linter

external/anomaly/anomaly_classification/configs/padim/configuration.yaml

@@ -127,7 +127,7 @@ nncf_optimization:
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
-      rules: [ ]
+      rules: []
       type: UI_RULES
     value: false
     visible_in_ui: false

external/anomaly/anomaly_classification/configs/stfpm/configuration.yaml

@@ -176,7 +176,7 @@ nncf_optimization:
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
-      rules: [ ]
+      rules: []
       type: UI_RULES
     value: false
     visible_in_ui: false

external/anomaly/anomaly_segmentation/configs/padim/configuration.yaml

@@ -127,7 +127,7 @@ nncf_optimization:
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
-      rules: [ ]
+      rules: []
       type: UI_RULES
     value: false
     visible_in_ui: false

external/anomaly/anomaly_segmentation/configs/stfpm/configuration.yaml

@@ -176,7 +176,7 @@ nncf_optimization:
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
-      rules: [ ]
+      rules: []
       type: UI_RULES
     value: false
     visible_in_ui: false

external/anomaly/ote_anomalib/configs/configuration.py

@@ -19,17 +19,19 @@
 from sys import maxsize
 
 from attr import attrs
+from ote_anomalib.configs.configuration_enums import POTQuantizationPreset
 from ote_sdk.configuration import ConfigurableParameters
-from ote_sdk.configuration.elements import (ParameterGroup,
-                                            add_parameter_group,
-                                            boolean_attribute,
-                                            configurable_boolean,
-                                            configurable_integer, selectable,
-                                            string_attribute)
+from ote_sdk.configuration.elements import (
+    ParameterGroup,
+    add_parameter_group,
+    boolean_attribute,
+    configurable_boolean,
+    configurable_integer,
+    selectable,
+    string_attribute,
+)
 from ote_sdk.configuration.model_lifecycle import ModelLifecycle
 
-from ote_anomalib.configs.configuration_enums import POTQuantizationPreset
-
 
 @attrs
 class BaseAnomalyConfig(ConfigurableParameters):
@@ -98,6 +100,10 @@ class POTParameters(ParameterGroup):
 
     @attrs
     class NNCFOptimization(ParameterGroup):
+        """
+        Parameters for NNCF optimization
+        """
+
         header = string_attribute("Optimization by NNCF")
         description = header
 
@@ -117,7 +123,7 @@ class NNCFOptimization(ParameterGroup):
             default_value=False,
             header="Whether filter pruning is supported",
             description="Whether filter pruning is supported",
-            affects_outcome_of=ModelLifecycle.TRAINING
+            affects_outcome_of=ModelLifecycle.TRAINING,
         )
 
     dataset = add_parameter_group(DatasetParameters)

external/anomaly/ote_anomalib/nncf_task.py

@@ -17,9 +17,7 @@
 import io
 import json
 import os
-import subprocess  # nosec
-from glob import glob
-from typing import Dict, Optional
+from typing import Dict, Optional, Union
 
 import torch
 from anomalib.models import AnomalyModule, get_model
@@ -30,23 +28,20 @@
     is_state_nncf,
     wrap_nncf_model,
 )
+from nncf.api.compression import CompressionAlgorithmController
 from ote_anomalib import AnomalyInferenceTask
 from ote_anomalib.callbacks import ProgressCallback
 from ote_anomalib.data import OTEAnomalyDataModule
 from ote_anomalib.logging import get_logger
 from ote_sdk.entities.datasets import DatasetEntity
-from ote_sdk.entities.metrics import Performance, ScoreMetric
 from ote_sdk.entities.model import (
     ModelEntity,
-    ModelFormat,
     ModelOptimizationType,
     ModelPrecision,
     OptimizationMethod,
 )
 from ote_sdk.entities.optimization_parameters import OptimizationParameters
 from ote_sdk.entities.task_environment import TaskEnvironment
-from ote_sdk.serialization.label_mapper import label_schema_to_bytes
-from ote_sdk.usecases.tasks.interfaces.export_interface import ExportType
 from ote_sdk.usecases.tasks.interfaces.optimization_interface import (
     IOptimizationTask,
     OptimizationType,
@@ -65,8 +60,7 @@ def __init__(self, task_environment: TaskEnvironment) -> None:
         Args:
             task_environment (TaskEnvironment): OTE Task environment.
         """
-        self.val_dataloader = None
-        self.compression_ctrl = None
+        self.compression_ctrl: Union[CompressionAlgorithmController, None] = None
         self.nncf_preset = "nncf_quantization"
         super().__init__(task_environment)
         self.optimization_type = ModelOptimizationType.NNCF
@@ -76,7 +70,10 @@ def _set_attributes_by_hyperparams(self):
         pruning = self.hyper_parameters.nncf_optimization.enable_pruning
         if quantization and pruning:
             self.nncf_preset = "nncf_quantization_pruning"
-            self.optimization_methods = [OptimizationMethod.QUANTIZATION, OptimizationMethod.FILTER_PRUNING]
+            self.optimization_methods = [
+                OptimizationMethod.QUANTIZATION,
+                OptimizationMethod.FILTER_PRUNING,
+            ]
             self.precision = [ModelPrecision.INT8]
             return
         if quantization and not pruning:
@@ -115,33 +112,33 @@ def load_model(self, ote_model: Optional[ModelEntity]) -> AnomalyModule:
         self.optimization_config = compose_nncf_config(common_nncf_config, [self.nncf_preset])
         self.config.merge_with(self.optimization_config)
         model = get_model(config=self.config)
-        if ote_model is None:
+        if ote_model is not None:
             raise ValueError("No trained model in project. NNCF require pretrained weights to compress the model")
+
+        buffer = io.BytesIO(ote_model.get_data("weights.pth"))  # type: ignore
+        model_data = torch.load(buffer, map_location=torch.device("cpu"))
+
+        if is_state_nncf(model_data):
+            logger.info("Loaded model weights from Task Environment and wrapped by NNCF")
+
+            # Workaround to fix incorrect loading state for wrapped pytorch_lighting model
+            new_model = dict()
+            for key in model_data["model"].keys():
+                if key.startswith("model."):
+                    new_model[key.replace("model.", "")] = model_data["model"][key]
+            model_data["model"] = new_model
+
+            self.compression_ctrl, model.model = wrap_nncf_model(
+                model.model,
+                self.optimization_config["nncf_config"],
+                init_state_dict=model_data,
+            )
         else:
-            buffer = io.BytesIO(ote_model.get_data("weights.pth"))
-            model_data = torch.load(buffer, map_location=torch.device("cpu"))
-
-            if is_state_nncf(model_data):
-                logger.info("Loaded model weights from Task Environment and wrapped by NNCF")
-
-                # Workaround to fix incorrect loading state for wrapped pytorch_lighting model
-                new_model = dict()
-                for key in model_data["model"].keys():
-                    if key.startswith("model."):
-                        new_model[key.replace("model.", "")] = model_data["model"][key]
-                model_data["model"] = new_model
-
-                self.compression_ctrl, model.model = wrap_nncf_model(
-                    model.model, self.optimization_config["nncf_config"], init_state_dict=model_data
-                )
-            else:
-                try:
-                    model.load_state_dict(model_data["model"])
-                    logger.info("Loaded model weights from Task Environment")
-                except BaseException as exception:
-                    raise ValueError(
-                        "Could not load the saved model. The model file structure is invalid."
-                    ) from exception
+            try:
+                model.load_state_dict(model_data["model"])
+                logger.info("Loaded model weights from Task Environment")
+            except BaseException as exception:
+                raise ValueError("Could not load the saved model. The model file structure is invalid.") from exception
 
         return model
 
@@ -165,13 +162,7 @@ def optimize(
         if optimization_type is not OptimizationType.NNCF:
             raise RuntimeError("NNCF is the only supported optimization")
 
-        # config = self.get_config()
-        # logger.info("Training Configs '%s'", config)
-
         datamodule = OTEAnomalyDataModule(config=self.config, dataset=dataset, task_type=self.task_type)
-        # Setup dataset to initialization of compressed model
-        # datamodule.setup(stage="fit")
-        # nncf_config = yaml.safe_load(OmegaConf.to_yaml(self.config['nncf_config']))
 
         nncf_callback = NNCFCallback(nncf_config=self.optimization_config["nncf_config"])
         callbacks = [
@@ -195,7 +186,7 @@ def _model_info(self) -> Dict:
         """
 
         return {
-            "compression_state": self.compression_ctrl.get_compression_state(),
+            "compression_state": self.compression_ctrl.get_compression_state(),  # type: ignore
             "meta": {
                 "config": self.config,
                 "nncf_enable_compression": True,
@@ -211,4 +202,4 @@ def _export_to_onnx(self, onnx_path: str):
         Args:
              onnx_path (str): path to save ONNX file
         """
-        self.compression_ctrl.export_model(onnx_path, "onnx_11")
+        self.compression_ctrl.export_model(onnx_path, "onnx_11")  # type: ignore

external/anomaly/ote_anomalib/train_task.py

@@ -14,19 +14,14 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import io
-
-import torch
 from anomalib.utils.callbacks import MinMaxNormalizationCallback
 from ote_anomalib import AnomalyInferenceTask
 from ote_anomalib.callbacks import ProgressCallback
 from ote_anomalib.data import OTEAnomalyDataModule
 from ote_anomalib.logging import get_logger
 from ote_sdk.entities.datasets import DatasetEntity
-from ote_sdk.entities.metrics import Performance, ScoreMetric
-from ote_sdk.entities.model import ModelEntity, ModelPrecision
+from ote_sdk.entities.model import ModelEntity
 from ote_sdk.entities.train_parameters import TrainParameters
-from ote_sdk.serialization.label_mapper import label_schema_to_bytes
 from ote_sdk.usecases.tasks.interfaces.training_interface import ITrainingTask
 from pytorch_lightning import Trainer