Add Pytorch lightning classifier#580
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Pull request overview
Adds a PyTorch Lightning-based classifier container to support Trainer-driven training workflows, including multi-GPU usage, while also fixing several existing ClassifierContainer regression points and expanding tests/docs.
Changes:
- Introduces
LightningClassifierContainerwith Lightning hooks for training, validation, prediction, saving/loading, metrics, and dataset loading. - Adds Lightning-specific tests plus regression tests for classifier input sizing, layerwise LR keys, and save/load roundtrips.
- Adds optional
lightningdependency extra, CI installation updates, documentation, and changelog entry.
Reviewed changes
Copilot reviewed 10 out of 10 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
mapreader/classify/lightning_classifier.py |
Adds the new Lightning classifier implementation. |
mapreader/classify/classifier.py |
Fixes existing classifier regressions and formatting around model setup, summaries, checkpoints, logits, and metrics. |
mapreader/__init__.py |
Exposes LightningClassifierContainer when optional dependencies are installed. |
tests/test_classify/test_lightning_classifier.py |
Adds coverage for Lightning classifier initialization, training/prediction hooks, saving/loading, and errors. |
tests/test_classify/test_classifier.py |
Adds regression coverage for existing classifier fixes. |
setup.py |
Adds the optional lightning dependency extra. |
.github/workflows/mr_ci.yml |
Installs the Lightning extra for main CI. |
.github/workflows/mr_ci_text_spotting.yml |
Installs the Lightning extra for text spotting CI. |
docs/source/using-mapreader/step-by-step-guide/4-classify/train.rst |
Documents Lightning classifier usage. |
CHANGELOG.md |
Notes the new Lightning classifier feature. |
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…apReader into pytorch_lightning
| model: str | nn.Module | None = None, | ||
| labels_map: dict[int, str] | None = None, | ||
| dataloaders: dict[str, DataLoader] | None = None, | ||
| input_size: int = (224, 224), |
| model_name: str, | ||
| weights: str | None = "DEFAULT", | ||
| last_layer_num_classes: str | int | None = "default", | ||
| ) -> tuple[Any, int, bool]: |
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| try: | ||
| self.device = mydevice | ||
| self.model = self.model.to(mydevice) | ||
| except: | ||
| pass |
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| if force_device: | ||
| if not isinstance(force_device, str): | ||
| force_device = str(force_device) | ||
| os.environ["CUDA_VISIBLE_DEVICES"] = force_device |
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Summary
Adds PyTorch lightning classifier
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