Merge back bug fixes in release 1.1.2 (#1971)

* Fix exception -> warning for anomaly dump_feature option

* Remove `dataset.with_empty_annotations()` to keep original input structure (#1964)

* Fix OV batch inference (saliency map generation)  (#1965)

* Release 1.1.2 RC1

* Fix classification model download logic to resolve zip issue (#1967)

* Replace model download logic by pytorchcv to resolve zip issue

* Update change log

* Fix pre-commit

* Configure checkpoint before model

* Add pytorchcv to cls/seg requirement

* Hot fix for numpy attr for ResultMediaEntity

* Release 1.1.2

* Update exportable code demo commit

---------

Co-authored-by: Evgeny Tsykunov <evgeny.tsykunov@intel.com>

Author: Songki Choi

CHANGELOG.md

@@ -22,6 +22,15 @@ All notable changes to this project will be documented in this file.
 - OpenVINO(==2022.3) IR inference is not working well on 2-stage models (e.g. Mask-RCNN) exported from torch==1.13.1
   (working well up to torch==1.12.1) (<https://github.com/openvinotoolkit/training_extensions/issues/1906>)
 
+## \[v1.1.2\]
+
+### Bug fixes
+
+- Fix exception -> warning for anomaly dump_feature option
+- Remove `dataset.with_empty_annotations()` to keep original input structure (<https://github.com/openvinotoolkit/training_extensions/pull/1964>)
+- Fix OV batch inference (saliency map generation) (<https://github.com/openvinotoolkit/training_extensions/pull/1965>)
+- Replace EfficentNetB0 model download logic by pytorchcv to resolve zip issue (<https://github.com/openvinotoolkit/training_extensions/pull/1967>)
+
 ## \[v1.1.1\]
 
 ### Bug fixes

otx/algorithms/anomaly/tasks/inference.py

@@ -266,7 +266,7 @@ def export(
             Exception: If export_type is not ExportType.OPENVINO
         """
         if dump_features:
-            raise NotImplementedError(
+            logger.warning(
                 "Feature dumping is not implemented for the anomaly task."
                 "The saliency maps and representation vector outputs will not be dumped in the exported model."
             )

otx/algorithms/classification/adapters/mmcls/tasks/stage.py

@@ -30,8 +30,8 @@ def configure(self, model_cfg, model_ckpt, data_cfg, training=True, **kwargs):
 
         # Recipe + model
         cfg = self.cfg
-        self.configure_model(cfg, model_cfg, **kwargs)
         self.configure_ckpt(cfg, model_ckpt, kwargs.get("pretrained", None))
+        self.configure_model(cfg, model_cfg, **kwargs)
         self.configure_data(cfg, data_cfg, training)
         self.configure_task(cfg, training)
         return cfg

otx/algorithms/classification/tasks/inference.py

@@ -127,7 +127,6 @@ def infer(
         logger.info("called infer()")
         stage_module = "ClsInferrer"
         self._data_cfg = self._init_test_data_cfg(dataset)
-        dataset = dataset.with_empty_annotations()
 
         dump_features = True
         dump_saliency_map = not inference_parameters.is_evaluation if inference_parameters else True
@@ -169,7 +168,6 @@ def explain(
         logger.info("called explain()")
         stage_module = "ClsExplainer"
         self._data_cfg = self._init_test_data_cfg(dataset)
-        dataset = dataset.with_empty_annotations()
 
         results = self._run_task(
             stage_module,

otx/algorithms/classification/tasks/openvino.py

@@ -273,7 +273,7 @@ def explain(
             dataset_item.append_labels(item_labels)
             add_saliency_maps_to_dataset_item(
                 dataset_item=dataset_item,
-                saliency_map=np.copy(saliency_map),
+                saliency_map=saliency_map,
                 model=self.model,
                 labels=self.task_environment.get_labels(),
                 predicted_scored_labels=item_labels,

otx/algorithms/common/adapters/mmcv/models/backbones/efficientnet.py

@@ -18,6 +18,7 @@
 from mmcv.cnn import build_activation_layer
 from mmcv.cnn.bricks import ConvModule
 from mmcv.runner import load_checkpoint
+from pytorchcv.models.model_store import download_model
 from torch import nn
 from torch.nn import init
 
@@ -1308,5 +1309,5 @@ def init_weights(self, pretrained=None):
             load_checkpoint(self, pretrained)
             logger.info(f"init weight - {pretrained}")
         elif pretrained is not None:
-            load_checkpoint(self, pretrained_urls[self.model_name])
+            download_model(net=self, model_name=self.model_name)
             logger.info(f"init weight - {pretrained_urls[self.model_name]}")

otx/algorithms/detection/adapters/openvino/task.py

@@ -465,7 +465,7 @@ def explain(
 
             add_saliency_maps_to_dataset_item(
                 dataset_item=dataset_item,
-                saliency_map=np.copy(saliency_map),
+                saliency_map=saliency_map,
                 model=self.model,
                 labels=labels,
                 predicted_scored_labels=predicted_scored_labels,

otx/api/entities/result_media.py

@@ -82,7 +82,7 @@ def __init__(
         self.annotation_scene = annotation_scene
         self.roi = Annotation(Rectangle.generate_full_box(), labels=[]) if roi is None else roi
         self.label = label
-        self.numpy = numpy
+        self._numpy = np.copy(numpy)
 
     def __repr__(self):
         """Returns a string with all the attributes of the ResultMediaEntity."""

otx/api/usecases/exportable_code/demo/requirements.txt

@@ -1,4 +1,4 @@
 openvino==2022.3.0
 openmodelzoo-modelapi==2022.3.0
-otx @ git+https://github.com/openvinotoolkit/training_extensions/@9753acd6fef11fa83240e6ed3a031fb9a3e62b0f#egg=otx
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@dd03235da2319815227f1b75bce298ee6e8b0f31#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

requirements/classification.txt

@@ -4,3 +4,4 @@ mmcv-full==1.7.0
 mmcls==0.25.0
 timm
 otxdeploy==0.12.1  # FIXME: mmdeploy==? when PyPI packages are available
+pytorchcv

requirements/segmentation.txt

@@ -5,3 +5,4 @@ mmsegmentation==0.30.0
 scikit-image
 otxdeploy==0.12.1  # FIXME: mmdeploy==? when PyPI packages are available
 timm
+pytorchcv