Updates docs for release

.github/workflows/docs_stable.yml

@@ -0,0 +1,34 @@
+name: Build Docs for releases
+
+on:
+  workflow_dispatch: # run on request (no need for PR)
+  release:
+    types: [published]
+
+jobs:
+  Build-Docs:
+    runs-on: ubuntu-20.04
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0 # otherwise, you will failed to push refs to dest repo
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: python -m pip install tox
+      - name: Build-Docs
+        run: |
+          echo RELEASE_VERSION=${GITHUB_REF#refs/*/} >> $GITHUB_ENV
+          tox -e build-doc
+      # - name: Deploy
+      #   uses: peaceiris/actions-gh-pages@v3
+      #   with:
+      #     github_token: ${{ secrets.GITHUB_TOKEN }}
+      #     publish_dir: ./public
+      #     destination_dir: ${{ env.RELEASE_VERSION }}
+      #     force_orphan: true

datumaro/cli/util/project.py

@@ -153,7 +153,7 @@ def split_local_revpath(revpath: str) -> Tuple[Revision, str]:
 
     A local revpath is a path to a revision withing the current project.
     The syntax is:
-      - [ <revision> : ] [ <target> ]
+        - [ <revision> : ] [ <target> ]
     At least one part must be present.
 
     Returns: (revision, build target)

datumaro/plugins/data_formats/common_semantic_segmentation.py

@@ -165,19 +165,19 @@ def find_sources(cls, path):
 class CommonSemanticSegmentationWithSubsetDirsImporter(CommonSemanticSegmentationImporter):
     """It supports the following subset sub-directory structure for CommonSemanticSegmentation.
 
-    ```
-    Dataset/
-    └─ <split: train,val, ...>
-        ├── dataset_meta.json # a list of labels
-        ├── images/
-        │   ├── <img1>.png
-        │   ├── <img2>.png
-        │   └── ...
-        └── masks/
-            ├── <img1>.png
-            ├── <img2>.png
-            └── ...
+    .. code-block::
+
+        Dataset/
+        └─ <split: train,val, ...>
+            ├── dataset_meta.json # a list of labels
+            ├── images/
+            │   ├── <img1>.png
+            │   ├── <img2>.png
+            │   └── ...
+            └── masks/
+                ├── <img1>.png
+                ├── <img2>.png
+                └── ...
 
     Then, the imported dataset will have train, val, ... CommonSemanticSegmentation subsets.
-    ```
     """

datumaro/plugins/data_formats/datumaro_binary/mapper/__init__.py

@@ -5,7 +5,7 @@
 # ruff: noqa: F405
 
 from .annotation import *
-from .common import *
+from .common import DictMapper, FloatListMapper, IntListMapper, Mapper, StringMapper
 from .dataset_item import *
 from .media import *
 

datumaro/plugins/data_formats/imagenet.py

@@ -66,12 +66,15 @@ class ImagenetImporter(Importer):
     """TorchVision's ImageFolder style importer.
     For example, it imports the following directory structure.
 
-    root
-    ├── label_0
-    │   ├── label_0_1.jpg
-    │   └── label_0_2.jpg
-    └── label_1
-        └── label_1_1.jpg
+    .. code-block:: text
+
+        root
+        ├── label_0
+        │   ├── label_0_1.jpg
+        │   └── label_0_2.jpg
+        └── label_1
+            └── label_1_1.jpg
+
     """
 
     @classmethod
@@ -106,25 +109,27 @@ class ImagenetWithSubsetDirsImporter(ImagenetImporter):
     """TorchVision ImageFolder style importer.
     For example, it imports the following directory structure.
 
-    root
-    ├── train
-    │   ├── label_0
-    │   │   ├── label_0_1.jpg
-    │   │   └── label_0_2.jpg
-    │   └── label_1
-    │       └── label_1_1.jpg
-    ├── val
-    │   ├── label_0
-    │   │   ├── label_0_1.jpg
-    │   │   └── label_0_2.jpg
-    │   └── label_1
-    │       └── label_1_1.jpg
-    └── test
-        ├── label_0
-        │   ├── label_0_1.jpg
-        │   └── label_0_2.jpg
-        └── label_1
-            └── label_1_1.jpg
+    .. code-block::
+
+        root
+        ├── train
+        │   ├── label_0
+        │   │   ├── label_0_1.jpg
+        │   │   └── label_0_2.jpg
+        │   └── label_1
+        │       └── label_1_1.jpg
+        ├── val
+        │   ├── label_0
+        │   │   ├── label_0_1.jpg
+        │   │   └── label_0_2.jpg
+        │   └── label_1
+        │       └── label_1_1.jpg
+        └── test
+            ├── label_0
+            │   ├── label_0_1.jpg
+            │   └── label_0_2.jpg
+            └── label_1
+                └── label_1_1.jpg
 
     Then, it will have three subsets: train, val, and test and they have label_0 and label_1 labels.
     """

datumaro/plugins/sampler/random_sampler.py

@@ -15,7 +15,7 @@
 
 
 class RandomSampler(Transform, CliPlugin):
-    """
+    r"""
     Sampler that keeps no more than required number of items in the dataset.|n
     |n
     Notes:|n

docs/source/conf.py

@@ -63,7 +63,8 @@
 
 suppress_warnings = [
     # "myst.xref_missing",
-    "myst.iref_ambiguous"
+    "myst.iref_ambiguous",
+    "autosectionlabel.*",
 ]
 
 autosummary_generate = True  # Turn on sphinx.ext.autosummary

docs/source/docs/level-up/advanced_skills/index.rst

@@ -1,5 +1,5 @@
 Advanced Skills
-###########
+###############
 
 .. panels::
 

docs/source/docs/level-up/basic_skills/03_dataset_import_export.rst

@@ -1,22 +1,22 @@
-=============
+===============================
 Level 3: Data Import and Export
-=============
+===============================
 
 Datumaro is a tool that supports public data formats across a wide range of tasks such as
 classification, detection, segmentation, pose estimation, or visual tracking.
 To facilitate this, Datumaro provides assistance with data import and export via both Python API and CLI.
 This makes it easier for users to work with various data formats using Datumaro.
 
 Prepare dataset
-============
+===============
 
 For the segmentation task, we here introduce the Cityscapes, which collects road scenes from 50
 different cities and contains 5K fine-grained pixel-level annotations and 20K coarse annotations.
 More detailed description is given by :ref:`here <Cityscapes>`.
 The Cityscapes dataset is available for free `download <https://www.cityscapes-dataset.com/downloads/>`_.
 
 Convert data format
-============
+===================
 
 Users sometimes needs to compare, merge, or manage various kinds of public datasets in a unified
 system. To achieve this, Datumaro not only has `import` and `export` funcionalities, but also

docs/source/docs/level-up/basic_skills/04_detect_data_format.rst

@@ -1,14 +1,14 @@
-=============
+===================================================
 Level 4: Detect Data Format from an Unknown Dataset
-=============
+===================================================
 
 Datumaro provides a function to detect the format of a dataset before importing data. This can be
 useful in cases where information about the original format of the data has been lost or is unclear.
 With this function, users can easily identify the format and proceed with appropriate data
 handling processes.
 
 Detect data format
-============
+==================
 
 .. tabbed:: CLI
 

docs/source/docs/level-up/basic_skills/index.rst

@@ -1,5 +1,5 @@
 Basic Skills
-###########
+############
 
 .. panels::
 

docs/source/docs/level-up/intermediate_skills/index.rst

@@ -1,5 +1,5 @@
 Intermediate Skills
-###########
+###################
 
 .. panels::
 

docs/source/docs/reference/plugins/sampler/algorithm/datumaro.plugins.sampler.algorithm.entropy.rst

@@ -2,13 +2,6 @@ Entropy module
 --------------
 
 .. automodule:: datumaro.plugins.sampler.algorithm.entropy
-
-   .. autoclass:: SampleEntropy
-
-      .. automethod:: __init__
-
-      .. automethod:: get_sample
-
-      .. automethod:: _get_sample_mixed
-
-      .. automethod:: _rank_images
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/docs/reference/plugins/sampler/algorithm/datumaro.plugins.sampler.algorithm.rst

@@ -5,4 +5,3 @@ Algorithm module
    :members:
    :undoc-members:
    :show-inheritance:
-   :private-members:

docs/source/docs/user-manual/extending.md

@@ -1,8 +1,8 @@
 # Extending
 
 There are few ways to extend and customize Datumaro behavior, which is
-supported by plugins. Check [our contribution guide](/docs/contributing) for
-details on plugin implementation. In general, a plugin is a Python module.
+supported by plugins. Check [our contribution guide](https://github.com/openvinotoolkit/datumaro/blob/develop/contributing.md)
+for details on plugin implementation. In general, a plugin is a Python module.
 It must be put into a plugin directory:
 - `<project_dir>/.datumaro/plugins` for project-specific plugins
 - `<datumaro_dir>/plugins` for global plugins

notebooks/09_encrypt_dataset.ipynb

@@ -315,7 +315,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Re-export again to any dataset format with no encryption\n",
+    "## Re-export again to any dataset format with no encryption\n",
     "\n",
     "Because the `DatumaroBinary` format is encrypted, it cannot be easily used for your purposes. In this time, we re-export it to any dataset format for the future usage. For example, COCO format is used for the export."
    ]

requirements.txt

@@ -2,8 +2,3 @@
 -r requirements-default.txt
 
 opencv-python-headless>=4.1.0.25
-
-# docs
-markupsafe>=2.0.1
-nbconvert>=7.2.3
-ipython>=8.4.0

tox.ini

@@ -2,10 +2,12 @@
 isolated_build = true
 skip_missing_interpreters = true
 
+
 [testenv]
 deps =
     -r{toxinidir}/requirements.txt
 
+
 [testenv:pre-commit]
 basepython = python3
 deps =