB48ePER0dXejyb9++jcDAQGRnZ+OPP/6Am5ub3rX369cPDg4OcHZ2RvPmzZGWloa1a9eiQYMGBc5X1O/al02aNAnPnz/Xe9dd9erV0bt3b/z444+4d+9esZYJ8JgpvVlZWQF4cXyQPm7cuAEDAwOtMzdUKhVsbGxw48YNjfa8gQkArK2tAQCurq4621/+YDAwMED16tU12t5++20A0Dg2YOfOnWjUqBFMTU1ha2sr7UZKS0vTGkO1atUKGyaAF/u3z507B1dXVzRs2BBTp07V+HBUj7V27dpa83p4eGitC1NTU+k4IbXKlSvn+2FY2HJMTExQvXp1reWUlJefO/WXhbreK1euQAiByZMnw8HBQeOm3iWkPkC1OIyMjODi4qLVfvPmTYSHh8PW1lY69iwwMBAAdD7fhY1LPbbCngd9571x44bOM5v0PdspOTkZI0eOhJOTE8zMzODg4CC9ZnWNr7DnSf2efXkXhq7XrS729vYICgrCpk2bpLbIyEgYGRlJu2eBF19033//PWrVqgWlUgl7e3s4ODjgzJkzej0veRWl5qdPn2LKlClwdXXVWG5qamqRl5t3+bVq1dIIE8D/dgsW9jn38nNQlOUChX+mKJVKzJo1C3v27IGTkxM+/PBDzJ49GwkJCVL/wMBAdOrUCdOmTYO9vT3atWuHlStXah3bqm8NJfl5U69ePXh4eGDDhg1Yv349VCoVmjZtmm//1atX45133oGpqSns7Ozg4OCAXbt2aTy/Q4YMwdtvv40WLVrAxcUF/fr10zoWUq13795ISkrCoUOH8NZbbxWp9ilTpiA6Ohrbtm1Dnz59kJaWpnVoSkJCgsYtKyuryN+1LytOOCpqANOFYUpPVlZWcHZ2xrlz54o0n74X2MvvjKj82sVLB5br488//0Tbtm1hamqKJUuWYPfu3YiOjkaPHj10Pl7ev2QL0rVrV/z7779YuHAhnJ2dMWfOHHh7e2PPnj1FrhHIf8yvqsKeo9zcXADA2LFjER0drfOmb4DQJe+WDrWcnBw0a9YMu3btwvjx47F9+3ZER0dLF4BU1yRnXKU1r766du2K5cuX49NPP8XWrVvx22+/SV8KusZXFjV1794dly5dwunTpwEAmzZtQlBQEOzt7aU+33zzDUaPHo0PP/wQ69atw969exEdHQ1vb2+9npfiGj58OL7++mt07doVmzZtwm+//Ybo6GjY2dmV6nLzKovn4GWjRo3CpUuXMGPGDJiammLy5Mnw9PSUts6qr0EUExODYcOG4c6dO+jXrx/8/Pzw6NGjUqtLXz169EBkZCQ2bNiAbt26ab3X1datWyddy2rFihWIiopCdHQ0mjZtqvH8Ojo64vTp09ixYwfatm2LAwcOoEWLFggLC9N6zI4dOyI1NbXQg9N18fHxQXBwMNq3b4/Vq1ejbdu2GDhwoHTtrFu3bqFKlSoatyNHjhT7uzavL7/8Es+fP9c4HrIg1atXR69evWRtnWKYKoLWrVvj6tWriImJKbSvm5sbcnNzcfnyZY32xMREpKamFmlzqT5yc3O1NpVfunQJAKQDx3/55ReYmppi79696NevH1q0aIHg4OASWX6VKlUwZMgQbN++HdeuXYOdnR2+/vprAJDGqusMkPj4+BJbF/ktJysrC9euXSv2cop7dXA19RZDY2NjBAcH67wVtEm7OMs/e/YsLl26hLlz52L8+PFo164dgoODZZ/+W5Lc3Nxw5coVrXZdbS9LSUnBvn37MGHCBEybNg0dOnRAs2bNtLbOFrWe3NxcrbONinLmUvv27WFiYoLIyEicPn0aly5dQvfu3TX6bNmyBR999BFWrFiB7t274+OPP0ZwcLDGGWClUfOWLVsQFhaGuXPnonPnzmjWrBnef/99reUW5fXm5uaGy5cva4Ux9RloJf05l3e5gP6fKTVq1MCYMWPw22+/4dy5c8jKysLcuXM1+jRq1Ahff/01Tpw4gfXr1+P8+fPYuHFjkWuQ+3nzsh49euDevXu4dOlSvrv4gBfPb/Xq1bF161b07t0bISEhCA4OxrNnz7T6mpiYoE2bNliyZIl0Ycw1a9ZovfeGDx+O6dOnY+bMmbLPeJs5cyaePXsmfS+oVCqtPyp9fX0BFO27VpcaNWqgV69e+OGHH4q8dUrfAPYyhqki+Pzzz2Fubo4BAwYgMTFRa/rVq1elBN+yZUsAwLx58zT6fPfddwCg1xlCRaU+9Rp48ZfeokWLYGxsjKCgIAAv/ipUKBQalxi4fv06tm/fXuxl5uTkaO0icHR0hLOzs7SZvH79+nB0dMSyZcs0Np3v2bMHFy9eLLF1ERwcDBMTEyxYsEDjL90VK1YgLS2t2MtRX7OpOF92wIv10aRJk3zf2Pfv3y9wfvXZeUVZvnoLQN71IIQo1l+YpSUkJAQxMTHSVhzgxa679evXFzqvrvEB2u+3omjRogUAaJ0FVJTHtLGxQUhICDZt2oSNGzfCxMQE7du31+hjaGioVffmzZuLddxcUWrWtdyFCxdqXXKkKK/3li1bIiEhQeM4sefPn2PhwoWwsLCQdiuXNH0/U548eaIVJmrUqAFLS0tpvpSUFK31oj6Or6BdfaX1efOyGjVqYN68eZgxYwYaNmyYbz9d74mjR49qBZKXLx1iYGAgnV2oa7yTJ0/G2LFjMXHiRCxdulTWODp16oRVq1YhISEBpqamWn9Uqnf7FuW7Nj+TJk1CdnY2Zs+erXd96gCWdzewvnhphCKoUaOGtKnV09NT46qsR44ckU4JBgBfX1+EhYXhxx9/RGpqKgIDA3Hs2DGsXr0a7du3x0cffVSitZmamiIqKgphYWHw9/fHnj17sGvXLnzxxRfS8UetWrXCd999h+bNm6NHjx5ISkrC4sWLUbNmTZw5c6ZYy83IyICLiws6d+4MX19fWFhY4Pfff8fx48elv/yMjY0xa9Ys9O3bF4GBgQgNDZVOY3Z3d8dnn31WIuvAwcEBEydOxLRp09C8eXO0bdsW8fHxWLJkCRo0aIBevXoV63H9/PwAvDjINyQkBIaGhlpbGwqzePFivP/++/Dx8cHAgQNRvXp1JCYmIiYmBrdv3y7wGkNmZmbw8vJCZGQk3n77bdja2qJOnToF/tyBh4cHatSogbFjx+LOnTuwsrLCL7/8UuRjU0rT559/jnXr1qFZs2YYPny4dGmEqlWrIjk5ucAtJFZWVtLxL9nZ2Xjrrbfw22+/4dq1a8Wup27duggNDcWSJUuQlpaGxo0bY9++fXptKcurW7du6NWrF5YsWYKQkBCtCzW2bt0a06dPR9++fdG4cWOcPXsW69evL9ZWtaLU3Lp1a6xduxbW1tbw8vJCTEwMfv/9d63LXNStWxeGhoaYNWsW0tLSoFQq0bRpUzg6Omo95qBBg/DDDz8gPDwcsbGxcHd3x5YtW3D48GHMmzev2AcRF0bfz5RLly4hKCgIXbt2hZeXF4yMjLBt2zYkJiZK7+HVq1djyZIl6NChA2rUqIGMjAwsX74cVlZW0h/FupTW540u+V2yIK/WrVtj69at6NChA1q1aoVr165h2bJl8PLy0thdOWDAACQnJ6Np06ZwcXHBjRs3sHDhQtStWzffS2DMmTMHaWlpGDp0KCwtLYs9tnHjxmHTpk2YN29egVu6ivJdW9Bj9OrVS+uaZwX58ssvsXbtWsTHx0uXcNFbkc79IyGEEJcuXRIDBw4U7u7uwsTERFhaWor33ntPLFy4UOPU9+zsbDFt2jRRrVo1YWxsLFxdXcXEiRM1+giheVpnXnjp1Fch/neKcd5TfcPCwoS5ubm4evWq+Pjjj0WlSpWEk5OTiIiI0DoFe8WKFaJWrVpCqVQKDw8PsXLlynxPjc/vcgfIc8p1ZmamGDdunPD19RWWlpbC3Nxc+Pr6iiVLlmjNFxkZKerVqyeUSqWwtbUVPXv2FLdv39boox7Ly3TVmJ9FixYJDw8PYWxsLJycnMTgwYNFSkqKzsfT51Tl58+fi+HDhwsHBwehUCikOnQ9F2p515Ha1atXRZ8+fYRKpRLGxsbirbfeEq1btxZbtmwptIYjR44IPz8/YWJiovHY+a0vIYS4cOGCCA4OFhYWFsLe3l4MHDhQurxBQafbq+vX9fy7ubmJsLAw6X5+l0bQ9Xp++bR6IYQ4deqU+OCDD4RSqRQuLi5ixowZYsGCBQKASEhIKHCd3L59W3To0EHY2NgIa2tr0aVLF3H37l2tdZ/fc62r9qdPn4oRI0YIOzs7YW5uLtq0aSNu3bql16UR1NLT04WZmZnWJQPUnj17JsaMGSOqVKkizMzMxHvvvSdiYmK01o8+l0YoSs0pKSmib9++wt7eXlhYWIiQkBARFxen9ZwKIcTy5ctF9erVhaGhocYlCXQ9h4mJidLjmpiYCB8fH42a845F3/fKy16+NIJaYZ8pDx48EEOHDhUeHh7C3NxcWFtbC39/f7Fp0yapz8mTJ0VoaKioWrWqUCqVwtHRUbRu3VqcOHGiwJrUSvrzRt++L79Hc3NzxTfffCPc3NyEUqkU9erVEzt37hRhYWHCzc1N6rdlyxbx8ccfC0dHR2FiYiKqVq0qPvnkE3Hv3j2pT95LB6jl5OSI0NBQYWRkJLZv355vXbouRZBXkyZNhJWVlUhNTS1sVej9XZvfZ87ly5el13B+l0Z4mfpyM0W9NIJCiFI88o/KRHh4OLZs2fJKHCxJJNeoUaPwww8/4NGjRxXuZAQiejPxmCkiKjcv/7TNw4cPsXbtWrz//vsMUkRUYfCYKSIqNwEBAWjSpAk8PT2RmJiIFStWID09HZMnTy7v0oiI9MYwRUTlpmXLltiyZQt+/PFHKBQKvPvuu1ixYgU+/PDD8i6NiEhvPGaKiIiISAYeM0VEREQkA8MUERERkQw8ZqoE5Obm4u7du7C0tJT90yNERERUNoQQyMjIgLOzc76/e6gPhqkScPfuXbi6upZ3GURERFQMt27dgouLS7HnZ5gqAeqfTLh16xasrKzKuRoiIiLSR3p6OlxdXWX/9BHDVAlQ79qzsrJimCIiIqpg5B6iwwPQiYiIiGRgmCIiIiKSgWGKiIiISAYeM0VE9AbKyclBdnZ2eZdBVKqMjY3L5EfTGaaIiN4gQggkJCQgNTW1vEshKhM2NjZQqVSleh1IhikiojeIOkg5OjqiUqVKvNAwvbaEEHjy5AmSkpIAAFWqVCm1ZTFMERG9IXJycqQgZWdnV97lEJU6MzMzAEBSUhIcHR1LbZcfD0AnInpDqI+RqlSpUjlXQlR21K/30jxGkGGKiOgNw1179CYpi9c7wxQRERGRDAxTRERU4SkUCmzfvr28y9CpSZMmGDVqlHTf3d0d8+bNK3CekhrPq7xeXicMU0RE9Eq7f/8+Bg8ejKpVq0KpVEKlUiEkJASHDx+W+ty7dw8tWrQAAFy/fh0KhQKnT5+Wtdw2bdqgefPmOqf9+eefUCgUOHPmTJEf9/jx4xg0aJCs2l42depU1K1bV6s973opbU+fPoWtrS3s7e2RmZmpNd3d3R0KhQIKhQLm5uZ49913sXnzZmn6qlWrpOnqm6mpqcZjCCEwZcoUVKlSBWZmZggODsbly5dLfWyFYZgiIqJXWqdOnXDq1CmsXr0aly5dwo4dO9CkSRM8fPhQ6qNSqaBUKkt0uf3790d0dDRu376tNW3lypWoX78+3nnnnSI/roODQ5mdBFAa6yU/v/zyC7y9veHh4ZHv1rDp06fj3r17OHXqFBo0aIBu3brhyJEj0nQrKyvcu3dPut24cUNj/tmzZ2PBggVYtmwZjh49CnNzc4SEhODZs2elObTCCZItLS1NABBpaWnlXQoRUb6ePn0qLly4IJ4+fVrepegtJSVFABAHDx4ssB8AsW3bNun/eW+BgYFSv+XLlwsPDw+hVCpF7dq1xeLFi/N9zOzsbOHk5CS++uorjfaMjAxhYWEhli5dKh48eCC6d+8unJ2dhZmZmahTp47YsGGDRv/AwEAxcuRI6b6bm5v4/vvvpfuXLl0SH3zwgVAqlcLT01P89ttvGuMRQojPP/9c1KpVS5iZmYlq1aqJSZMmiaysLCGEECtXrtQa88qVK7XWixBCnDlzRnz00UfC1NRU2NraioEDB4qMjAxpelhYmGjXrp2YM2eOUKlUwtbWVgwZMkRaVkGaNGkili1bJpYuXSqaNWumNf3lcWdnZ4tKlSqJCRMmSOOwtrbO9/Fzc3OFSqUSc+bMkdpSU1OFUqkUP//8c77zFfS6L6nvb15niojoTSYE8ORJ2S+3UiVAj7OsLCwsYGFhge3bt6NRo0Z6bWU5duwYGjZsiN9//x3e3t4wMTEBAKxfvx5TpkzBokWLUK9ePZw6dQoDBw6Eubk5wsLCtB7HyMgIffr0wapVq/Dll19KZ4Vt3rwZOTk5CA0NxaNHj+Dn54fx48fDysoKu3btQu/evVGjRg00bNiw0Fpzc3PRsWNHODk54ejRo0hLS9M4vkrN0tISq1atgrOzM86ePYuBAwfC0tISn3/+Obp164Zz584hKioKv//+OwDA2tpa6zEeP36MkJAQBAQE4Pjx40hKSsKAAQMwbNgwrFq1Sup34MABVKlSBQcOHMCVK1fQrVs31K1bFwMHDsx3HFevXkVMTAy2bt0KIQQ+++wz3LhxA25ubvnOY2RkBGNjY2RlZUltjx49gpubG3Jzc/Huu+/im2++gbe3NwDg2rVrSEhIQHBwsNTf2toa/v7+iImJQffu3fNdVqmTFcVICMEtU0RUMej8C/3RIyFeRKqyvT16pHfdW7ZsEZUrVxampqaicePGYuLEieKff/7R6IM8W2CuXbsmAIhTp05p9KlRo4bWVqOvvvpKBAQE5LvsixcvCgDiwIEDUtsHH3wgevXqle88rVq1EmPGjJHuF7Rlau/evcLIyEjcuXNHmr5nzx6tLUovmzNnjvDz85PuR0RECF9fX61+eR/nxx9/FJUrVxaP8qz7Xbt2CQMDA5GQkCCEeLFlys3NTTx//lzq06VLF9GtW7d8axFCiC+++EK0b99eut+uXTsRERGh0SfvuDMzM8U333wjAIidO3cKIYQ4cuSIWL16tTh16pQ4ePCgaN26tbCyshK3bt0SQghx+PBhAUDcvXtX43G7dOkiunbtmm9tZbFlisdMERHRK61Tp064e/cuduzYgebNm+PgwYN49913NbamFObx48e4evUq+vfvL23tsrCwwH/+8x9cvXo13/k8PDzQuHFj/PTTTwCAK1eu4M8//0T//v0BvLiq/FdffQUfHx/Y2trCwsICe/fuxc2bN/Wq6+LFi3B1dYWzs7PUFhAQoNUvMjIS7733HlQqFSwsLDBp0iS9l5F3Wb6+vjA3N5fa3nvvPeTm5iI+Pl5q8/b21rhSeJUqVaSfZNElJycHq1evRq9evaS2Xr16YdWqVcjNzdXoO378eFhYWKBSpUqYNWsWZs6ciVatWknj7tOnD+rWrYvAwEBs3boVDg4O+OGHH4o0zvLA3XxERG+ySpWAR4/KZ7lFYGpqimbNmqFZs2aYPHkyBgwYgIiICISHh+s1/6P/P8bly5fD399fY1phPzHSv39/DB8+HIsXL8bKlStRo0YNBAYGAgDmzJmD+fPnY968efDx8YG5uTlGjRqlsetKrpiYGPTs2RPTpk1DSEgIrK2tsXHjRsydO7fElpGXsbGxxn2FQqEVivLau3cv7ty5g27dumm05+TkYN++fWjWrJnUNm7cOISHh8PCwgJOTk4FXlDT2NgY9erVw5UrVwC8OJgeABITEzV+Zy8xMVHnmYxliVumiIjeZAoFYG5e9jeZV6X28vLC48ePdU5THyOVk5MjtTk5OcHZ2Rn//vsvatasqXGrVq1agcvq2rUrDAwMsGHDBqxZswb9+vWTQsDhw4fRrl079OrVC76+vqhevTouXbqk9zg8PT1x69Yt3Lt3T2r7+++/NfocOXIEbm5u+PLLL1G/fn3UqlVL6yw3ExMTjfHmt6x//vlHY70dPnwYBgYGqF27tt41v2zFihXo3r07Tp8+rXHr3r07VqxYodHX3t4eNWvWhEqlKvTK5Dk5OTh79qwUnKpVqwaVSoV9+/ZJfdLT03H06FGdW/PKErdMERHRK+vhw4fo0qUL+vXrh3feeQeWlpY4ceIEZs+ejXbt2umcx9HREWZmZoiKioKLiwtMTU1hbW2NadOmYcSIEbC2tkbz5s2RmZmJEydOICUlBaNHj863BgsLC3Tr1g0TJ05Eenq6xtawWrVqYcuWLThy5AgqV66M7777DomJifDy8tJrfMHBwXj77bcRFhaGOXPmID09HV9++aVGn1q1auHmzZvYuHEjGjRogF27dmHbtm0afdzd3XHt2jWcPn0aLi4usLS01DpYv2fPnoiIiEBYWBimTp2K+/fvY/jw4ejduzecnJz0qvdl9+/fx6+//oodO3agTp06GtP69OmDDh06IDk5Gba2toU+1vTp09GoUSPUrFkTqampmDNnDm7cuIEBAwYAeLGFbNSoUfjPf/6DWrVqoVq1apg8eTKcnZ3Rvn37YtVfUrhlioiIXlkWFhbw9/fH999/jw8//BB16tTB5MmTMXDgQCxatEjnPEZGRliwYAF++OEHODs7S6FrwIAB+O9//4uVK1fCx8cHgYGBWLVqVaFbpoAXu/pSUlIQEhKicXzTpEmT8O677yIkJARNmjSBSqUq0he7gYEBtm3bhqdPn6Jhw4YYMGAAvv76a40+bdu2xWeffYZhw4ahbt26OHLkCCZPnqzRp1OnTmjevDk++ugjODg44Oeff9ZaVqVKlbB3714kJyejQYMG6Ny5M4KCgvJdj/pYs2YNzM3NERQUpDUtKCgIZmZmWLdunV6PlZKSgoEDB8LT0xMtW7ZEeno6jhw5ohFMP//8cwwfPhyDBg1CgwYN8OjRI0RFRWld3LOsKYQQolwreA2kp6fD2toaaWlpsLKyKu9yiIh0evbsGa5du4Zq1aqV+5cPUVkp6HVfUt/f3DJFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVE9IbheUf0JimL1zvDFBHRG0J9Zesn5fHDxkTlRP16f/nK7iWJF+0kInpDGBoawsbGRvqdtUqVKhV6FWqiikoIgSdPniApKQk2NjaF/myQHAxTRERvEPXvmxX0w7VErxMbGxvpdV9aGKaIiN4gCoUCVapUgaOjI7Kzs8u7HKJSZWxsXKpbpNQYpoiI3kCGhoZl8iVD9CbgAehEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJEOFC1OLFy+Gu7s7TE1N4e/vj2PHjhXYf/PmzfDw8ICpqSl8fHywe/fufPt++umnUCgUmDdvXglXTURERK+rChWmIiMjMXr0aERERODkyZPw9fVFSEgIkpKSdPY/cuQIQkND0b9/f5w6dQrt27dH+/btce7cOa2+27Ztw99//w1nZ+fSHgYRERG9RipUmPruu+8wcOBA9O3bF15eXli2bBkqVaqEn376SWf/+fPno3nz5hg3bhw8PT3x1Vdf4d1338WiRYs0+t25cwfDhw/H+vXrYWxsXBZDISIiotdEhQlTWVlZiI2NRXBwsNRmYGCA4OBgxMTE6JwnJiZGoz8AhISEaPTPzc1F7969MW7cOHh7e5dO8URERPTaMirvAvT14MED5OTkwMnJSaPdyckJcXFxOudJSEjQ2T8hIUG6P2vWLBgZGWHEiBF615KZmYnMzEzpfnp6ut7zEhER0eulwmyZKg2xsbGYP38+Vq1aBYVCofd8M2bMgLW1tXRzdXUtxSqJiIjoVVZhwpS9vT0MDQ2RmJio0Z6YmAiVSqVzHpVKVWD/P//8E0lJSahatSqMjIxgZGSEGzduYMyYMXB3d8+3lokTJyItLU263bp1S97giIiIqMKqMGHKxMQEfn5+2Ldvn9SWm5uLffv2ISAgQOc8AQEBGv0BIDo6Wurfu3dvnDlzBqdPn5Zuzs7OGDduHPbu3ZtvLUqlElZWVho3IiIiejNVmGOmAGD06NEICwtD/fr10bBhQ8ybNw+PHz9G3759AQB9+vTBW2+9hRkzZgAARo4cicDAQMydOxetWrXCxo0bceLECfz4448AADs7O9jZ2Wksw9jYGCqVCrVr1y7bwREREVGFVKHCVLdu3XD//n1MmTIFCQkJqFu3LqKioqSDzG/evAkDg/9tbGvcuDE2bNiASZMm4YsvvkCtWrWwfft21KlTp7yGQERERK8ZhRBClHcRFV16ejqsra2RlpbGXX5EREQVREl9f1eYY6aIiIiIXkUMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJEOFC1OLFy+Gu7s7TE1N4e/vj2PHjhXYf/PmzfDw8ICpqSl8fHywe/duaVp2djbGjx8PHx8fmJubw9nZGX369MHdu3dLexhERET0mqhQYSoyMhKjR49GREQETp48CV9fX4SEhCApKUln/yNHjiA0NBT9+/fHqVOn0L59e7Rv3x7nzp0DADx58gQnT57E5MmTcfLkSWzduhXx8fFo27ZtWQ6LiIiIKjCFEEKUdxH68vf3R4MGDbBo0SIAQG5uLlxdXTF8+HBMmDBBq3+3bt3w+PFj7Ny5U2pr1KgR6tati2XLlulcxvHjx9GwYUPcuHEDVatW1auu9PR0WFtbIy0tDVZWVsUYGREREZW1kvr+rjBbprKyshAbG4vg4GCpzcDAAMHBwYiJidE5T0xMjEZ/AAgJCcm3PwCkpaVBoVDAxsamROomIiKi15tReRegrwcPHiAnJwdOTk4a7U5OToiLi9M5T0JCgs7+CQkJOvs/e/YM48ePR2hoaIEJNTMzE5mZmdL99PR0fYdBREREr5kKs2WqtGVnZ6Nr164QQmDp0qUF9p0xYwasra2lm6uraxlVSURERK+aChOm7O3tYWhoiMTERI32xMREqFQqnfOoVCq9+quD1I0bNxAdHV3oftOJEyciLS1Nut26dasYIyIiIqLXQYUJUyYmJvDz88O+ffukttzcXOzbtw8BAQE65wkICNDoDwDR0dEa/dVB6vLly/j9999hZ2dXaC1KpRJWVlYaNyIiInozVZhjpgBg9OjRCAsLQ/369dGwYUPMmzcPjx8/Rt++fQEAffr0wVtvvYUZM2YAAEaOHInAwEDMnTsXrVq1wsaNG3HixAn8+OOPAF4Eqc6dO+PkyZPYuXMncnJypOOpbG1tYWJiUj4DJSIiogqjQoWpbt264f79+5gyZQoSEhJQt25dREVFSQeZ37x5EwYG/9vY1rhxY2zYsAGTJk3CF198gVq1amH79u2oU6cOAODOnTvYsWMHAKBu3boayzpw4ACaNGlSJuMiIiKiiqtCXWfqVcXrTBEREVU8b9x1poiIiIheRQxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyGBV1hoSEBBw9ehQJCQkAAJVKBX9/f6hUqhIvjoiIiOhVp3eYevz4MT755BNs3LgRCoUCtra2AIDk5GQIIRAaGooffvgBlSpVKrViiYiIiF41eu/mGzlyJI4dO4Zdu3bh2bNnSExMRGJiIp49e4bdu3fj2LFjGDlyZGnWSkRERPTKUQghhD4dK1eujF27dqFx48Y6px8+fBitW7dGSkpKiRZYEaSnp8Pa2hppaWmwsrIq73KIiIhIDyX1/a33lqnc3FyYmJjkO93ExAS5ubnFLoSIiIioItI7TLVu3RqDBg3CqVOntKadOnUKgwcPRps2bUq0OCIiIqJXnd5hatGiRXBycoKfnx/s7Ozg6ekJT09P2NnZoX79+nB0dMSiRYtKs1YiIiKiV47eZ/NVrlwZe/bsQVxcHGJiYjQujRAQEAAPD49SK5KIiIjoVVXk60x5eHgwOBERERH9f0UOU2pCCBw8eBBXrlxBlSpVEBISAmNj45KsjYiIiOiVp3eYatmyJX7++WdYW1sjOTkZLVu2xLFjx2Bvb4+HDx/i7bffxh9//AEHB4fSrJeIiIjolaL3AehRUVHIzMwEAEyaNAkZGRm4evUqkpKScOPGDZibm2PKlCmlVqja4sWL4e7uDlNTU/j7++PYsWMF9t+8eTM8PDxgamoKHx8f7N69W2O6EAJTpkxBlSpVYGZmhuDgYFy+fLk0h0BERESvkWL90PH+/fsxY8YMVKtWDQDg4uKCWbNmYe/evSVa3MsiIyMxevRoRERE4OTJk/D19UVISAiSkpJ09j9y5AhCQ0PRv39/nDp1Cu3bt0f79u1x7tw5qc/s2bOxYMECLFu2DEePHoW5uTlCQkLw7NmzUh0LERERvR70vgK6gYEBEhMT4eDgACcnJ+zfvx/e3t7S9Bs3bqB27dqlGkL8/f3RoEED6RIMubm5cHV1xfDhwzFhwgSt/t26dcPjx4+xc+dOqa1Ro0aoW7culi1bBiEEnJ2dMWbMGIwdOxYAkJaWBicnJ6xatQrdu3fXqy5eAZ2IiKjiKfMroANAeHg4OnbsiOzsbFy7dk1jWkJCAmxsbIpdSGGysrIQGxuL4OBgqc3AwADBwcGIiYnROU9MTIxGfwAICQmR+l+7dg0JCQkafaytreHv75/vYwJAZmYm0tPTNW5ERET0ZtI7TIWFhcHR0RHW1tZo164dnjx5ojH9l19+Qd26dUu6PsmDBw+Qk5MDJycnjXYnJyfpmlcvS0hIKLC/+t+iPCYAzJgxA9bW1tLN1dW1yOMhIiKi14PeZ/OtXLmywOkREREwNDSUXVBFMHHiRIwePVq6n56ezkBFRET0hirSdabS09Nx9OhRZGVloWHDhhqXQTA3Ny/x4vKyt7eHoaEhEhMTNdoTExOhUql0zqNSqQrsr/43MTERVapU0ehT0FY2pVIJpVJZnGEQERHRa0bv3XynT5+Gh4cHQkJC0KZNG9SsWbPUz97Ly8TEBH5+fti3b5/Ulpubi3379iEgIEDnPAEBARr9ASA6OlrqX61aNahUKo0+6sCY32MSERER5aV3mBo/fjyqVauGw4cPIzY2FkFBQRg2bFhp1qZl9OjRWL58OVavXo2LFy9i8ODBePz4Mfr27QsA6NOnDyZOnCj1HzlyJKKiojB37lzExcVh6tSpOHHihFS3QqHAqFGj8J///Ac7duzA2bNn0adPHzg7O6N9+/ZlOjYiIiKqmPTezRcbG4vffvsN7777LgDgp59+gq2tLdLT08vscgDdunXD/fv3MWXKFCQkJKBu3bqIioqSDiC/efMmDAz+lw8bN26MDRs2YNKkSfjiiy9Qq1YtbN++HXXq1JH6fP7553j8+DEGDRqE1NRUvP/++4iKioKpqWmZjImIiIgqtiJdZyohIQGOjo5Sm6WlJc6cOSNdvPNNxetMERERVTwl9f1dpAPQL1y4oHHJACEELl68iIyMDKntnXfeKXYxRERERBVNkbZMKRQKFNRdoVAgJyenxIqrKLhlioiIqOIp8y1TL1/xXJe8W6iIiIiI3gR6hyk3Nzed7RkZGfj555+xYsUKnDhx4o3cMkVERERvriL9Nl9ef/zxB8LCwlClShV8++23+Oijj/D333+XZG1EREREr7wiHYCekJCAVatWYcWKFUhPT0fXrl2RmZmJ7du3w8vLq7RqJCIiInpl6b1lqk2bNqhduzbOnDmDefPm4e7du1i4cGFp1kZERET0ytN7y9SePXswYsQIDB48GLVq1SrNmoiIiIgqDL23TP3111/IyMiAn58f/P39sWjRIjx48KA0ayMiIiJ65ekdpho1aoTly5fj3r17+OSTT7Bx40Y4OzsjNzcX0dHRvCwCERERvZH0vminLvHx8VixYgXWrl2L1NRUNGvWDDt27CjJ+ioEXrSTiIio4imp7+9iXxoBAGrXro3Zs2fj9u3b+Pnnn+U8FBEREVGFJGvLFL3ALVNEREQVzyuxZYqIiIjoTccwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMFSZMJScno2fPnrCysoKNjQ369++PR48eFTjPs2fPMHToUNjZ2cHCwgKdOnVCYmKiNP2ff/5BaGgoXF1dYWZmBk9PT8yfP7+0h0JERESvkQoTpnr27Inz588jOjoaO3fuxB9//IFBgwYVOM9nn32GX3/9FZs3b8ahQ4dw9+5ddOzYUZoeGxsLR0dHrFu3DufPn8eXX36JiRMnYtGiRaU9HCIiInpNKIQQoryLKMzFixfh5eWF48ePo379+gCAqKgotGzZErdv34azs7PWPGlpaXBwcMCGDRvQuXNnAEBcXBw8PT0RExODRo0a6VzW0KFDcfHiRezfv1/v+tLT02FtbY20tDRYWVkVY4RERERU1krq+7tCbJmKiYmBjY2NFKQAIDg4GAYGBjh69KjOeWJjY5GdnY3g4GCpzcPDA1WrVkVMTEy+y0pLS4OtrW2B9WRmZiI9PV3jRkRERG+mChGmEhIS4OjoqNFmZGQEW1tbJCQk5DuPiYkJbGxsNNqdnJzynefIkSOIjIwsdPfhjBkzYG1tLd1cXV31HwwRERG9Vso1TE2YMAEKhaLAW1xcXJnUcu7cObRr1w4RERH4+OOPC+w7ceJEpKWlSbdbt26VSY1ERET06jEqz4WPGTMG4eHhBfapXr06VCoVkpKSNNqfP3+O5ORkqFQqnfOpVCpkZWUhNTVVY+tUYmKi1jwXLlxAUFAQBg0ahEmTJhVat1KphFKpLLQfERERvf7KNUw5ODjAwcGh0H4BAQFITU1FbGws/Pz8AAD79+9Hbm4u/P39dc7j5+cHY2Nj7Nu3D506dQIAxMfH4+bNmwgICJD6nT9/Hk2bNkVYWBi+/vrrEhgVERERvUkqxNl8ANCiRQskJiZi2bJlyM7ORt++fVG/fn1s2LABAHDnzh0EBQVhzZo1aNiwIQBg8ODB2L17N1atWgUrKysMHz4cwItjo4AXu/aaNm2KkJAQzJkzR1qWoaGhXiFPjWfzERERVTwl9f1drlumimL9+vUYNmwYgoKCYGBggE6dOmHBggXS9OzsbMTHx+PJkydS2/fffy/1zczMREhICJYsWSJN37JlC+7fv49169Zh3bp1UrubmxuuX79eJuMiIiKiiq3CbJl6lXHLFBERUcXzRl1nioiIiOhVxTBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyVJgwlZycjJ49e8LKygo2Njbo378/Hj16VOA8z549w9ChQ2FnZwcLCwt06tQJiYmJOvs+fPgQLi4uUCgUSE1NLYUREBER0euowoSpnj174vz584iOjsbOnTvxxx9/YNCgQQXO89lnn+HXX3/F5s2bcejQIdy9excdO3bU2bd///545513SqN0IiIieo0phBCivIsozMWLF+Hl5YXjx4+jfv36AICoqCi0bNkSt2/fhrOzs9Y8aWlpcHBwwIYNG9C5c2cAQFxcHDw9PRETE4NGjRpJfZcuXYrIyEhMmTIFQUFBSElJgY2Njd71paenw9raGmlpabCyspI3WCIiIioTJfX9XSG2TMXExMDGxkYKUgAQHBwMAwMDHD16VOc8sbGxyM7ORnBwsNTm4eGBqlWrIiYmRmq7cOECpk+fjjVr1sDAQL/VkZmZifT0dI0bERERvZkqRJhKSEiAo6OjRpuRkRFsbW2RkJCQ7zwmJiZaW5icnJykeTIzMxEaGoo5c+agatWqetczY8YMWFtbSzdXV9eiDYiIiIheG+UapiZMmACFQlHgLS4urtSWP3HiRHh6eqJXr15Fni8tLU263bp1q5QqJCIioledUXkufMyYMQgPDy+wT/Xq1aFSqZCUlKTR/vz5cyQnJ0OlUumcT6VSISsrC6mpqRpbpxITE6V59u/fj7Nnz2LLli0AAPXhY/b29vjyyy8xbdo0nY+tVCqhVCr1GSIRERG95so1TDk4OMDBwaHQfgEBAUhNTUVsbCz8/PwAvAhCubm58Pf31zmPn58fjI2NsW/fPnTq1AkAEB8fj5s3byIgIAAA8Msvv+Dp06fSPMePH0e/fv3w559/okaNGnKHR0RERG+Acg1T+vL09ETz5s0xcOBALFu2DNnZ2Rg2bBi6d+8uncl3584dBAUFYc2aNWjYsCGsra3Rv39/jB49Gra2trCyssLw4cMREBAgncn3cmB68OCBtLyinM1HREREb64KEaYAYP369Rg2bBiCgoJgYGCATp06YcGCBdL07OxsxMfH48mTJ1Lb999/L/XNzMxESEgIlixZUh7lExER0WuqQlxn6lXH60wRERFVPG/UdaaIiIiIXlUMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJYFTeBbwOhBAAgPT09HKuhIiIiPSl/t5Wf48XF8NUCcjIyAAAuLq6lnMlREREVFQZGRmwtrYu9vwKITeOEXJzc3H37l1YWlpCoVCUdznlKj09Ha6urrh16xasrKzKu5zXFtdz2eG6Lhtcz2WD61mTEAIZGRlwdnaGgUHxj3zilqkSYGBgABcXl/Iu45ViZWXFN2oZ4HouO1zXZYPruWxwPf+PnC1SajwAnYiIiEgGhikiIiIiGRimqEQplUpERERAqVSWdymvNa7nssN1XTa4nssG13Pp4AHoRERERDJwyxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBVZcnIyevbsCSsrK9jY2KB///549OhRgfM8e/YMQ4cOhZ2dHSwsLNCpUyckJibq7Pvw4UO4uLhAoVAgNTW1FEZQMZTGev7nn38QGhoKV1dXmJmZwdPTE/Pnzy/tobxSFi9eDHd3d5iamsLf3x/Hjh0rsP/mzZvh4eEBU1NT+Pj4YPfu3RrThRCYMmUKqlSpAjMzMwQHB+Py5culOYQKoSTXc3Z2NsaPHw8fHx+Ym5vD2dkZffr0wd27d0t7GK+8kn495/Xpp59CoVBg3rx5JVz1a0gQFVHz5s2Fr6+v+Pvvv8Wff/4patasKUJDQwuc59NPPxWurq5i37594sSJE6JRo0aicePGOvu2a9dOtGjRQgAQKSkppTCCiqE01vOKFSvEiBEjxMGDB8XVq1fF2rVrhZmZmVi4cGFpD+eVsHHjRmFiYiJ++ukncf78eTFw4EBhY2MjEhMTdfY/fPiwMDQ0FLNnzxYXLlwQkyZNEsbGxuLs2bNSn5kzZwpra2uxfft28c8//4i2bduKatWqiadPn5bVsF45Jb2eU1NTRXBwsIiMjBRxcXEiJiZGNGzYUPj5+ZXlsF45pfF6Vtu6davw9fUVzs7O4vvvvy/lkVR8DFNUJBcuXBAAxPHjx6W2PXv2CIVCIe7cuaNzntTUVGFsbCw2b94stV28eFEAEDExMRp9lyxZIgIDA8W+ffve6DBV2us5ryFDhoiPPvqo5Ip/hTVs2FAMHTpUup+TkyOcnZ3FjBkzdPbv2rWraNWqlUabv7+/+OSTT4QQQuTm5gqVSiXmzJkjTU9NTRVKpVL8/PPPpTCCiqGk17Mux44dEwDEjRs3SqboCqi01vPt27fFW2+9Jc6dOyfc3NwYpvTA3XxUJDExMbCxsUH9+vWltuDgYBgYGODo0aM654mNjUV2djaCg4OlNg8PD1StWhUxMTFS24ULFzB9+nSsWbNG1g9Ovg5Kcz2/LC0tDba2tiVX/CsqKysLsbGxGuvHwMAAwcHB+a6fmJgYjf4AEBISIvW/du0aEhISNPpYW1vD39+/wHX+OiuN9axLWloaFAoFbGxsSqTuiqa01nNubi569+6NcePGwdvbu3SKfw292d9YVGQJCQlwdHTUaDMyMoKtrS0SEhLyncfExETrQ8/JyUmaJzMzE6GhoZgzZw6qVq1aKrVXJKW1nl925MgRREZGYtCgQSVS96vswYMHyMnJgZOTk0Z7QesnISGhwP7qf4vymK+70ljPL3v27BnGjx+P0NDQN/bHektrPc+aNQtGRkYYMWJEyRf9GmOYIgDAhAkToFAoCrzFxcWV2vInTpwIT09P9OrVq9SW8Soo7/Wc17lz59CuXTtERETg448/LpNlEsmVnZ2Nrl27QgiBpUuXlnc5r5XY2FjMnz8fq1atgkKhKO9yKhSj8i6AXg1jxoxBeHh4gX2qV68OlUqFpKQkjfbnz58jOTkZKpVK53wqlQpZWVlITU3V2GqSmJgozbN//36cPXsWW7ZsAfDiDCkAsLe3x5dffolp06YVc2SvlvJez2oXLlxAUFAQBg0ahEmTJhVrLBWNvb09DA0Ntc4i1bV+1FQqVYH91f8mJiaiSpUqGn3q1q1bgtVXHKWxntXUQerGjRvYv3//G7tVCiid9fznn38iKSlJY+9ATk4OxowZg3nz5uH69eslO4jXSXkftEUVi/rA6BMnTkhte/fu1evA6C1btkhtcXFxGgdGX7lyRZw9e1a6/fTTTwKAOHLkSL5nprzOSms9CyHEuXPnhKOjoxg3blzpDeAV1bBhQzFs2DDpfk5OjnjrrbcKPGC3devWGm0BAQFaB6B/++230vS0tDQegF7C61kIIbKyskT79u2Ft7e3SEpKKp3CK5iSXs8PHjzQ+Bw+e/ascHZ2FuPHjxdxcXGlN5DXAMMUFVnz5s1FvXr1xNGjR8Vff/0latWqpXHK/u3bt0Xt2rXF0aNHpbZPP/1UVK1aVezfv1+cOHFCBAQEiICAgHyXceDAgTf6bD4hSmc9nz17Vjg4OIhevXqJe/fuSbc35ctp48aNQqlUilWrVokLFy6IQYMGCRsbG5GQkCCEEKJ3795iwoQJUv/Dhw8LIyMj8e2334qLFy+KiIgInZdGsLGxEf/3f/8nzpw5I9q1a8dLI5Twes7KyhJt27YVLi4u4vTp0xqv3czMzHIZ46ugNF7PL+PZfPphmKIie/jwoQgNDRUWFhbCyspK9O3bV2RkZEjTr127JgCIAwcOSG1Pnz4VQ4YMEZUrVxaVKlUSHTp0EPfu3ct3GQxTpbOeIyIiBACtm5ubWxmOrHwtXLhQVK1aVZiYmIiGDRuKv//+W5oWGBgowsLCNPpv2rRJvP3228LExER4e3uLXbt2aUzPzc0VkydPFk5OTkKpVIqgoCARHx9fFkN5pZXkela/1nXd8r7+30Ql/Xp+GcOUfhRC/P+DU4iIiIioyHg2HxEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRUTl7vr161AoFDh9+nR5lyKJi4tDo0aNYGpqWuF+Z8/d3R3z5s0r7zKI3hgMU0SE8PBwKBQKzJw5U6N9+/btb+yvx0dERMDc3Bzx8fHYt2+fzj7h4eFo3769dL9JkyYYNWpU2RQIYNWqVRo/aq12/PhxDBo0qMzqIHrTMUwREQDA1NQUs2bNQkpKSnmXUmKysrKKPe/Vq1fx/vvvw83NDXZ2diVYVeHk1A0ADg4OqFSpUglVQ0SFYZgiIgBAcHAwVCoVZsyYkW+fqVOnau3ymjdvHtzd3aX76q0133zzDZycnGBjY4Pp06fj+fPnGDduHGxtbeHi4oKVK1dqPX5cXBwaN24MU1NT1KlTB4cOHdKYfu7cObRo0QIWFhZwcnJC79698eDBA2l6kyZNMGzYMIwaNQr29vYICQnROY7c3FxMnz4dLi4uUCqVqFu3LqKioqTpCoUCsbGxmD59OhQKBaZOnVrAmvvfuA8dOoT58+dDoVBAoVDg+vXrsur+7rvv4OPjA3Nzc7i6umLIkCF49OgRAODgwYPo27cv0tLSpOWp63x5N9/NmzfRrl07WFhYwMrKCl27dkViYqI0Xf28rl27Fu7u7rC2tkb37t2RkZEh9dmyZQt8fHxgZmYGOzs7BAcH4/Hjx4WuF6I3AcMUEQEADA0N8c0332DhwoW4ffu2rMfav38/7t69iz/++APfffcdIiIi0Lp1a1SuXBlHjx7Fp59+ik8++URrOePGjcOYMWNw6tQpBAQEoE2bNnj48CEAIDU1FU2bNkW9evVw4sQJREVFITExEV27dtV4jNWrV8PExASHDx/GsmXLdNY3f/58zJ07F99++y3OnDmDkJAQtG3bFpcvXwYA3Lt3D97e3hgzZgzu3buHsWPHFjrm+fPnIyAgAAMHDsS9e/dw7949uLq6yqrbwMAACxYswPnz57F69Wrs378fn3/+OQCgcePGmDdvHqysrKTl6aozNzcX7dq1Q3JyMg4dOoTo6Gj8+++/6Natm0a/q1evYvv27di5cyd27tyJQ4cOSbt97927h9DQUPTr1w8XL17EwYMH0bFjR/CnXYn+v3L+oWUiegWEhYWJdu3aCSGEaNSokejXr58QQoht27aJvB8TERERwtfXV2Pe77//Xri5uWk8lpubm8jJyZHaateuLT744APp/vPnz4W5ubn4+eefhRBCXLt2TQAQM2fOlPpkZ2cLFxcXMWvWLCGEEF999ZX4+OOPNZZ969YtAUDEx8cLIYQIDAwU9erVK3S8zs7O4uuvv9Zoa9CggRgyZIh039fXV0RERBT4OHnXm3r5I0eO1OhTknVv3rxZ2NnZSfdXrlwprK2ttfq5ubmJ77//XgghxG+//SYMDQ3FzZs3pennz58XAMSxY8eEEC+e10qVKon09HSpz7hx44S/v78QQojY2FgBQFy/fr3QGoneRNwyRUQaZs2ahdWrV+PixYvFfgxvb28YGPzv48XJyQk+Pj7SfUNDQ9jZ2SEpKUljvoCAAOn/RkZGqF+/vlTHP//8gwMHDsDCwkK6eXh4AHixVUXNz8+vwNrS09Nx9+5dvPfeexrt7733nqwx50dO3b///juCgoLw1ltvwdLSEr1798bDhw/x5MkTvZd/8eJFuLq6wtXVVWrz8vKCjY2Nxnjd3d1haWkp3a9SpYr0/Pj6+iIoKAg+Pj7o0qULli9f/lodW0ckF8MUEWn48MMPERISgokTJ2pNMzAw0Nq1k52drdXP2NhY475CodDZlpubq3ddjx49Qps2bXD69GmN2+XLl/Hhhx9K/czNzfV+zLJQ3LqvX7+O1q1b45133sEvv/yC2NhYLF68GID8A9R1Kej5MTQ0RHR0NPbs2QMvLy8sXLgQtWvXxrVr10q8DqKKiGGKiLTMnDkTv/76K2JiYjTaHRwckJCQoBGoSvLaUH///bf0/+fPnyM2Nhaenp4AgHfffRfnz5+Hu7s7atasqXErSoCysrKCs7MzDh8+rNF++PBheHl5yarfxMQEOTk5Gm3FrTs2Nha5ubmYO3cuGjVqhLfffht3794tdHkv8/T0xK1bt3Dr1i2p7cKFC0hNTS3SeBUKBd577z1MmzYNp06dgomJCbZt26b3/ESvM4YpItLi4+ODnj17YsGCBRrtTZo0wf379zF79mxcvXoVixcvxp49e0psuYsXL8a2bdsQFxeHoUOHIiUlBf369QMADB06FMnJyQgNDcXx48dx9epV7N27F3379i00ULxs3LhxmDVrFiIjIxEfH48JEybg9OnTGDlypKz63d3dcfToUVy/fh0PHjxAbm5useuuWbMmsrOzsXDhQvz7779Yu3at1gH17u7uePToEfbt24cHDx7o3P0XHBwsPZ8nT57EsWPH0KdPHwQGBqJ+/fp6jevo0aP45ptvcOLECdy8eRNbt27F/fv3paBL9KZjmCIinaZPn661G87T0xNLlizB4sWL4evri2PHjul1ppu+Zs6ciZkzZ8LX1xd//fUXduzYAXt7ewCQtibl5OTg448/ho+PD0aNGgUbGxuN47P0MWLECIwePRpjxoyBj48PoqKisGPHDtSqVUtW/WPHjoWhoSG8vLzg4OCAmzdvFrtuX19ffPfdd5g1axbq1KmD9evXa122onHjxvj000/RrVs3ODg4YPbs2VqPo1Ao8H//93+oXLkyPvzwQwQHB6N69eqIjIzUe1xWVlb4448/0LJlS7z99tuYNGkS5s6dixYtWui/coheYwrx8gEQRERERKQ3bpkiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhk+H8+Y0OB4w+ykgAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["### Plot ap50\n","# IF THERE ARE MULTIPLE NAMES/SITES\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","experiment_folder = out_dir\n","\n","names = [\"Paracou2016\", \"Danum\", \"SepilokE\", \"SepilokW\", \"Paracou2019\", \"ParacouUAV\", \"BCI_50ha\"]\n","name = names[6]\n","#name = \"ParacouMS\"\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if name + '_val/segm/AP50' in x],\n"," [x[name + '_val/segm/AP50'] for x in experiment_metrics if name + '_val/segm/AP50' in x], label='Site Validation AP50', color='red')\n","\n","plt.legend(loc='upper right')\n","plt.title('Validation AP50 loss of detectree2')\n","plt.ylabel('AP50')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"executionInfo":{"elapsed":610,"status":"ok","timestamp":1725385806618,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"HmKv9SqXz9ES","outputId":"ee153ced-a468-41f6-cd5e-923db7de324d"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["### Plot ap50\n","\n","import json\n","import matplotlib.pyplot as plt\n","from detectree2.models.train import load_json_arr\n","\n","experiment_folder = out_dir\n","#name = names[0]\n","name = \"ParacouMS\"\n","experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')\n","\n","plt.plot(\n"," [x['iteration'] for x in experiment_metrics if 'segm/AP50' in x],\n"," [x['segm/AP50'] for x in experiment_metrics if 'segm/AP50' in x], color='red')\n","\n","plt.legend(loc='upper right')\n","plt.title('Val fold AP50')\n","plt.ylabel('AP50')\n","plt.xlabel('Number of Iterations')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"UERn9vzjlRs3"},"source":["## Make predictions on the validation set and visualise"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2gimbSCg4oIE"},"outputs":[],"source":["cfg.INPUT.MIN_SIZE_TEST = 900\n","cfg.INPUT.MAX_SIZE_TEST = 1050"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZIZOJpH_w3ki"},"outputs":[],"source":["import os\n","\n","def get_latest_model_path(output_dir):\n"," \"\"\"\n"," Find the model file with the highest index in the specified output directory.\n","\n"," Args:\n"," output_dir (str): The directory where the model files are stored.\n","\n"," Returns:\n"," str: The path to the model file with the highest index.\n"," \"\"\"\n"," # List all files in the output directory and filter those that match the \"model_X.pth\" pattern\n"," model_files = [f for f in os.listdir(output_dir) if f.startswith(\"model_\") and f.endswith(\".pth\")]\n","\n"," if not model_files:\n"," raise FileNotFoundError(f\"No model files found in the directory {output_dir}\")\n","\n"," # Sort the files by the numeric index extracted from the filename\n"," latest_model_file = max(model_files, key=lambda f: int(f.split('_')[1].split('.')[0]))\n","\n"," # Return the full path to the latest model file\n"," return os.path.join(output_dir, latest_model_file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1l-KgKOFMjRaTHvdMHBqTITC3oUEASzLb"},"executionInfo":{"elapsed":84178,"status":"ok","timestamp":1725295631209,"user":{"displayName":"James Ball","userId":"12200917192257062155"},"user_tz":-60},"id":"zlYlFAsag4hL","outputId":"9d81d0a7-5ef1-4132-852a-46115037ee93"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["import os\n","from detectree2.models.train import combine_dicts\n","import cv2\n","from detectron2.utils.visualizer import Visualizer, ColorMode\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","from detectron2.engine import DefaultPredictor\n","from detectron2.config import get_cfg\n","from detectron2 import model_zoo\n","from PIL import Image\n","import numpy as np\n","import rasterio\n","\n","# Configuration for the new routine\n","name1 = \"Paracou\"\n","appends = \"15_15_0.7\" # Ensure this is defined\n","train_location = f\"/content/drive/MyDrive/WORK/detectree2/data/{name1}/tilesMS_{appends}/train/\"\n","\n","# Register the dataset if not already done\n","#MetadataCatalog.get(name1 + \"_train\").set(thing_classes=['tree'])\n","trees_metadata = MetadataCatalog.get(\"ParacouMS\" + \"_val\")\n","\n","# Load the combined dictionaries for the validation fold\n","#val_fold1 = 1 # Make sure this is defined\n","dataset_dicts = combine_dicts(train_location, val_fold, mode='val')\n","\n","# Retain the cfg from training\n","\n","# Set up the configuration and the predictor\n","#cfg = get_cfg()\n","#cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\"))\n","#cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml\")\n","#cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # Set the threshold for this model\n","#cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 # Assuming binary classification (tree or not)\n","#cfg.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675, 103.530, 116.280][:5] # Adjust for the number of bands\n","#cfg.MODEL.PIXEL_STD = [1.0, 1.0, 1.0, 1.0, 1.0][:5] # Adjust for the number of bands\n","#cfg.INPUT.FORMAT = \"BGR\" # Adjust if using a different format\n","\n","#cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\n","\n","cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.35\n","cfg.MODEL.WEIGHTS = get_latest_model_path(out_dir)\n","\n","print(cfg.MODEL.WEIGHTS)\n","\n","predictor = DefaultPredictor(cfg)\n","\n","# Function to normalize and convert multi-band image to RGB if needed\n","def prepare_image_for_visualization(image):\n"," if image.shape[2] == 3:\n"," # If the image has 3 bands, assume it's RGB\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n"," else:\n"," # If the image has more than 3 bands, choose the first 3 for visualization\n"," image = image[:, :, :3] # Or select specific bands\n"," image = np.stack([\n"," cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX)\n"," for i in range(3)\n"," ], axis=-1).astype(np.uint8)\n","\n"," return image\n","\n","# Loop through the dataset and visualize predictions\n","for d in dataset_dicts:\n"," #img = cv2.imread(d[\"file_name\"], cv2.IMREAD_UNCHANGED) # Use IMREAD_UNCHANGED to correctly handle multi-band images\n"," with rasterio.open(d[\"file_name\"]) as src:\n"," img = src.read() # Read all bands\n"," img = np.transpose(img, (1, 2, 0)).astype(\"float32\") # Convert to HWC format\n"," #img = prepare_image_for_visualization(img) # Normalize and prepare for visualization\n"," print(img.shape)\n"," # Handle multi-band images correctly (if your model expects 5 channels)\n"," #if img.shape[-1] != 3:\n"," # img = img[:, :, :3] # Example: Select the first 3 bands if needed\n","\n"," outputs = predictor(img)\n"," #v = Visualizer(img[:, :, ::-1], metadata=trees_metadata, scale=0.7) # remove the colors of unsegmented pixels\n"," #img = prepare_image_for_visualization(img)\n"," img = img[:, :, :3]/10\n"," v = Visualizer(img[:, :, ::-1], metadata=MetadataCatalog.get(cfg.DATASETS.TEST[0]), scale=1.2) # remove the colors of unsegmented pixels\n"," v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n"," image = cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB)\n"," display(Image.fromarray(image))\n"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1_GdZ4YRp4KZ2kLWK1MlY1--mGtPlDqVL","timestamp":1723475016377}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file