Add command line script (#1559)

* Add command line script

* Label PRs correctly

* Flake8 fix

* Add callbacks

* Update all conf files

* Enforce class of model/data

* Update BYOL tests

* Update all tests

* Use default num workers: 0

* Variable interpolation only supported by omegaconf backend

* Fix regression tests

* Ensure that jsonargparse is installed during testing

* Bump minimimum lightning version

* Workaround for lightning/jsonargparse bugs

* Ignore Python 3.9 warning message

* Add test coverage for __main__.py

* Increase coverage of PixelwiseRegressionTask

* jsonargparse bug fixed in latest release

* Revert "jsonargparse bug fixed in latest release"

This reverts commit 4f38cdf.

* Use default logging frequency

* Ensure that metrics are actually logged

* Fix ObjectDetectionTask logging

* Ignore type errors

* Not all need to be ignored

.github/labeler.yml

@@ -22,7 +22,8 @@ dependencies:
 documentation:
 - "docs/**"
 scripts:
-- "*.py"
+- "torchgeo/__main__.py"
+- "torchgeo/main.py"
 - "experiments/**"
 testing:
 - "tests/**"

.github/workflows/release.yaml

@@ -29,7 +29,10 @@ jobs:
     - name: List pip dependencies
       run: pip list
     - name: Run pytest checks
-      run: pytest --cov=torchgeo --cov-report=xml --durations=10
+      run: |
+        pytest --cov=torchgeo --cov-report=xml --durations=10
+        python -m torchgeo --help
+        torchgeo --help
   integration:
     name: integration
     runs-on: ubuntu-latest
@@ -52,7 +55,10 @@ jobs:
     - name: List pip dependencies
       run: pip list
     - name: Run integration checks
-      run: pytest -m slow --durations=10
+      run: |
+        pytest -m slow --durations=10
+        python -m torchgeo --help
+        torchgeo --help
   notebooks:
     name: notebooks
     runs-on: ubuntu-latest

.github/workflows/tests.yaml

@@ -74,7 +74,9 @@ jobs:
     - name: List pip dependencies
       run: pip list
     - name: Run pytest checks
-      run: pytest --cov=torchgeo --cov-report=xml --durations=10
+      run: |
+        pytest --cov=torchgeo --cov-report=xml --durations=10
+        python3 -m torchgeo --help
     - name: Report coverage
       uses: codecov/[email protected]
       with:
@@ -109,7 +111,9 @@ jobs:
     - name: List pip dependencies
       run: pip list
     - name: Run pytest checks
-      run: pytest --cov=torchgeo --cov-report=xml --durations=10
+      run: |
+        pytest --cov=torchgeo --cov-report=xml --durations=10
+        python3 -m torchgeo --help
     - name: Report coverage
       uses: codecov/[email protected]
       with:

.pre-commit-config.yaml

@@ -34,5 +34,5 @@ repos:
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]
-            additional_dependencies: [torch>=2, torchmetrics>=0.10, lightning>=2.0.9, pytest>=6.1.2, pyvista>=0.34.2, omegaconf>=2.0.1, hydra-core>=1, kornia>=0.6.5, numpy>=1.22]
+            additional_dependencies: [torch>=2, torchmetrics>=0.10, lightning>=2.0.9, pytest>=6.1.2, pyvista>=0.34.2, kornia>=0.6.5, numpy>=1.22]
             exclude: (build|data|dist|logo|logs|output)/

conf/bigearthnet.yaml

@@ -1,24 +1,22 @@
-module:
-  _target_: torchgeo.trainers.MultiLabelClassificationTask
-  loss: "bce"
-  model: "resnet18"
-  lr: 1e-3
-  patience: 6
-  weights: null
-  in_channels: 14
-  num_classes: 19
-
-datamodule:
-  _target_: torchgeo.datamodules.BigEarthNetDataModule
-  root: "data/bigearthnet"
-  bands: "all"
-  num_classes: ${module.num_classes}
-  batch_size: 128
-  num_workers: 4
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: MultiLabelClassificationTask
+  init_args:
+    loss: "bce"
+    model: "resnet18"
+    lr: 1e-3
+    patience: 6
+    weights: null
+    in_channels: 14
+    num_classes: 19
+data:
+  class_path: BigEarthNetDataModule
+  init_args:
+    batch_size: 128
+    num_workers: 4
+  dict_kwargs:
+    root: "data/bigearthnet"
+    bands: "all"
+    num_classes: ${model.init_args.num_classes}

conf/chesapeake_cvpr.yaml

@@ -1,34 +1,32 @@
-module:
-  _target_: torchgeo.trainers.SemanticSegmentationTask
-  loss: "ce"
-  model: "unet"
-  backbone: "resnet18"
-  weights: null
-  lr: 1e-3
-  patience: 6
-  in_channels: 4
-  num_classes: 7
-  num_filters: 256
-  ignore_index: null
-
-datamodule:
-  _target_: torchgeo.datamodules.ChesapeakeCVPRDataModule
-  root: "data/chesapeake/cvpr"
-  train_splits:
-    - "de-train"
-  val_splits:
-    - "de-val"
-  test_splits:
-    - "de-test"
-  batch_size: 200
-  patch_size: 256
-  num_workers: 4
-  class_set: ${module.num_classes}
-  use_prior_labels: False
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: SemanticSegmentationTask
+  init_args:
+    loss: "ce"
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
+    lr: 1e-3
+    patience: 6
+    in_channels: 4
+    num_classes: 7
+    num_filters: 256
+    ignore_index: null
+data:
+  class_path: ChesapeakeCVPRDataModule
+  init_args:
+    train_splits:
+      - "de-train"
+    val_splits:
+      - "de-val"
+    test_splits:
+      - "de-test"
+    batch_size: 200
+    patch_size: 256
+    num_workers: 4
+    class_set: ${model.init_args.num_classes}
+    use_prior_labels: False
+  dict_kwargs:
+    root: "data/chesapeake/cvpr"

conf/cowc_counting.yaml

@@ -1,21 +1,19 @@
-module:
-  _target_: torchgeo.trainers.RegressionTask
-  model: resnet18
-  weights: null
-  num_outputs: 1
-  in_channels: 3
-  lr: 1e-3
-  patience: 2
-
-datamodule:
-  _target_: torchgeo.datamodules.COWCCountingDataModule
-  root: "data/cowc_counting"
-  batch_size: 64
-  num_workers: 4
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: RegressionTask
+  init_args:
+    model: resnet18
+    weights: null
+    num_outputs: 1
+    in_channels: 3
+    lr: 1e-3
+    patience: 2
+data:
+  class_path: COWCCountingDataModule
+  init_args:
+    batch_size: 64
+    num_workers: 4
+  dict_kwargs:
+    root: "data/cowc_counting"

conf/cyclone.yaml

@@ -1,21 +1,19 @@
-module:
-  _target_: torchgeo.trainers.RegressionTask
-  model: "resnet18"
-  weights: null
-  num_outputs: 1
-  in_channels: 3
-  lr: 1e-3
-  patience: 2
-
-datamodule:
-  _target_: torchgeo.datamodules.TropicalCycloneDataModule
-  root: "data/cyclone"
-  batch_size: 32
-  num_workers: 4
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: RegressionTask
+  init_args:
+    model: "resnet18"
+    weights: null
+    num_outputs: 1
+    in_channels: 3
+    lr: 1e-3
+    patience: 2
+data:
+  class_path: TropicalCycloneDataModule
+  init_args:
+    batch_size: 32
+    num_workers: 4
+  dict_kwargs:
+    root: "data/cyclone"

conf/deepglobelandcover.yaml

@@ -1,27 +1,25 @@
-module:
-  _target_: torchgeo.trainers.SemanticSegmentationTask
-  loss: "ce"
-  model: "unet"
-  backbone: "resnet18"
-  weights: null
-  lr: 1e-3
-  patience: 6
-  in_channels: 3
-  num_classes: 7
-  num_filters: 1
-  ignore_index: null
-
-datamodule:
-  _target_: torchgeo.datamodules.DeepGlobeLandCoverDataModule
-  root: "data/deepglobelandcover"
-  batch_size: 1
-  patch_size: 64
-  val_split_pct: 0.5
-  num_workers: 0
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: SemanticSegmentationTask
+  init_args:
+    loss: "ce"
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
+    lr: 1e-3
+    patience: 6
+    in_channels: 3
+    num_classes: 7
+    num_filters: 1
+    ignore_index: null
+data:
+  class_path: DeepGlobeLandCoverDataModule
+  init_args:
+    batch_size: 1
+    patch_size: 64
+    val_split_pct: 0.5
+    num_workers: 0
+  dict_kwargs:
+    root: "data/deepglobelandcover"

conf/etci2021.yaml

@@ -1,24 +1,22 @@
-module:
-  _target_: torchgeo.trainers.SemanticSegmentationTask
-  loss: "ce"
-  model: "unet"
-  backbone: "resnet18"
-  weights: true
-  lr: 1e-3
-  patience: 6
-  in_channels: 6
-  num_classes: 2
-  ignore_index: 0
-
-datamodule:
-  _target_: torchgeo.datamodules.ETCI2021DataModule
-  root: "data/etci2021"
-  batch_size: 32
-  num_workers: 4
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: SemanticSegmentationTask
+  init_args:
+    loss: "ce"
+    model: "unet"
+    backbone: "resnet18"
+    weights: true
+    lr: 1e-3
+    patience: 6
+    in_channels: 6
+    num_classes: 2
+    ignore_index: 0
+data:
+  class_path: ETCI2021DataModule
+  init_args:
+    batch_size: 32
+    num_workers: 4
+  dict_kwargs:
+    root: "data/etci2021"

conf/eurosat.yaml

@@ -1,22 +1,20 @@
-module:
-  _target_: torchgeo.trainers.ClassificationTask
-  loss: "ce"
-  model: "resnet18"
-  lr: 1e-3
-  patience: 6
-  weights: null
-  in_channels: 13
-  num_classes: 10
-
-datamodule:
-  _target_: torchgeo.datamodules.EuroSATDataModule
-  root: "data/eurosat"
-  batch_size: 128
-  num_workers: 4
-
 trainer:
-  _target_: lightning.pytorch.Trainer
-  accelerator: gpu
-  devices: 1
   min_epochs: 15
   max_epochs: 40
+model:
+  class_path: ClassificationTask
+  init_args:
+    loss: "ce"
+    model: "resnet18"
+    lr: 1e-3
+    patience: 6
+    weights: null
+    in_channels: 13
+    num_classes: 10
+data:
+  class_path: EuroSATDataModule
+  init_args:
+    batch_size: 128
+    num_workers: 4
+  dict_kwargs:
+    root: "data/eurosat"