Merge branch 'develop' into pf/adding_anomaly_training_tests

Author: pfinashx

external/anomaly/ote_anomalib/openvino.py

@@ -16,17 +16,15 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import inspect
+import io
 import json
 import os
-import subprocess  # nosec
-import sys
 import tempfile
-from shutil import copyfile, copytree
 from typing import Any, Dict, List, Optional
 from zipfile import ZipFile
 
 import numpy as np
+import ote_anomalib.exportable_code
 from addict import Dict as ADDict
 from anomalib.deploy import OpenVINOInferencer
 from anomalib.post_processing import anomaly_map_to_color_map
@@ -41,12 +39,6 @@
     contains_anomalous_images,
     split_local_global_resultset,
 )
-from ote_anomalib.exportable_code import (
-    AnomalyBase,
-    AnomalyClassification,
-    AnomalyDetection,
-    AnomalySegmentation,
-)
 from ote_anomalib.logging import get_logger
 from ote_sdk.entities.datasets import DatasetEntity
 from ote_sdk.entities.inference_parameters import (
@@ -307,11 +299,17 @@ def optimize(
             if get_nodes_by_type(model, ["FakeQuantize"]):
                 raise RuntimeError("Model is already optimized by POT")
 
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(10)
+
         engine = IEEngine(config=ADDict({"device": "CPU"}), data_loader=data_loader, metric=None)
         pipeline = create_pipeline(algo_config=self._get_optimization_algorithms_configs(), engine=engine)
         compressed_model = pipeline.run(model)
         compress_model_weights(compressed_model)
 
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(90)
+
         with tempfile.TemporaryDirectory() as tempdir:
             save_model(compressed_model, tempdir, model_name="model")
             self.__load_weights(path=os.path.join(tempdir, "model.xml"), output_model=output_model, key="openvino.xml")
@@ -330,6 +328,10 @@ def optimize(
         self.task_environment.model = output_model
         self.inferencer = self.load_inferencer()
 
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(100)
+        logger.info("POT optimization completed")
+
     def load_inferencer(self) -> OpenVINOInferencer:
         """
         Create the OpenVINO inferencer object
@@ -422,57 +424,26 @@ def deploy(self, output_model: ModelEntity) -> None:
 
         task_type = str(self.task_type).lower()
 
-        if self.task_type == TaskType.ANOMALY_CLASSIFICATION:
-            selected_class = AnomalyClassification
-        elif self.task_type == TaskType.ANOMALY_DETECTION:
-            selected_class = AnomalyDetection
-        elif self.task_type == TaskType.ANOMALY_SEGMENTATION:
-            selected_class = AnomalySegmentation
-        else:
-            raise ValueError(
-                f"{self.task_type} is not supported. "
-                "Only Anomaly <Classification, Detection, Segmentation> are supported"
-            )
-
         parameters["type_of_model"] = task_type
         parameters["converter_type"] = task_type.upper()
         parameters["model_parameters"] = self._get_openvino_configuration()
-        name_of_package = "demo_package"
-
-        with tempfile.TemporaryDirectory() as tempdir:
-            copyfile(os.path.join(work_dir, "setup.py"), os.path.join(tempdir, "setup.py"))
-            copyfile(os.path.join(work_dir, "requirements.txt"), os.path.join(tempdir, "requirements.txt"))
-            copytree(os.path.join(work_dir, name_of_package), os.path.join(tempdir, name_of_package))
-            config_path = os.path.join(tempdir, name_of_package, "config.json")
-            with open(config_path, "w", encoding="utf-8") as file:
-                json.dump(parameters, file, ensure_ascii=False, indent=4)
-
-            copyfile(inspect.getfile(selected_class), os.path.join(tempdir, name_of_package, "model.py"))
-            copyfile(inspect.getfile(AnomalyBase), os.path.join(tempdir, name_of_package, "base.py"))
-
-            # create wheel package
-            subprocess.run(
-                [
-                    sys.executable,
-                    os.path.join(tempdir, "setup.py"),
-                    "bdist_wheel",
-                    "--dist-dir",
-                    tempdir,
-                    "clean",
-                    "--all",
-                ],
-                check=True,
-            )
-            wheel_file_name = [f for f in os.listdir(tempdir) if f.endswith(".whl")][0]
-
-            with ZipFile(os.path.join(tempdir, "openvino.zip"), "w") as arch:
-                arch.writestr(os.path.join("model", "model.xml"), self.task_environment.model.get_data("openvino.xml"))
-                arch.writestr(os.path.join("model", "model.bin"), self.task_environment.model.get_data("openvino.bin"))
-                arch.write(os.path.join(tempdir, "requirements.txt"), os.path.join("python", "requirements.txt"))
-                arch.write(os.path.join(work_dir, "README.md"), os.path.join("python", "README.md"))
-                arch.write(os.path.join(work_dir, "LICENSE"), os.path.join("python", "LICENSE"))
-                arch.write(os.path.join(work_dir, "demo.py"), os.path.join("python", "demo.py"))
-                arch.write(os.path.join(tempdir, wheel_file_name), os.path.join("python", wheel_file_name))
-            with open(os.path.join(tempdir, "openvino.zip"), "rb") as output_arch:
-                output_model.exportable_code = output_arch.read()
+        zip_buffer = io.BytesIO()
+        with ZipFile(zip_buffer, "w") as arch:
+            # model files
+            arch.writestr(os.path.join("model", "model.xml"), self.task_environment.model.get_data("openvino.xml"))
+            arch.writestr(os.path.join("model", "model.bin"), self.task_environment.model.get_data("openvino.bin"))
+            arch.writestr(os.path.join("model", "config.json"), json.dumps(parameters, ensure_ascii=False, indent=4))
+            # model_wrappers files
+            for root, _, files in os.walk(os.path.dirname(ote_anomalib.exportable_code.__file__)):
+                for file in files:
+                    file_path = os.path.join(root, file)
+                    arch.write(
+                        file_path, os.path.join("python", "model_wrappers", file_path.split("exportable_code/")[1])
+                    )
+            # other python files
+            arch.write(os.path.join(work_dir, "requirements.txt"), os.path.join("python", "requirements.txt"))
+            arch.write(os.path.join(work_dir, "LICENSE"), os.path.join("python", "LICENSE"))
+            arch.write(os.path.join(work_dir, "README.md"), os.path.join("python", "README.md"))
+            arch.write(os.path.join(work_dir, "demo.py"), os.path.join("python", "demo.py"))
+        output_model.exportable_code = zip_buffer.getvalue()
         logger.info("Deployment completed.")

external/anomaly/requirements.txt

@@ -1,5 +1,5 @@
 anomalib @ git+https://github.com/openvinotoolkit/anomalib.git@834d45ab1761841ba4041eb4472f01fb63d344a6
-openmodelzoo-modelapi @ git+https://github.com/openvinotoolkit/open_model_zoo/@releases/2021/SCMVP#egg=openmodelzoo-modelapi&subdirectory=demos/common/python
+openmodelzoo-modelapi @ git+https://github.com/openvinotoolkit/open_model_zoo/@releases/2022/SCv1.1#egg=openmodelzoo-modelapi&subdirectory=demos/common/python
 openvino==2022.1.0
 openvino-dev==2022.1.0
 onnx==1.10.1

external/anomaly/tests/ote_cli/test_anomaly_classification.py

@@ -128,6 +128,7 @@ def test_nncf_export(self, template):
 
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
+    @pytest.mark.xfail(reason="CVS-83124")
     def test_nncf_eval(self, template):
         if template.entrypoints.nncf is None:
             pytest.skip("nncf entrypoint is none")

external/anomaly/tests/ote_cli/test_anomaly_detection.py

@@ -127,6 +127,7 @@ def test_nncf_export(self, template):
 
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
+    @pytest.mark.xfail(reason="CVS-83124")
     def test_nncf_eval(self, template):
         if template.entrypoints.nncf is None:
             pytest.skip("nncf entrypoint is none")

external/anomaly/tests/ote_cli/test_anomaly_segmentation.py

@@ -128,6 +128,7 @@ def test_nncf_export(self, template):
 
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
+    @pytest.mark.xfail(reason="CVS-83124")
     def test_nncf_eval(self, template):
         if template.entrypoints.nncf is None:
             pytest.skip("nncf entrypoint is none")

external/deep-object-reid/requirements.txt

@@ -1,4 +1,4 @@
 nncf @ git+https://github.com/openvinotoolkit/nncf@464244204fc2c5e80c8164c17d8d266ccae50062#egg=nncf
 openvino==2022.1.0
 openvino-dev==2022.1.0
-openmodelzoo-modelapi @ git+https://github.com/openvinotoolkit/open_model_zoo/@releases/2021/SCMVP#egg=openmodelzoo-modelapi&subdirectory=demos/common/python
+openmodelzoo-modelapi @ git+https://github.com/openvinotoolkit/open_model_zoo/@releases/2022/SCv1.1#egg=openmodelzoo-modelapi&subdirectory=demos/common/python

external/deep-object-reid/tests/ote_cli/test_classification.py

@@ -151,7 +151,7 @@ def test_nncf_eval(self, template):
             pytest.skip("nncf entrypoint is none")
 
         nncf_eval_testing(template, root, ote_dir, args, threshold=0.001)
-    
+
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_nncf_eval_openvino(self, template):

external/deep-object-reid/torchreid_tasks/model_wrappers/classification.py

@@ -45,6 +45,9 @@ def _check_io_number(self, inp, outp):
 
     def _get_outputs(self):
         layer_name = 'logits'
+        for name, meta in self.outputs.items():
+            if 'logits' in meta.names:
+                layer_name = name
         layer_shape = self.outputs[layer_name].shape
 
         if len(layer_shape) != 2 and len(layer_shape) != 4:

external/deep-object-reid/torchreid_tasks/openvino_task.py

@@ -12,20 +12,17 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import inspect
+import io
 import json
 import logging
 import os
-import subprocess  # nosec
-import sys
 import tempfile
-from shutil import copyfile, copytree
 from typing import Any, Dict, Optional, Tuple, Union
 
 from addict import Dict as ADDict
 
 import numpy as np
-
+import torchreid_tasks.model_wrappers as model_wrappers
 from ote_sdk.usecases.exportable_code import demo
 from ote_sdk.entities.annotation import AnnotationSceneEntity
 from ote_sdk.entities.datasets import DatasetEntity
@@ -72,7 +69,6 @@
 from torchreid_tasks.utils import get_multihead_class_info
 
 from zipfile import ZipFile
-from . import model_wrappers
 
 logger = logging.getLogger(__name__)
 
@@ -204,39 +200,31 @@ def deploy(self,
         logger.info('Deploying the model')
 
         work_dir = os.path.dirname(demo.__file__)
-        model_file = inspect.getfile(type(self.inferencer.model))
         parameters = {}
         parameters['type_of_model'] = 'ote_classification'
         parameters['converter_type'] = 'CLASSIFICATION'
         parameters['model_parameters'] = self.inferencer.configuration
         parameters['model_parameters']['labels'] = LabelSchemaMapper.forward(self.task_environment.label_schema)
-        name_of_package = "demo_package"
-        with tempfile.TemporaryDirectory() as tempdir:
-            copyfile(os.path.join(work_dir, "setup.py"), os.path.join(tempdir, "setup.py"))
-            copyfile(os.path.join(work_dir, "requirements.txt"), os.path.join(tempdir, "requirements.txt"))
-            copytree(os.path.join(work_dir, name_of_package), os.path.join(tempdir, name_of_package))
-            config_path = os.path.join(tempdir, name_of_package, "config.json")
-            with open(config_path, "w", encoding='utf-8') as f:
-                json.dump(parameters, f, ensure_ascii=False, indent=4)
-            # generate model.py
-            if (inspect.getmodule(self.inferencer.model) in
-               [module[1] for module in inspect.getmembers(model_wrappers, inspect.ismodule)]):
-                copyfile(model_file, os.path.join(tempdir, name_of_package, "model.py"))
-            # create wheel package
-            subprocess.run([sys.executable, os.path.join(tempdir, "setup.py"), 'bdist_wheel',
-                            '--dist-dir', tempdir, 'clean', '--all'], check=True)
-            wheel_file_name = [f for f in os.listdir(tempdir) if f.endswith('.whl')][0]
-
-            with ZipFile(os.path.join(tempdir, "openvino.zip"), 'w') as zip_f:
-                zip_f.writestr(os.path.join("model", "model.xml"), self.model.get_data("openvino.xml"))
-                zip_f.writestr(os.path.join("model", "model.bin"), self.model.get_data("openvino.bin"))
-                zip_f.write(os.path.join(tempdir, "requirements.txt"), os.path.join("python", "requirements.txt"))
-                zip_f.write(os.path.join(work_dir, "README.md"), os.path.join("python", "README.md"))
-                zip_f.write(os.path.join(work_dir, "LICENSE"), os.path.join("python", "LICENSE"))
-                zip_f.write(os.path.join(work_dir, "demo.py"), os.path.join("python", "demo.py"))
-                zip_f.write(os.path.join(tempdir, wheel_file_name), os.path.join("python", wheel_file_name))
-            with open(os.path.join(tempdir, "openvino.zip"), "rb") as file:
-                output_model.exportable_code = file.read()
+
+        zip_buffer = io.BytesIO()
+        with ZipFile(zip_buffer, 'w') as arch:
+            # model files
+            arch.writestr(os.path.join("model", "model.xml"), self.model.get_data("openvino.xml"))
+            arch.writestr(os.path.join("model", "model.bin"), self.model.get_data("openvino.bin"))
+            arch.writestr(
+                os.path.join("model", "config.json"), json.dumps(parameters, ensure_ascii=False, indent=4)
+            )
+            # model_wrappers files
+            for root, dirs, files in os.walk(os.path.dirname(model_wrappers.__file__)):
+                for file in files:
+                    file_path = os.path.join(root, file)
+                    arch.write(file_path, os.path.join("python", "model_wrappers", file_path.split("model_wrappers/")[1]))
+            # other python files
+            arch.write(os.path.join(work_dir, "requirements.txt"), os.path.join("python", "requirements.txt"))
+            arch.write(os.path.join(work_dir, "LICENSE"), os.path.join("python", "LICENSE"))
+            arch.write(os.path.join(work_dir, "README.md"), os.path.join("python", "README.md"))
+            arch.write(os.path.join(work_dir, "demo.py"), os.path.join("python", "demo.py"))
+        output_model.exportable_code = zip_buffer.getvalue()
         logger.info('Deploying completed')
 
     def optimize(self,
@@ -269,6 +257,9 @@ def optimize(self,
             if get_nodes_by_type(model, ["FakeQuantize"]):
                 raise RuntimeError("Model is already optimized by POT")
 
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(10)
+
         engine_config = ADDict({
             'device': 'CPU'
         })
@@ -296,6 +287,9 @@ def optimize(self,
 
         compress_model_weights(compressed_model)
 
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(90)
+
         with tempfile.TemporaryDirectory() as tempdir:
             save_model(compressed_model, tempdir, model_name="model")
             with open(os.path.join(tempdir, "model.xml"), "rb") as f:
@@ -313,3 +307,7 @@ def optimize(self,
 
         self.model = output_model
         self.inferencer = self.load_inferencer()
+
+        if optimization_parameters is not None:
+            optimization_parameters.update_progress(100)
+        logger.info('POT optimization completed')