Resolve some flake8 issues

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
         name: "flake8"
         files: '^(otx|tests)/.*\.py'
         args: ["--config", ".flake8", "--max-complexity", "20"]
-        exclude: "^(.*/protobuf|tests/ote_cli)"
+        exclude: "^(.*/protobuf|tests/ote_cli)|otx/mpa|otx/recipes"
 
   # yaml formatting
   - repo: https://github.com/pre-commit/mirrors-prettier

otx/mpa/cls/explainer.py

@@ -8,7 +8,6 @@
 import torch
 from mmcls.datasets import build_dataloader, build_dataset
 from mmcls.models import build_classifier
-from mmcv.parallel import MMDataParallel
 from mmcv.runner import load_checkpoint, wrap_fp16_model
 
 from otx.mpa.cls.stage import ClsStage

otx/mpa/cls/exporter.py

@@ -10,7 +10,7 @@
 import torch.onnx
 from mmcls.datasets.pipelines import Compose
 from mmcls.models import build_classifier
-from mmcv.runner import load_checkpoint, wrap_fp16_model
+from mmcv.runner import load_checkpoint
 
 from otx.mpa.registry import STAGES
 from otx.mpa.utils import mo_wrapper

otx/mpa/cls/stage.py

@@ -161,6 +161,7 @@ def configure_model(cfg, training, **kwargs):
 
             # checking task incremental model configurations
 
+    # noqa: C901
     @staticmethod
     def configure_task(cfg, training, model_meta=None, **kwargs):
         """Configure for Task Adaptation Task"""

otx/mpa/cls/trainer.py

@@ -35,6 +35,7 @@
 
 @STAGES.register_module()
 class ClsTrainer(ClsStage):
+    # noqa: C901
     def run(self, model_cfg, model_ckpt, data_cfg, **kwargs):
         """Run training stage"""
         self._init_logger()

otx/mpa/det/inferrer.py

@@ -78,6 +78,7 @@ def default(self, obj):
         print(json_dump)
         """
 
+    # noqa: C901
     def infer(self, cfg, eval=False, dump_features=False, dump_saliency_map=False):
         samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1)
         if samples_per_gpu > 1:

otx/mpa/det/semisl/__init__.py

@@ -1,5 +1,3 @@
 # Copyright (C) 2022 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 #
-
-from . import inferrer, trainer

otx/mpa/det/trainer.py

@@ -19,8 +19,6 @@
 from otx.mpa.registry import STAGES
 from otx.mpa.utils.logger import get_logger
 
-from .stage import DetectionStage
-
 # TODO[JAEGUK]: Remove import detection_tasks
 # from detection_tasks.apis.detection.config_utils import cluster_anchors
 

otx/mpa/modules/datasets/seg_incr_voc_dataset.py

@@ -8,7 +8,6 @@
 from mmseg.datasets import DATASETS, CustomDataset
 
 from otx.mpa.modules.utils.task_adapt import map_class_names
-from otx.mpa.utils import logger
 
 
 @DATASETS.register_module()

otx/mpa/modules/hooks/model_ema_v2_hook.py

@@ -6,7 +6,6 @@
 
 import torch
 import torch.nn as nn
-from mmcv.parallel import is_module_wrapper
 from mmcv.runner import HOOKS, Hook
 
 from otx.mpa.utils.logger import get_logger

otx/mpa/modules/ov/graph/graph.py

@@ -343,7 +343,7 @@ def get_nodes_by_types(self, types: List[str]) -> List[Operation]:
 
     def bfs(
         self, node: Operation, reverse: bool = False, depth_limit: Optional[int] = None
-    ) -> Generator[Union[Tuple[Operation, Operation], Tuple[Operation, Tuple[Operation]]], None, None,]:
+    ) -> Generator[Union[Tuple[Operation, Operation], Tuple[Operation, Tuple[Operation]]], None, None]:
         if reverse:
             for s, t in nx.bfs_edges(self, node, reverse=True, depth_limit=depth_limit):
                 yield (t, s)
@@ -406,6 +406,7 @@ def get_nodes_by_type_pattern(self, pattern: List[str], start_node: Optional[Ope
             start_nodes = [found[-1] for found in founds]
         return founds
 
+    # noqa: C901
     def _freeze_normalize_nodes(self):
         invariant_types = ["Transpose", "Convert"]
 

otx/mpa/modules/ov/models/__init__.py

@@ -26,5 +26,3 @@
 #      pass
 #  else:
 #      from . import mmseg
-
-from .ov_model import OVModel

otx/mpa/seg/inferrer.py

@@ -2,7 +2,6 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-import os
 import os.path as osp
 from contextlib import nullcontext