linter

anomalib/integration/nncf/callbacks.py

@@ -48,12 +48,12 @@ def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: Optio
         """
         if self.nncf_ctrl:
             return
-        # pylint: disable=attr-defined
+
         init_loader = InitLoader(trainer.datamodule.train_dataloader())  # type: ignore
         nncf_config = register_default_init_args(self.nncf_config, init_loader)
 
         self.nncf_ctrl, pl_module.model = wrap_nncf_model(
-            model=pl_module.model, config=nncf_config, dataloader=trainer.datamodule.train_dataloader()
+            model=pl_module.model, config=nncf_config, dataloader=trainer.datamodule.train_dataloader()  # type: ignore
         )
 
     def on_train_batch_start(

anomalib/integration/nncf/compression.py

@@ -14,18 +14,19 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from typing import Any, Dict, Iterator, Tuple
 
-import torch.nn as nn
 from nncf import NNCFConfig
 from nncf.torch import create_compressed_model, load_state, register_default_init_args
 from nncf.torch.compression_method_api import PTCompressionAlgorithmController
 from nncf.torch.initialization import PTInitializingDataLoader
 from nncf.torch.nncf_network import NNCFNetwork
-from ote_anomalib.logging import get_logger
+from torch import nn
 from torch.utils.data.dataloader import DataLoader
 
-logger = get_logger(__name__)
+logger = logging.getLogger(name="NNCF compression")
+logger.setLevel(logging.DEBUG)
 
 
 class InitLoader(PTInitializingDataLoader):
@@ -68,8 +69,7 @@ def get_target(self, _):
 def wrap_nncf_model(
     model: nn.Module, config: Dict, dataloader: DataLoader = None, init_state_dict: Dict = None
 ) -> Tuple[NNCFNetwork, PTCompressionAlgorithmController]:
-    """
-    Wrap model by NNCF.
+    """Wrap model by NNCF.
 
     :param model: Anomalib model.
     :param config: NNCF config.
@@ -107,7 +107,5 @@ def wrap_nncf_model(
 
 
 def is_state_nncf(state: Dict) -> bool:
-    """
-    The function to check if sate was the result of training of NNCF-compressed model.
-    """
+    """The function to check if sate is the result of NNCF-compressed model."""
     return bool(state.get("meta", {}).get("nncf_enable_compression", False))

anomalib/integration/nncf/utils.py

@@ -112,6 +112,7 @@ def merge_dicts_and_lists_b_into_a(a, b):
 
 def _merge_dicts_and_lists_b_into_a(a, b, cur_key=None):
     """The function is inspired by mmcf.Config._merge_a_into_b.
+
     * works with usual dicts and lists and derived types
     * supports merging of lists (by concatenating the lists)
     * makes recursive merging for dict + dict case

anomalib/models/__init__.py

@@ -25,7 +25,7 @@
 
 # TODO(AlexanderDokuchaev): Workaround of wrapping by NNCF.
 #                           Can't not wrap `spatial_softmax2d` if use import_module.
-from anomalib.models.padim.model import PadimLightning
+from anomalib.models.padim.model import PadimLightning  # noqa: F401
 
 
 def get_model(config: Union[DictConfig, ListConfig]) -> AnomalyModule: