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Add FastFlow model training testing inference via Anomalib API (#386)
* Refactored MVTec datamodule * 🏷️ Rename BTech datamodule * 🏷️ Rename Folder datamodule * Create datamodule jupyter notebook * Added mvtec into jupyter notebook * Apply black formatter * Finished MVTec * 🏷 Rename the name of the folder * 🏷 Renamed anomaly-datamodule.ipynb to mvtec.ipynb * ➕ Created BTech notebook * 🚚 Move the main description from mvtec to README.md * Polish btech jupyter notebook * ➕Created folder jupyter notebook * Addres PR comments. * Format the notebooks * Added black, isort, flake8 and pylint * ➕ Add mdformat to dev requirements * 🛠 Update the nnotebook markdowns * Configured pre-commit for notebooks * ➕ Add DATAMODULE_REGISTRY to the datamodules. * ➕ Added relative imports to __init__ modules. * Register callbacks via @CALLBACK_REGISTRY * ➕ Added missing callback imports * ➕ Add AnomalibCLI * ➕ Add AnomalibCLI * ➕ Add `normalization_method` to `MetricsConfigurationCallback` * ➕ Add padim config file * 🛠 Fix pytorch-lightning version to add extra * Update torchmetrics version requirement * Add anomalib CLI entrypoint * Remove --save_images flag from the cli * Refactor trainer.py and create main() function * Updated anomalib entrypoint. Removed trainer.py from cli * Add logger to cli * ➕ Add patchcore config * ➕ Add Cflow CLI config * 🚚 Move padim and patchcore configs to config directory * Add cflow, dfkde, dfm, draem, fastflow, padim and patchcore * ➕ Add ganomaly config * add reverse distillation and stfpm configs * Update notebooks * Create the project directory only during traaining. * Added notebooks * 🏷 Renamed config directory to configs * Fix the project directory creation logic. * Add the training CLI command to the README.md * 🛠 Fix incorrect statement. * Fix patchcore training * Added fastflow training * Added fastflow jupyter notbeook * Removeed models jupyter notebooks * Removed trainer.py * Addressed PR comments' * Address PR comments. * Address markdown checks
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configs/model/fastflow.yaml

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@@ -29,7 +29,7 @@ model:
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hidden_ratio: 1.0 # options: [1.0, 1.0, 0.16, 0.16] - for each supported backbone
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optimizer:
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class_path: torch.optim.Adam
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class_path: torch.optim._multi_tensor.Adam
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init_args:
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lr: 0.001
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weight_decay: 0.00001

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