chore: linters

Author: senysenyseny16

onnx2torch/node_converters/arg_extrema.py

@@ -1,12 +1,10 @@
+# pylint: disable=missing-docstring
 __all__ = [
-    "OnnxArgExtremumOld",
-    "OnnxArgExtremum",
+    'OnnxArgExtremumOld',
+    'OnnxArgExtremum',
 ]
 
-from typing import Optional
-
 import torch
-import torch.nn.functional as F
 from torch import nn
 
 from onnx2torch.node_converters.registry import add_converter
@@ -21,31 +19,31 @@
 DEFAULT_SELECT_LAST_INDEX = 0
 
 _TORCH_FUNCTION_FROM_ONNX_TYPE = {
-    "ArgMax": torch.argmax,
-    "ArgMin": torch.argmin,
+    'ArgMax': torch.argmax,
+    'ArgMin': torch.argmin,
 }
 
 
-class OnnxArgExtremumOld(nn.Module, OnnxToTorchModule):  # pylint: disable=missing-docstring
+class OnnxArgExtremumOld(nn.Module, OnnxToTorchModule):
     def __init__(self, operation_type: str, axis: int, keepdims: int):
         super().__init__()
         self.axis = axis
         self.keepdims = bool(keepdims)
         self.extremum_function = _TORCH_FUNCTION_FROM_ONNX_TYPE[operation_type]
 
-    def forward(self, data: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
+    def forward(self, data: torch.Tensor) -> torch.Tensor:
         return self.extremum_function(data, dim=self.axis, keepdim=self.keepdims)
 
 
-class OnnxArgExtremum(nn.Module, OnnxToTorchModule):  # pylint: disable=missing-class-docstring
+class OnnxArgExtremum(nn.Module, OnnxToTorchModule):
     def __init__(self, operation_type: str, axis: int, keepdims: int, select_last_index: int):
         super().__init__()
         self.axis = axis
         self.keepdims = bool(keepdims)
         self.select_last_index = bool(select_last_index)
         self.extremum_function = _TORCH_FUNCTION_FROM_ONNX_TYPE[operation_type]
 
-    def forward(self, data: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
+    def forward(self, data: torch.Tensor) -> torch.Tensor:
         if self.select_last_index:
             # torch's argmax does not handle the select_last_index attribute from Onnx.
             # We flip the data, call the normal argmax, then map it back to the original
@@ -54,34 +52,36 @@ def forward(self, data: torch.Tensor) -> torch.Tensor:  # pylint: disable=missin
             extremum_index_flipped = self.extremum_function(flipped, dim=self.axis, keepdim=self.keepdims)
             extremum_index_original = data.size(dim=self.axis) - 1 - extremum_index_flipped
             return extremum_index_original
-        else:
-            return self.extremum_function(data, dim=self.axis, keepdim=self.keepdims)
+
+        return self.extremum_function(data, dim=self.axis, keepdim=self.keepdims)
 
 
-@add_converter(operation_type="ArgMax", version=12)
-@add_converter(operation_type="ArgMax", version=13)
-@add_converter(operation_type="ArgMin", version=12)
-@add_converter(operation_type="ArgMin", version=13)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+@add_converter(operation_type='ArgMax', version=12)
+@add_converter(operation_type='ArgMax', version=13)
+@add_converter(operation_type='ArgMin', version=12)
+@add_converter(operation_type='ArgMin', version=13)
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     return OperationConverterResult(
         torch_module=OnnxArgExtremum(
             operation_type=node.operation_type,
-            axis=node.attributes.get("axis", DEFAULT_AXIS),
-            keepdims=node.attributes.get("keepdims", DEFAULT_KEEPDIMS),
-            select_last_index=node.attributes.get("select_last_index", DEFAULT_SELECT_LAST_INDEX),
+            axis=node.attributes.get('axis', DEFAULT_AXIS),
+            keepdims=node.attributes.get('keepdims', DEFAULT_KEEPDIMS),
+            select_last_index=node.attributes.get('select_last_index', DEFAULT_SELECT_LAST_INDEX),
         ),
         onnx_mapping=onnx_mapping_from_node(node=node),
     )
 
 
-@add_converter(operation_type="ArgMax", version=11)
-@add_converter(operation_type="ArgMin", version=11)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+@add_converter(operation_type='ArgMax', version=11)
+@add_converter(operation_type='ArgMin', version=11)
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     return OperationConverterResult(
         torch_module=OnnxArgExtremumOld(
             operation_type=node.operation_type,
-            axis=node.attributes.get("axis", DEFAULT_AXIS),
-            keepdims=node.attributes.get("keepdims", DEFAULT_KEEPDIMS),
+            axis=node.attributes.get('axis', DEFAULT_AXIS),
+            keepdims=node.attributes.get('keepdims', DEFAULT_KEEPDIMS),
         ),
         onnx_mapping=onnx_mapping_from_node(node=node),
     )

onnx2torch/utils/custom_export_to_onnx.py

@@ -57,7 +57,7 @@ def export(cls, forward_function: Callable, *args) -> Any:
         return cls.apply(*args)
 
     @staticmethod
-    def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:  # pylint: disable=unused-argument
+    def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:  # pylint: disable=unused-argument, arguments-differ
         """Applies custom forward function."""
         if CustomExportToOnnx._NEXT_FORWARD_FUNCTION is None:
             raise RuntimeError('Forward function is not set')

tests/node_converters/arg_extrema_test.py

@@ -1,10 +1,11 @@
+# pylint: disable=missing-docstring
 from pathlib import Path
 
 import numpy as np
 import onnx
-from onnx.helper import make_tensor_value_info
 import pytest
 import torch
+from onnx.helper import make_tensor_value_info
 
 from tests.utils.common import check_onnx_model
 from tests.utils.common import make_model_from_nodes
@@ -51,7 +52,7 @@
     "select_last_index",
     (0, 1),
 )
-def test_arg_max_arg_min(  # pylint: disable=missing-function-docstring
+def test_arg_max_arg_min(
     op_type: str,
     opset_version: int,
     dims: int,
@@ -95,7 +96,7 @@ class ArgMaxModel(torch.nn.Module):
     def __init__(self, axis: int, keepdims: bool):
         super().__init__()
         self.axis = axis
-        self.keepdims = bool(keepdims)
+        self.keepdims = keepdims
 
     def forward(self, data: torch.Tensor) -> torch.Tensor:
         return torch.argmax(data, dim=self.axis, keepdim=self.keepdims)
@@ -105,29 +106,16 @@ class ArgMinModel(torch.nn.Module):
     def __init__(self, axis: int, keepdims: bool):
         super().__init__()
         self.axis = axis
-        self.keepdims = bool(keepdims)
+        self.keepdims = keepdims
 
     def forward(self, data: torch.Tensor) -> torch.Tensor:
         return torch.argmin(data, dim=self.axis, keepdim=self.keepdims)
 
 
+@pytest.mark.parametrize("op_type", ["ArgMax", "ArgMin"])
+@pytest.mark.parametrize("opset_version", [11, 12, 13])
 @pytest.mark.parametrize(
-    "op_type",
-    (
-        "ArgMax",
-        "ArgMin",
-    ),
-)
-@pytest.mark.parametrize(
-    "opset_version",
-    (
-        11,
-        12,
-        13,
-    ),
-)
-@pytest.mark.parametrize(
-    "dims,axis",
+    "dims, axis",
     (
         (1, 0),
         (2, 0),
@@ -141,19 +129,13 @@ def forward(self, data: torch.Tensor) -> torch.Tensor:
         (4, 3),
     ),
 )
-@pytest.mark.parametrize(
-    "keepdims",
-    (
-        0,
-        1,
-    ),
-)
+@pytest.mark.parametrize("keepdims", [True, False])
 def test_start_from_torch_module(
     op_type: str,
     opset_version: int,
     dims: int,
     axis: int,
-    keepdims: int,
+    keepdims: bool,
     tmp_path: Path,
 ) -> None:
     """
@@ -179,7 +161,7 @@ def test_start_from_torch_module(
         input_names=input_names,
         output_names=output_names,
         do_constant_folding=False,
-        training=torch._C._onnx.TrainingMode.TRAINING,
+        opset_version=opset_version,
     )
 
     # load the exported onnx file

tests/node_converters/conv_test.py

@@ -1,5 +1,6 @@
 from itertools import chain
 from itertools import product
+from typing import Literal
 from typing import Tuple
 
 import numpy as np
@@ -10,7 +11,7 @@
 
 
 def _test_conv(
-    op_type: str,
+    op_type: Literal['Conv', 'ConvTranspose'],
     in_channels: int,
     out_channels: int,
     kernel_shape: Tuple[int, int],
@@ -23,7 +24,7 @@ def _test_conv(
     x = np.random.uniform(low=-1.0, high=1.0, size=x_shape).astype(np.float32)
     if op_type == 'Conv':
         weights_shape = (out_channels, in_channels // group) + kernel_shape
-    elif op_type == 'ConvTranspose':
+    else:  # ConvTranspose
         weights_shape = (in_channels, out_channels // group) + kernel_shape
     weights = np.random.uniform(low=-1.0, high=1.0, size=weights_shape).astype(np.float32)