Upgrade NNCF & OpenVINO

.github/workflows/pre_merge.yml

@@ -22,6 +22,8 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
+      - name: Installing Rust toolchain
+        uses: actions-rust-lang/setup-rust-toolchain@v1
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
@@ -52,6 +54,8 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
+      - name: Installing Rust toolchain
+        uses: actions-rust-lang/setup-rust-toolchain@v1
       - name: Install Python
         uses: actions/setup-python@v4
         with:

.github/workflows/run_tests_in_tox.yml

@@ -42,6 +42,8 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
+      - name: Installing Rust toolchain
+        uses: actions-rust-lang/setup-rust-toolchain@v1
       - name: Set up Python
         uses: actions/setup-python@v4
         with:

CHANGELOG.md

@@ -8,14 +8,18 @@ All notable changes to this project will be documented in this file.
 
 - Add zero-shot visual prompting (https://github.com/openvinotoolkit/training_extensions/pull/2616)
 
+### Enhancements
+
+- Upgrade NNCF to 2.7 and OpenVINO to 2023.2 (<https://github.com/openvinotoolkit/training_extensions/pull/2656>)
+
 ## \[v1.5.0\]
 
 ### New features
 
 - Enable configurable confidence threshold for otx eval and export (<https://github.com/openvinotoolkit/training_extensions/pull/2388>)
 - Add YOLOX variants as new object detector models (<https://github.com/openvinotoolkit/training_extensions/pull/2402>)
 - Enable FeatureVectorHook to support action tasks (<https://github.com/openvinotoolkit/training_extensions/pull/2408>)
-- Add ONNX metadata to detection, instance segmantation, and segmentation models (<https://github.com/openvinotoolkit/training_extensions/pull/2418>)
+- Add ONNX metadata to detection, instance segmentation, and segmentation models (<https://github.com/openvinotoolkit/training_extensions/pull/2418>)
 - Add a new feature to configure input size (<https://github.com/openvinotoolkit/training_extensions/pull/2420>)
 - Introduce the OTXSampler and AdaptiveRepeatDataHook to achieve faster training at the small data regime (<https://github.com/openvinotoolkit/training_extensions/pull/2428>)
 - Add a new object detector Lite-DINO (<https://github.com/openvinotoolkit/training_extensions/pull/2457>)

requirements/base.txt

@@ -4,7 +4,7 @@ natsort==8.1.*
 prettytable==3.9.*
 protobuf==3.20.*
 pyyaml
-datumaro~=1.6.0rc0
+datumaro @ git+https://github.com/openvinotoolkit/datumaro/@3782380de8788dabd32eb18e872d4d075f934e8e#egg=datumaro
 psutil==5.9.*
 scipy==1.10.*
 bayesian-optimization==1.4.*

requirements/openvino.txt

@@ -1,8 +1,8 @@
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
 # OpenVINO Requirements.                                                      #
-nncf==2.6.0
+nncf==2.7.0
 onnx==1.13.0
-openvino-model-api==0.1.6
-openvino==2023.1.0
-openvino-dev==2023.1.0
+openvino-model-api==0.1.7
+openvino==2023.2.0
+openvino-dev==2023.2.0
 openvino-telemetry==2023.2.*

src/otx/algorithms/detection/adapters/mmdet/task.py

@@ -309,6 +309,8 @@
 
         dump_features = True
         dump_saliency_map = not inference_parameters.is_evaluation if inference_parameters else True
+        if isinstance(self, NNCFBaseTask):
+            dump_saliency_map = False
 
         self._init_task()
 

src/otx/algorithms/detection/configs/instance_segmentation/convnext_maskrcnn/compression_config.json

@@ -20,6 +20,11 @@
           "algorithm": "quantization",
           "initializer": {
             "range": {
+              "type": "percentile",
+              "params": {
+                "min_percentile": 0,
+                "max_percentile": 100
+              },
               "num_init_samples": 1000
             },
             "batchnorm_adaptation": {

src/otx/algorithms/detection/configs/instance_segmentation/efficientnetb2b_maskrcnn/compression_config.json

@@ -20,6 +20,11 @@
           "algorithm": "quantization",
           "initializer": {
             "range": {
+              "type": "percentile",
+              "params": {
+                "min_percentile": 0,
+                "max_percentile": 100
+              },
               "num_init_samples": 1000
             },
             "batchnorm_adaptation": {

src/otx/algorithms/detection/configs/instance_segmentation/maskrcnn_swin_t/compression_config.json

@@ -20,6 +20,11 @@
           "algorithm": "quantization",
           "initializer": {
             "range": {
+              "type": "percentile",
+              "params": {
+                "min_percentile": 0,
+                "max_percentile": 100
+              },
               "num_init_samples": 1000
             },
             "batchnorm_adaptation": {

src/otx/algorithms/detection/configs/instance_segmentation/resnet50_maskrcnn/compression_config.json

@@ -20,6 +20,11 @@
           "algorithm": "quantization",
           "initializer": {
             "range": {
+              "type": "percentile",
+              "params": {
+                "min_percentile": 0,
+                "max_percentile": 100
+              },
               "num_init_samples": 1000
             },
             "batchnorm_adaptation": {

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

@@ -1,4 +1,4 @@
-openvino==2023.0
-openvino-model-api==0.1.6
-otx @ git+https://github.com/openvinotoolkit/training_extensions/@0e1200f4cc5343dcac597674fc97a898fc9829c2#egg=otx
+openvino==2023.2.0
+openvino-model-api==0.1.7
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@2988fdc51ef7e4a136a9d4e09602b3844d7bafec#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

tests/e2e/cli/classification/reference/Custom_Image_Classification_EfficientNet-V2-S/compressed_model.yml

@@ -2,14 +2,14 @@ TestToolsHierarchicalClassification:
   nncf:
     number_of_fakequantizers: 267
   ptq:
-    number_of_fakequantizers: 177
+    number_of_fakequantizers: 207
 TestToolsMultiClassClassification:
   nncf:
     number_of_fakequantizers: 267
   ptq:
-    number_of_fakequantizers: 177
+    number_of_fakequantizers: 207
 TestToolsMultilabelClassification:
   nncf:
     number_of_fakequantizers: 269
   ptq:
-    number_of_fakequantizers: 179
+    number_of_fakequantizers: 209

tests/e2e/cli/classification/reference/Custom_Image_Classification_EfficinetNet-B0/compressed_model.yml

@@ -2,14 +2,14 @@ TestToolsHierarchicalClassification:
   nncf:
     number_of_fakequantizers: 124
   ptq:
-    number_of_fakequantizers: 76
+    number_of_fakequantizers: 92
 TestToolsMultiClassClassification:
   nncf:
     number_of_fakequantizers: 124
   ptq:
-    number_of_fakequantizers: 76
+    number_of_fakequantizers: 92
 TestToolsMultilabelClassification:
   nncf:
     number_of_fakequantizers: 126
   ptq:
-    number_of_fakequantizers: 78
+    number_of_fakequantizers: 94

tests/e2e/cli/instance_segmentation/reference/Custom_Counting_Instance_Segmentation_MaskRCNN_EfficientNetB2B/compressed_model.yml

@@ -1,14 +1,14 @@
 TestToolsOTXInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 204
-  ptq:
-    number_of_fakequantizers: 137
   pot:
     number_of_fakequantizers: 137
+  ptq:
+    number_of_fakequantizers: 160
 TestToolsTilingInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 204
-  ptq:
-    number_of_fakequantizers: 137
   pot:
     number_of_fakequantizers: 137
+  ptq:
+    number_of_fakequantizers: 160

tests/e2e/cli/instance_segmentation/reference/Custom_Counting_Instance_Segmentation_MaskRCNN_ResNet50/compressed_model.yml

@@ -1,14 +1,14 @@
 TestToolsOTXInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 97
-  ptq:
-    number_of_fakequantizers: 99
   pot:
     number_of_fakequantizers: 99
+  ptq:
+    number_of_fakequantizers: 99
 TestToolsTilingInstanceSegmentation:
   nncf:
     number_of_fakequantizers: 97
-  ptq:
-    number_of_fakequantizers: 99
   pot:
     number_of_fakequantizers: 99
+  ptq:
+    number_of_fakequantizers: 99

tests/e2e/cli/semantic_segmentation/reference/Custom_Semantic_Segmentation_Lite-HRNet-x-mod3_OCR/compressed_model.yml

@@ -2,4 +2,4 @@ TestToolsOTXSegmentation:
   nncf:
     number_of_fakequantizers: 1138
   ptq:
-    number_of_fakequantizers: 942
+    number_of_fakequantizers: 1026