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Galina Zalesskaya
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[ENHANCE] Parametrize saliency maps dumping in export (part 2) (#1888)
* Add dump_features for segmentation * Dump_features for the rest of the tasks * Add unit tests * Fix pre-commit issues * Update documentation * Fixes from comments * Add integration cli tests * Remove commented code
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‎docs/source/guide/get_started/quick_start_guide/cli_commands.rst‎

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@@ -261,10 +261,10 @@ With the ``--help`` command, you can list additional information, such as its pa
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.. code-block::
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(otx) ...$ otx export --help
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usage: otx export [-h] [--load-weights LOAD_WEIGHTS] [--save-model-to SAVE_MODEL_TO] [template]
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usage: otx export [-h] [--load-weights LOAD_WEIGHTS] [--save-model-to SAVE_MODEL_TO] [--work-dir WORK_DIR] [--dump-features] [--half-precision] [template]
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positional arguments:
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template Enter the path or ID or name of the template file.
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template Enter the path or ID or name of the template file.
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This can be omitted if you have train-data-roots or run inside a workspace.
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optional arguments:
@@ -273,6 +273,9 @@ With the ``--help`` command, you can list additional information, such as its pa
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Load model weights from previously saved checkpoint.
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--save-model-to SAVE_MODEL_TO
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Location where exported model will be stored.
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--work-dir WORK_DIR Location where the intermediate output of the export will be stored.
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--dump-features Whether to return feature vector and saliency map for explanation purposes.
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--half-precision This flag indicated if model is exported in half precision (FP16).
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The command below performs exporting to the ``outputs/openvino`` path.
@@ -283,6 +286,12 @@ The command below performs exporting to the ``outputs/openvino`` path.
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The command results in ``openvino.xml``, ``openvino.bin`` and ``label_schema.json``
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To use the exported model as an input for ``otx explain``, please dump additional outputs with internal information, using ``--dump-features``:
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.. code-block::
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(otx) ...$ otx export Custom_Object_Detection_Gen3_SSD --load-weights <path/to/trained/weights.pth> --save-model-to outputs/openvino/with_features --dump-features
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************
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Optimization
@@ -419,7 +428,7 @@ With the ``--help`` command, you can list additional information, such as its pa
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Weight of the saliency map when overlaying the saliency map.
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The command below will generate saliency maps (heatmaps with read colored areas of focus) of the trained model on the provided dataset and save the resulting images to ``save-explanation-to`` path:
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The command below will generate saliency maps (heatmaps with red colored areas of focus) of the trained model on the provided dataset and save the resulting images to ``save-explanation-to`` path:
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.. code-block::
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@@ -433,6 +442,20 @@ The command below will generate saliency maps (heatmaps with read colored areas
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It is possible to pass both PyTorch weights ``.pth`` or OpenVINO™ IR ``openvino.xml`` to ``--load-weights`` option.
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By default, the model is exported to the OpenVINO™ IR format without extra feature information needed for the ``explain`` function. To use OpenVINO™ IR model in ``otx explain``, please first export it with ``--dump-features`` parameter:
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.. code-block::
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(otx) ...$ otx export SSD --load-weights <path/to/trained/weights.pth> \
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--save-model-to outputs/openvino/with_features \
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--dump-features
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(otx) ...$ otx explain SSD --explain-data-roots <path/to/explain/root> \
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--load-weights outputs/openvino/with_features \
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--save-explanation-to <path/to/output/root> \
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--explain-algorithm classwisesaliencymap \
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--overlay-weight 0.5
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*************
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Demonstration

‎otx/algorithms/action/tasks/inference.py‎

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@@ -327,15 +327,13 @@ def export(
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export_type: ExportType,
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output_model: ModelEntity,
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precision: ModelPrecision = ModelPrecision.FP32,
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dump_features: bool = True,
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dump_features: bool = False,
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):
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"""Export function of OTX Action Task."""
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# TODO: add dumping saliency maps and representation vectors according to dump_features flag
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dump_features = False
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if not dump_features:
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logger.warning(
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"Ommitting feature dumping is not implemented."
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"The saliency maps and representation vector outputs will be dumped in the exported model."
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if dump_features:
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raise NotImplementedError(
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"Feature dumping is not implemented for the anomaly task."
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"The saliency maps and representation vector outputs will not be dumped in the exported model."
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)
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# copied from OTX inference_task.py

‎otx/algorithms/anomaly/tasks/inference.py‎

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@@ -252,7 +252,7 @@ def export(
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export_type: ExportType,
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output_model: ModelEntity,
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precision: ModelPrecision = ModelPrecision.FP32,
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dump_features: bool = True,
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dump_features: bool = False,
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) -> None:
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"""Export model to OpenVINO IR.
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@@ -265,11 +265,10 @@ def export(
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Raises:
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Exception: If export_type is not ExportType.OPENVINO
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"""
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# TODO: add dumping saliency maps and representation vectors according to dump_features flag
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if not dump_features:
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logger.warning(
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"Ommitting feature dumping is not implemented."
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"The saliency maps and representation vector outputs will be dumped in the exported model."
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if dump_features:
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raise NotImplementedError(
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"Feature dumping is not implemented for the anomaly task."
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"The saliency maps and representation vector outputs will not be dumped in the exported model."
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)
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self.precision[0] = precision

‎otx/algorithms/classification/tasks/inference.py‎

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@@ -225,7 +225,7 @@ def export(
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export_type: ExportType,
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output_model: ModelEntity,
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precision: ModelPrecision = ModelPrecision.FP32,
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dump_features: bool = True,
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dump_features: bool = False,
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):
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"""Export function of OTX Classification Task."""
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‎otx/algorithms/common/tasks/training_base.py‎

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@@ -336,8 +336,11 @@ def _initialize(self, options=None): # noqa: C901
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options["deploy_cfg"]["dump_features"] = options["dump_features"]
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if options["dump_features"]:
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output_names = options["deploy_cfg"]["ir_config"]["output_names"]
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if "feature_vector" not in output_names and "saliency_map" not in output_names:
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options["deploy_cfg"]["ir_config"]["output_names"] += ["feature_vector", "saliency_map"]
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if "feature_vector" not in output_names:
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options["deploy_cfg"]["ir_config"]["output_names"].append("feature_vector")
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if options["deploy_cfg"]["codebase_config"]["task"] != "Segmentation":
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if "saliency_map" not in output_names:
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options["deploy_cfg"]["ir_config"]["output_names"].append("saliency_map")
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self._initialize_post_hook(options)
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‎otx/algorithms/detection/tasks/inference.py‎

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@@ -244,7 +244,7 @@ def export(
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export_type: ExportType,
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output_model: ModelEntity,
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precision: ModelPrecision = ModelPrecision.FP32,
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dump_features: bool = True,
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dump_features: bool = False,
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):
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"""Export function of OTX Detection Task."""
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# copied from OTX inference_task.py

‎otx/algorithms/segmentation/configs/ocr_lite_hrnet_18/deployment.py‎

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_base_ = ["../base/deployments/base_segmentation_dynamic.py"]
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ir_config = dict(
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output_names=["output", "feature_vector"],
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output_names=["output"],
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)
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backend_config = dict(

‎otx/algorithms/segmentation/configs/ocr_lite_hrnet_18_mod2/deployment.py‎

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_base_ = ["../base/deployments/base_segmentation_dynamic.py"]
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ir_config = dict(
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output_names=["output", "feature_vector"],
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output_names=["output"],
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)
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backend_config = dict(

‎otx/algorithms/segmentation/configs/ocr_lite_hrnet_s_mod2/deployment.py‎

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_base_ = ["../base/deployments/base_segmentation_dynamic.py"]
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ir_config = dict(
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output_names=["output", "feature_vector"],
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output_names=["output"],
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)
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backend_config = dict(

‎otx/algorithms/segmentation/configs/ocr_lite_hrnet_x_mod3/deployment.py‎

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_base_ = ["../base/deployments/base_segmentation_dynamic.py"]
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ir_config = dict(
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output_names=["output", "feature_vector"],
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output_names=["output"],
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)
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backend_config = dict(

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