refactor: static analysis

ENOT-AutoDL · Mar 29, 2024 · 310490a · 310490a
1 parent a8b0603
commit 310490a
Show file tree

Hide file tree

Showing 11 changed files with 105 additions and 114 deletions.
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -22,7 +22,7 @@ jobs:
         python -m pip install -e .
     - name: Analysing the code with pylint
       run: |
-        pylint --rcfile .pylintrc --output-format=colorized $(git ls-files '*.py')
+        pylint --output-format=colorized $(git ls-files '*.py')
 
   lint-python-format:
     name: Python format

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,21 +1,14 @@
 repos:
 - repo: https://github.com/psf/black
-  rev: 23.1.0
+  rev: 24.3.0
   hooks:
     - id: black
 - repo: https://github.com/PyCQA/isort
-  rev: 5.12.0
+  rev: 5.13.2
   hooks:
     - id: isort
-      args:
-        [
-          "--force-single-line-imports",
-          "--ensure-newline-before-comments",
-          "--line-length=120",
-          "--profile=black",
-        ]
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v4.5.0
   hooks:
     - id: check-yaml
     - id: check-toml
@@ -29,18 +22,18 @@ repos:
     - id: end-of-file-fixer
     - id: requirements-txt-fixer
     - id: no-commit-to-branch
-      args:
-        [
-          "-b=main",
-        ]
+      args: ["-b=main"]
 - repo: https://github.com/PyCQA/pylint
-  rev: v2.16.0
+  rev: v3.1.0
   hooks:
     - id: pylint
+      name: pylint
+      entry: pylint
+      language: system
+      types: [ python ]
       args:
         [
           "-rn",
           "-sn",
-          "--rcfile=.pylintrc",
           "--output-format=colorized",
         ]
diff --git a/.pylintrc b/.pylintrc
diff --git a/onnx2torch/node_converters/base_element_wise.py b/onnx2torch/node_converters/base_element_wise.py
@@ -1,11 +1,12 @@
+# pylint: disable=missing-docstring
 import torch
 from torch import nn
 
 from onnx2torch.utils.custom_export_to_onnx import DefaultExportToOnnx
 from onnx2torch.utils.custom_export_to_onnx import OnnxToTorchModuleWithCustomExport
 
 
-class OnnxBaseElementWise(nn.Module, OnnxToTorchModuleWithCustomExport):  # pylint: disable=missing-docstring
+class OnnxBaseElementWise(nn.Module, OnnxToTorchModuleWithCustomExport):
     def __init__(self, op_type: str):
         super().__init__()
         self._op_type = op_type
@@ -16,17 +17,20 @@ def _broadcast_shape(*tensors: torch.Tensor):
         broadcast_shape = torch.broadcast_shapes(*shapes)
         return broadcast_shape
 
-    def apply_reduction(self, *tensors: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
+    def apply_reduction(self, *tensors: torch.Tensor) -> torch.Tensor:
+        del tensors
         raise NotImplementedError
 
-    def forward(self, *input_tensors: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
+    def forward(self, *input_tensors: torch.Tensor) -> torch.Tensor:
         if len(input_tensors) == 1:
             # If there is a single element, return it (no op).
             # Also, no need for manually building the ONNX node.
             return input_tensors[0]
 
-        forward_lambda = lambda: self.apply_reduction(*input_tensors)
+        def _forward() -> torch.Tensor:
+            return self.apply_reduction(*input_tensors)
+
         if torch.onnx.is_in_onnx_export():
-            return DefaultExportToOnnx.export(forward_lambda, self._op_type, *input_tensors, {})
+            return DefaultExportToOnnx.export(_forward, self._op_type, *input_tensors, {})
 
-        return forward_lambda()
+        return _forward()
diff --git a/onnx2torch/node_converters/global_average_pool.py b/onnx2torch/node_converters/global_average_pool.py
@@ -1,3 +1,4 @@
+# pylint: disable=missing-docstring
 __all__ = [
     'OnnxGlobalAveragePool',
     'OnnxGlobalAveragePoolWithKnownInputShape',
@@ -18,8 +19,8 @@
 from onnx2torch.utils.custom_export_to_onnx import OnnxToTorchModuleWithCustomExport
 
 
-class OnnxGlobalAveragePool(nn.Module, OnnxToTorchModuleWithCustomExport):  # pylint: disable=missing-docstring
-    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
+class OnnxGlobalAveragePool(nn.Module, OnnxToTorchModuleWithCustomExport):
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
         def _forward():
             x_dims = list(range(2, len(input_tensor.shape)))
             return torch.mean(input_tensor, dim=x_dims, keepdim=True)
@@ -30,24 +31,23 @@ def _forward():
         return _forward()
 
 
-class OnnxGlobalAveragePoolWithKnownInputShape(
-    nn.Module, OnnxToTorchModuleWithCustomExport
-):  # pylint: disable=missing-docstring
+class OnnxGlobalAveragePoolWithKnownInputShape(nn.Module, OnnxToTorchModuleWithCustomExport):
     def __init__(self, input_shape: List[int]):
         super().__init__()
         self._x_dims = list(range(2, len(input_shape)))
 
-    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
-        forward_lambda = lambda: torch.mean(input_tensor, dim=self._x_dims, keepdim=True)
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        def _forward() -> torch.Tensor:
+            return torch.mean(input_tensor, dim=self._x_dims, keepdim=True)
 
         if torch.onnx.is_in_onnx_export():
-            return DefaultExportToOnnx.export(forward_lambda, 'GlobalAveragePool', input_tensor, {})
+            return DefaultExportToOnnx.export(_forward, 'GlobalAveragePool', input_tensor, {})
 
-        return forward_lambda()
+        return _forward()
 
 
 @add_converter(operation_type='GlobalAveragePool', version=1)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
     input_value_info = graph.value_info[node.input_values[0]]
     input_shape = get_shape_from_value_info(input_value_info)
 

diff --git a/onnx2torch/node_converters/logical.py b/onnx2torch/node_converters/logical.py
@@ -1,3 +1,4 @@
+# pylint: disable=missing-docstring
 __all__ = [
     'OnnxNot',
     'OnnxLogical',
@@ -25,26 +26,26 @@
 }
 
 
-class OnnxNot(nn.Module, OnnxToTorchModuleWithCustomExport):  # pylint: disable=missing-class-docstring
-    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:  # pylint: disable=missing-function-docstring
-        forward_lambda = lambda: torch.logical_not(input_tensor)
+class OnnxNot(nn.Module, OnnxToTorchModuleWithCustomExport):
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        def _forward() -> torch.Tensor:
+            return torch.logical_not(input_tensor)
+
         if torch.onnx.is_in_onnx_export():
-            return DefaultExportToOnnx.export(forward_lambda, 'Not', input_tensor, {})
+            return DefaultExportToOnnx.export(_forward, 'Not', input_tensor, {})
 
-        return forward_lambda()
+        return _forward()
 
 
-class OnnxLogical(nn.Module, OnnxToTorchModule):  # pylint: disable=missing-class-docstring
+class OnnxLogical(nn.Module, OnnxToTorchModule):
     def __init__(self, operation_type: str, broadcast: Optional[int] = None, axis: Optional[int] = None):
         super().__init__()
         self.broadcast = broadcast
         self.axis = axis
 
         self.logic_op_function = _TORCH_FUNCTION_FROM_ONNX_TYPE[operation_type]
 
-    def forward(  # pylint: disable=missing-function-docstring
-        self, first_tensor: torch.Tensor, second_tensor: torch.Tensor
-    ):
+    def forward(self, first_tensor: torch.Tensor, second_tensor: torch.Tensor):
         if self.broadcast == 1 and self.axis is not None:
             second_tensor = old_style_broadcast(first_tensor, second_tensor, self.axis)
 
@@ -57,7 +58,8 @@ def forward(  # pylint: disable=missing-function-docstring
 @add_converter(operation_type='And', version=7)
 @add_converter(operation_type='Or', version=1)
 @add_converter(operation_type='Or', version=7)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     return OperationConverterResult(
         torch_module=OnnxLogical(
             operation_type=node.operation_type,
@@ -69,7 +71,8 @@ def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint:
 
 
 @add_converter(operation_type='Not', version=1)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     return OperationConverterResult(
         torch_module=OnnxNot(),
         onnx_mapping=onnx_mapping_from_node(node=node),

diff --git a/onnx2torch/node_converters/nms.py b/onnx2torch/node_converters/nms.py
@@ -1,3 +1,4 @@
+# pylint: disable=missing-docstring
 __all__ = [
     'OnnxNonMaxSuppression',
 ]
@@ -20,23 +21,25 @@
 from onnx2torch.utils.custom_export_to_onnx import OnnxToTorchModuleWithCustomExport
 
 
-class OnnxNonMaxSuppression(nn.Module, OnnxToTorchModuleWithCustomExport):  # pylint: disable=missing-class-docstring
+class OnnxNonMaxSuppression(nn.Module, OnnxToTorchModuleWithCustomExport):
     def __init__(self, center_point_box: int = 0):
         super().__init__()
         self._center_point_box = center_point_box
 
     def _onnx_attrs(self, opset_version: int) -> Dict[str, Any]:
+        del opset_version
         return {'center_point_box_i': self._center_point_box}
 
-    def forward(  # pylint: disable=missing-function-docstring
+    def forward(
         self,
         boxes: torch.Tensor,
         scores: torch.Tensor,
         max_output_boxes_per_class: Optional[torch.Tensor] = None,
         iou_threshold: Optional[torch.Tensor] = None,
         score_threshold: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        forward_lambda = lambda: self._nms(boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
+        def _forward() -> torch.Tensor:
+            return self._nms(boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
 
         if torch.onnx.is_in_onnx_export():
             if max_output_boxes_per_class is None:
@@ -48,7 +51,7 @@ def forward(  # pylint: disable=missing-function-docstring
 
             onnx_attrs = self._onnx_attrs(opset_version=get_onnx_version())
             return DefaultExportToOnnx.export(
-                forward_lambda,
+                _forward,
                 'NonMaxSuppression',
                 boxes,
                 scores,
@@ -58,7 +61,7 @@ def forward(  # pylint: disable=missing-function-docstring
                 onnx_attrs,
             )
 
-        return forward_lambda()
+        return _forward()
 
     def _nms(
         self,
@@ -109,7 +112,8 @@ def _nms(
 
 @add_converter(operation_type='NonMaxSuppression', version=10)
 @add_converter(operation_type='NonMaxSuppression', version=11)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     center_point_box = node.attributes.get('center_point_box', 0)
     return OperationConverterResult(
         torch_module=OnnxNonMaxSuppression(center_point_box=center_point_box),

diff --git a/onnx2torch/node_converters/range.py b/onnx2torch/node_converters/range.py
@@ -1,3 +1,4 @@
+# pylint: disable=missing-docstring
 __all__ = [
     'OnnxRange',
 ]
@@ -16,7 +17,7 @@
 from onnx2torch.utils.custom_export_to_onnx import OnnxToTorchModuleWithCustomExport
 
 
-class OnnxRange(nn.Module, OnnxToTorchModuleWithCustomExport):  # pylint: disable=missing-class-docstring
+class OnnxRange(nn.Module, OnnxToTorchModuleWithCustomExport):
     def __init__(self):
         super().__init__()
         self.register_buffer('dummy_buffer', torch.Tensor(), persistent=False)
@@ -41,22 +42,24 @@ def _arange(
             device=self.dummy_buffer.device,
         )
 
-    def forward(  # pylint: disable=missing-function-docstring
+    def forward(
         self,
         start: Union[torch.Tensor, float, int],
         limit: Union[torch.Tensor, float, int],
         delta: Union[torch.Tensor, float, int],
     ) -> torch.Tensor:
-        forward_lambda = lambda: self._arange(start, limit, delta)
+        def _forward() -> torch.Tensor:
+            return self._arange(start, limit, delta)
 
         if torch.onnx.is_in_onnx_export():
-            return DefaultExportToOnnx.export(forward_lambda, 'Range', start, limit, delta, {})
+            return DefaultExportToOnnx.export(_forward, 'Range', start, limit, delta, {})
 
-        return forward_lambda()
+        return _forward()
 
 
 @add_converter(operation_type='Range', version=11)
-def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
+def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
+    del graph
     return OperationConverterResult(
         torch_module=OnnxRange(),
         onnx_mapping=onnx_mapping_from_node(node),

diff --git a/onnx2torch/node_converters/reshape.py b/onnx2torch/node_converters/reshape.py
@@ -27,12 +27,13 @@ def forward(  # pylint: disable=missing-function-docstring
         input_tensor: torch.Tensor,
         shape: torch.Tensor,
     ) -> torch.Tensor:
-        forward_lambda = lambda: self._do_reshape(input_tensor, shape)
+        def _forward() -> torch.Tensor:
+            return self._do_reshape(input_tensor, shape)
 
         if torch.onnx.is_in_onnx_export():
-            return DefaultExportToOnnx.export(forward_lambda, 'Reshape', input_tensor, shape, {})
+            return DefaultExportToOnnx.export(_forward, 'Reshape', input_tensor, shape, {})
 
-        return forward_lambda()
+        return _forward()
 
 
 @add_converter(operation_type='Reshape', version=5)