Add ruff fix for unused variables

daniil-lyakhov · Jul 8, 2024 · 5c0d504 · 5c0d504
1 parent b56a3a4
commit 5c0d504
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Showing 2 changed files with 15 additions and 16 deletions.
diff --git a/nncf/torch/graph/operator_metatypes.py b/nncf/torch/graph/operator_metatypes.py
@@ -1100,6 +1100,7 @@ class PTScaledDotProductAttentionMetatype(PTOperatorMetatype):
  hw_config_names = [HWConfigOpName.SCALED_DOT_PRODUCT_ATTENTION]
  target_input_ports = [0, 1]
 
+
 @PT_OPERATOR_METATYPES.register()
 class PTEmptyMetatype(PTOperatorMetatype):
  name = "EmptyOP"

diff --git a/tests/torch/fx/test_models.py b/tests/torch/fx/test_models.py
@@ -11,11 +11,7 @@
 
 
 from dataclasses import dataclass
-from typing import Tuple
 
-import os
-
-import numpy as np
 import openvino.torch # noqa
 import pytest
 import torch
@@ -29,12 +25,10 @@
 from torch._export import capture_pre_autograd_graph
 
 import nncf
-from nncf.common.logging.track_progress import track
+from nncf.common.graph.operator_metatypes import UnknownMetatype
 from nncf.torch.dynamic_graph.patch_pytorch import disable_patching
-from tests.torch.fx.helpers import TinyImagenetDatasetManager
-
 from nncf.torch.graph.operator_metatypes import PT_OPERATOR_METATYPES
-from nncf.common.graph.operator_metatypes import UnknownMetatype
+from tests.torch.fx.helpers import TinyImagenetDatasetManager
 
 IMAGE_SIZE = 64
 BATCH_SIZE = 128
@@ -54,12 +48,14 @@ class ModelCase:
 MODELS = (
  ModelCase(
  "resnet18",
- "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/302_resnet18_fp32_v1.pth"
+ "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/302_resnet18_fp32_v1.pth",
  ),
 )
 
 
-def get_model(model_id: str, checkpoint_url: str, device: torch.device, num_classes: int = 200, in_features: int = 512) -> torch.nn.Module:
+def get_model(
+ model_id: str, checkpoint_url: str, device: torch.device, num_classes: int = 200, in_features: int = 512
+) -> torch.nn.Module:
  model = getattr(models, model_id)(weights=None)
  # Update the last FC layer for Tiny ImageNet number of classes.
  model.fc = nn.Linear(in_features=in_features, out_features=num_classes, bias=True)
@@ -73,14 +69,16 @@ def getNodeType(node: torch.fx.node) -> str:
  if node.op == "call_function" and hasattr(node.target, "overloadpacket"):
  node_type = str(node.target).split(".")[1]
  return node_type
- return ''
+ return ""
+
 
 def isNodeMetatype(node_type: str) -> bool:
  op_type = PT_OPERATOR_METATYPES.get_operator_metatype_by_op_name(node_type)
- if(op_type is UnknownMetatype):
+ if op_type is UnknownMetatype:
  return False
  return True
 
+
 def retrieve_nodes(model: torch.fx.GraphModule):
  for node in model.graph.nodes:
  yield node
@@ -92,7 +90,7 @@ def test_sanity(test_case: ModelCase, tiny_imagenet_dataset):
  torch.manual_seed(42)
  device = torch.device("cpu")
  model = get_model(test_case.model_id, test_case.checkpoint_url, device)
- _, val_dataloader, calibration_dataset = tiny_imagenet_dataset
+ _, _, calibration_dataset = tiny_imagenet_dataset
 
  def transform_fn(data_item):
  return data_item[0].to(device)
@@ -105,6 +103,6 @@ def transform_fn(data_item):
  exported_model = capture_pre_autograd_graph(model, args=(ex_input,))
  nodes = retrieve_nodes(exported_model)
  for node in nodes:
- node_type = getNodeType(node)
- if(node_type):
- assert isNodeMetatype(node_type) 
+  node_type = getNodeType(node)
+  if node_type:
+  assert isNodeMetatype(node_type)