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Update ConfigConverter for Geti2.12 (#4477)
* add factory for classficaiton * add mising files * minor * fix imports * fix imports in tests 2 * fix ruff * fix unit test * fix paths * change converter * add configurable augmentation and input size * temporary fix * update ConfigConverter: * fix linter * update unit test for ConfigConverter * change integration tests * add missing file * fix unit test * delete templates * update changelog * update recipe * fix linter * return templates back
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‎.github/workflows/pre_merge.yaml‎

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run: |
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pip install '.[ci_tox]'
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- name: Run Integration Test
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run: tox -vv -e integration-test-${{ matrix.task }} -- --run-category-only
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run: tox -vv -e integration-test-${{ matrix.task }} -- --task ${{ matrix.task }} --run-category-only

‎CHANGELOG.md‎

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### Enhancements
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- Refactor GetiConfigConverter. Update integration tests
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(<https://github.com/open-edge-platform/training_extensions/pull/4477>)
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- Refactor OTXModels
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(<https://github.com/open-edge-platform/training_extensions/pull/4241>)
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- Introduce Native OTX Engine, refactor folders structure

‎src/otx/backend/native/engine.py‎

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if not isinstance(self.checkpoint, (Path, str)) and not Path(self.checkpoint).exists():
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msg = f"Checkpoint {self.checkpoint} does not exist."
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raise FileNotFoundError(msg)
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self._model.load_state_dict_incrementally(torch.load(self.checkpoint))
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chkpt = self._load_model_checkpoint(self.checkpoint, map_location="cpu")
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self._model.load_state_dict_incrementally(chkpt)
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# ------------------------------------------------------------------------ #
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# General OTX Entry Points

‎src/otx/recipe/_base_/data/classification.yaml‎

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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true

‎src/otx/recipe/classification/h_label_cls/efficientnet_b0.yaml‎

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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true
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enable: false
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init_args:
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kernel_size: 5
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false
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- class_path: torchvision.transforms.v2.ToDtype
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init_args:
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dtype: ${as_torch_dtype:torch.float32}
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init_args:
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mean: [123.675, 116.28, 103.53]
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std: [58.395, 57.12, 57.375]
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false

‎src/otx/recipe/classification/h_label_cls/efficientnet_v2.yaml‎

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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true
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enable: false
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init_args:
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kernel_size: 5
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false
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- class_path: torchvision.transforms.v2.ToDtype
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init_args:
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dtype: ${as_torch_dtype:torch.float32}
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init_args:
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mean: [123.675, 116.28, 103.53]
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std: [58.395, 57.12, 57.375]
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false

‎src/otx/recipe/classification/h_label_cls/tv_efficientnet_b3.yaml‎

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- class_path: otx.data.transform_libs.torchvision.EfficientNetRandomCrop
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init_args:
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scale: $(input_size)
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- class_path: otx.data.transform_libs.torchvision.PhotoMetricDistortion
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true
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- class_path: torchvision.transforms.v2.RandomVerticalFlip
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enable: false
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- class_path: torchvision.transforms.v2.GaussianBlur
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enable: false
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init_args:
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kernel_size: 5
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false
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- class_path: torchvision.transforms.v2.ToDtype
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init_args:
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dtype: ${as_torch_dtype:torch.float32}

‎src/otx/recipe/classification/h_label_cls/tv_efficientnet_v2_l.yaml‎

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- class_path: otx.data.transform_libs.torchvision.EfficientNetRandomCrop
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init_args:
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scale: $(input_size)
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- class_path: otx.data.transform_libs.torchvision.PhotoMetricDistortion
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true
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- class_path: torchvision.transforms.v2.RandomVerticalFlip
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enable: false
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- class_path: torchvision.transforms.v2.GaussianBlur
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enable: false
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init_args:
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kernel_size: 5
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false
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- class_path: torchvision.transforms.v2.ToDtype
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init_args:
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dtype: ${as_torch_dtype:torch.float32}

‎src/otx/recipe/classification/h_label_cls/tv_mobilenet_v3_small.yaml‎

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filename: "checkpoints/epoch_{epoch:03d}"
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overrides:
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reset:
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- data.train_subset.transforms
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max_epochs: 90
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data:
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task: H_LABEL_CLS
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data_format: datumaro
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train_subset:
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transforms:
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- class_path: otx.data.transform_libs.torchvision.RandomResizedCrop
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init_args:
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scale: $(input_size)
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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init_args:
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prob: 0.5
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is_numpy_to_tvtensor: true
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- class_path: torchvision.transforms.v2.ToDtype
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init_args:
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dtype: ${as_torch_dtype:torch.float32}
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scale: false
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- class_path: torchvision.transforms.v2.Normalize
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init_args:
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mean: [123.675, 116.28, 103.53]
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std: [58.395, 57.12, 57.375]

‎src/otx/recipe/classification/multi_class_cls/efficientnet_b0.yaml‎

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- class_path: otx.data.transform_libs.torchvision.RandomAffine
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enable: false
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- class_path: otx.data.transform_libs.torchvision.RandomFlip
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enable: true
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init_args:
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is_numpy_to_tvtensor: true
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enable: false
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false
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- class_path: torchvision.transforms.v2.ToDtype
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mean: [123.675, 116.28, 103.53]
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std: [58.395, 57.12, 57.375]
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- class_path: torchvision.transforms.v2.GaussianNoise
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enable: false

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