Mergeback 1.4.2rc5 (#2511)

* Bug fix for albumentations (#2467) * bug fix for legacy openvino models * Add a dummy hierarchical config required by MAPI (#2483) * Bump datumaro version (#2502) * Upgrade nncf version (#2459) * Update FQ refs after NNCF upgrade --------- Co-authored-by: Ashwin Vaidya <ashwin.vaidya@intel.com> Co-authored-by: Ashwin Vaidya <ashwinitinvaidya@gmail.com> Co-authored-by: Vladislav Sovrasov <sovrasov.vlad@gmail.com>
openvinotoolkit · Sep 20, 2023 · 12bf332 · 12bf332
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -34,6 +34,21 @@ All notable changes to this project will be documented in this file.
 - OpenVINO(==2023.0) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch>=1.13.1
 - NNCF QAT optimization is disabled for MaskRCNN models due to CUDA runtime error in ROIAlign kernel on torch==2.0.1
 
+## \[v1.4.2\]
+
+### Enhancements
+
+- Bump datumaro version to 1.5.0rc0 (<https://github.com/openvinotoolkit/training_extensions/pull/2470>)
+- Set tox version constraint (<https://github.com/openvinotoolkit/training_extensions/pull/2472>)
+- Add model category attributes to model template (<https://github.com/openvinotoolkit/training_extensions/pull/2439>)
+
+### Bug fixes
+
+- Bug fix for albumentations (<https://github.com/openvinotoolkit/training_extensions/pull/2467>)
+- Add workaround for the incorrect meta info M-RCNN (used for XAI) (<https://github.com/openvinotoolkit/training_extensions/pull/2437>)
+- Fix label list order for h-label classification (<https://github.com/openvinotoolkit/training_extensions/pull/2440>)
+- Modified fq numbers for lite HRNET e2e tests (<https://github.com/openvinotoolkit/training_extensions/pull/2445>)
+
 ## \[v1.4.1\]
 
 ### Enhancements

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -11,14 +11,21 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../../src"))
+
+from otx import __version__
+
 # ruff: noqa
 
 # -- Project information ----------------------------------------------------- #
 
 project = 'OpenVINO™ Training Extensions'
 copyright = '2023, OpenVINO™ Training Extensions Contributors'
 author = 'OpenVINO™ Training Extensions Contributors'
-release = '1.5.0'
+release = __version__
 
 # -- General configuration --------------------------------------------------- #
 

diff --git a/requirements/base.txt b/requirements/base.txt
@@ -4,7 +4,7 @@ natsort>=6.0.0
 prettytable
 protobuf>=3.20.0
 pyyaml
-datumaro==1.5.0rc0
+datumaro~=1.5.0
 psutil
 scipy>=1.8
 bayesian-optimization>=1.2.0

diff --git a/requirements/openvino.txt b/requirements/openvino.txt
@@ -1,6 +1,6 @@
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
 # OpenVINO Requirements.                                                      #
-nncf==2.5.0
+nncf==2.6.0
 onnx==1.13.0
 openvino-model-api==0.1.5
 openvino==2023.0

diff --git a/src/otx/algorithms/anomaly/tasks/openvino.py b/src/otx/algorithms/anomaly/tasks/openvino.py
@@ -242,7 +242,8 @@ def _populate_metadata_legacy(self, model: ModelEntity) -> Dict[str, Any]:
             to_tensor=True,
         )
         metadata = {
-            "transform": transform.to_dict(),
+            # TODO: Replace with transform.to_dict() when OTX supports albumentations 1.3.0
+            "transform": {"transform": transform._to_dict()},
             "image_threshold": image_threshold,
             "pixel_threshold": pixel_threshold,
             "min": min_value,

diff --git a/src/otx/algorithms/classification/adapters/openvino/task.py b/src/otx/algorithms/classification/adapters/openvino/task.py
@@ -36,6 +36,7 @@
 from otx.algorithms.classification.configs import ClassificationConfig
 from otx.algorithms.classification.utils import (
     get_cls_deploy_config,
+    get_cls_inferencer_configuration,
     get_hierarchical_label_list,
 )
 from otx.algorithms.common.utils import OTXOpenVinoDataLoader
@@ -113,7 +114,19 @@ def __init__(
             max_num_requests=num_requests,
             plugin_config={"PERFORMANCE_HINT": "THROUGHPUT"},
         )
-        self.model = Model.create_model(model_adapter, "Classification", {}, preload=True)
+        self.configuration = get_cls_inferencer_configuration(self.label_schema)
+
+        # create a dummy hierarchical config for backward compatibility, which is not actually used
+        if self.configuration["hierarchical"]:
+            try:
+                model_adapter.get_rt_info(["model_info", "hierarchical_config"])
+            except RuntimeError:
+                self.configuration["hierarchical_config"] = json.dumps(
+                    {"cls_heads_info": {"label_to_idx": [], "all_groups": []}, "label_tree_edges": []}
+                )
+
+        self.model = Model.create_model(model_adapter, "Classification", self.configuration, preload=True)
+
         self.converter = ClassificationToAnnotationConverter(self.label_schema)
         self.callback_exceptions: List[Exception] = []
         self.model.inference_adapter.set_callback(self._async_callback)

diff --git a/src/otx/algorithms/common/tasks/nncf_task.py b/src/otx/algorithms/common/tasks/nncf_task.py
@@ -245,7 +245,7 @@ def model_builder(
 
         if is_export:
             compression_ctrl.prepare_for_export()
-            model.disable_dynamic_graph_building()
+            model.nncf.disable_dynamic_graph_building()
 
         if return_compression_ctrl:
             return compression_ctrl, model

diff --git a/src/otx/api/usecases/exportable_code/demo/requirements.txt b/src/otx/api/usecases/exportable_code/demo/requirements.txt
@@ -1,4 +1,4 @@
 openvino==2023.0
 openvino-model-api==0.1.5
-otx @ git+https://github.com/sovrasov/openvino_training_extensions/@38a9d7b20e3359d6139616c7127eac98d304c9f6#egg=otx
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@e066a04834952257c1c9384a09f472d13b76b264#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime
diff --git a/tests/e2e/cli/anomaly/reference/ote_anomaly_classification_padim/compressed_model.yml b/tests/e2e/cli/anomaly/reference/ote_anomaly_classification_padim/compressed_model.yml
@@ -3,3 +3,5 @@ TestToolsAnomalyClassification:
     number_of_fakequantizers: 26
   ptq:
     number_of_fakequantizers: 27
+  pot:
+    number_of_fakequantizers: 28
diff --git a/tests/e2e/cli/anomaly/reference/ote_anomaly_detection_padim/compressed_model.yml b/tests/e2e/cli/anomaly/reference/ote_anomaly_detection_padim/compressed_model.yml
@@ -3,3 +3,5 @@ TestToolsAnomalyDetection:
     number_of_fakequantizers: 26
   ptq:
     number_of_fakequantizers: 27
+  pot:
+    number_of_fakequantizers: 28
diff --git a/tests/e2e/cli/anomaly/reference/ote_anomaly_segmentation_padim/compressed_model.yml b/tests/e2e/cli/anomaly/reference/ote_anomaly_segmentation_padim/compressed_model.yml
@@ -3,3 +3,5 @@ TestToolsAnomalySegmentation:
     number_of_fakequantizers: 26
   ptq:
     number_of_fakequantizers: 27
+  pot:
+    number_of_fakequantizers: 28
diff --git a/...rence/Custom_Counting_Instance_Segmentation_MaskRCNN_EfficientNetB2B/compressed_model.yml b/...rence/Custom_Counting_Instance_Segmentation_MaskRCNN_EfficientNetB2B/compressed_model.yml
@@ -3,8 +3,12 @@ TestToolsOTXInstanceSegmentation:
     number_of_fakequantizers: 204
   ptq:
     number_of_fakequantizers: 174
+  pot:
+    number_of_fakequantizers: 137
 TestToolsTilingInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 204
   ptq:
     number_of_fakequantizers: 174
+  pot:
+    number_of_fakequantizers: 137
diff --git a/...on/reference/Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50/compressed_model.yml b/...on/reference/Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50/compressed_model.yml
@@ -3,8 +3,12 @@ TestToolsOTXInstanceSegmentation:
     number_of_fakequantizers: 97
   ptq:
     number_of_fakequantizers: 136
+  pot:
+    number_of_fakequantizers: 99
 TestToolsTilingInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 97
   ptq:
     number_of_fakequantizers: 136
+  pot:
+    number_of_fakequantizers: 99
diff --git a/tests/unit/core/data/test_helpers.py b/tests/unit/core/data/test_helpers.py
@@ -6,8 +6,12 @@
 import os
 
 import cv2
-import datumaro as dm
 import numpy as np
+from datumaro.components.annotation import Label, Bbox, Mask
+from datumaro.components.dataset import Dataset
+from datumaro.components.dataset_base import DatasetItem
+from datumaro.components.media import ImageFromFile, ImageFromNumpy
+
 
 from otx.api.entities.model_template import TaskType
 
@@ -107,7 +111,7 @@ def generate_datumaro_dataset_item(
     image_shape: np.array = np.array((5, 5, 3)),
     mask_shape: np.array = np.array((5, 5)),
     temp_dir: Optional[str] = None,
-) -> dm.DatasetItem:
+) -> DatasetItem:
     """Generate Datumaro DatasetItem.
 
     Args:
@@ -119,20 +123,22 @@ def generate_datumaro_dataset_item(
         temp_dir (str): directory to save image data
 
     Returns:
-        dm.DatasetItem: Datumaro DatasetItem
+        DatasetItem: Datumaro DatasetItem
     """
     ann_task_dict = {
-        "classification": dm.Label(label=0),
-        "detection": dm.Bbox(1, 2, 3, 4, label=0),
-        "segmentation": dm.Mask(np.zeros(mask_shape)),
+        "classification": Label(label=0),
+        "detection": Bbox(1, 2, 3, 4, label=0),
+        "segmentation": Mask(np.zeros(mask_shape)),
     }
 
     if temp_dir:
         path = os.path.join(temp_dir, "image.png")
         cv2.imwrite(path, np.ones(image_shape))
-        return dm.DatasetItem(id=item_id, subset=subset, image=path, annotations=[ann_task_dict[task]])
+        return DatasetItem(id=item_id, subset=subset, media=ImageFromFile(path), annotations=[ann_task_dict[task]])
 
-    return dm.DatasetItem(id=item_id, subset=subset, image=np.ones(image_shape), annotations=[ann_task_dict[task]])
+    return DatasetItem(
+        id=item_id, subset=subset, media=ImageFromNumpy(np.ones(image_shape)), annotations=[ann_task_dict[task]]
+    )
 
 
 def generate_datumaro_dataset(
@@ -141,7 +147,7 @@ def generate_datumaro_dataset(
     num_data: int = 1,
     image_shape: np.array = np.array((5, 5, 3)),
     mask_shape: np.array = np.array((5, 5)),
-) -> dm.Dataset:
+) -> Dataset:
     """Generate Datumaro Dataset.
 
     Args:
@@ -154,7 +160,7 @@ def generate_datumaro_dataset(
     Returns:
         dm.Dataset: Datumaro Dataset
     """
-    dataset_items: dm.DatasetItem = []
+    dataset_items: DatasetItem = []
     for subset in subsets:
         for idx in range(num_data):
             dataset_items.append(
@@ -166,4 +172,4 @@ def generate_datumaro_dataset(
                     mask_shape=mask_shape,
                 )
             )
-    return dm.Dataset.from_iterable(dataset_items, categories=["cat", "dog"])
+    return Dataset.from_iterable(dataset_items, categories=["cat", "dog"])