[Lint] run pre-commit for PR #125

Author: maltanar

src/qonnx/transformation/change_3d_tensors_to_4d.py

@@ -196,12 +196,10 @@ def apply(self, model):
                 scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
                 model.set_initializer(n.input[1], scales)
             elif node_op_type == "Resize":
-                assert ("axes" not in [x.name for x in n.attribute]), (
-                        "%s: Axes attribute is not supported." % n.name
-                        )  
-                assert (not (len(n.input) in (3, 4) and model.get_initializer(n.input[1]) is not None)), (
-                        "%s: ROI input is not supported." % n.name
-                        )    
+                assert "axes" not in [x.name for x in n.attribute], "%s: Axes attribute is not supported." % n.name
+                assert not (len(n.input) in (3, 4) and model.get_initializer(n.input[1]) is not None), (
+                    "%s: ROI input is not supported." % n.name
+                )
                 if len(n.input) == 2:
                     # Resize version 10
                     scales = model.get_initializer(n.input[1])
@@ -213,13 +211,17 @@ def apply(self, model):
                     scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
                     model.set_initializer(n.input[2], scales)
                 elif len(n.input) == 4:
-                    scales_exists = (model.get_initializer(n.input[2]) is not None) and (len(model.get_initializer(n.input[2])) != 0)
-                    sizes_exists = (model.get_initializer(n.input[3]) is not None) and (len(model.get_initializer(n.input[3])) != 0)
-                    assert (scales_exists ^ sizes_exists), (
-                        "%s: Either scales or the target output size must " 
+                    scales_exists = (model.get_initializer(n.input[2]) is not None) and (
+                        len(model.get_initializer(n.input[2])) != 0
+                    )
+                    sizes_exists = (model.get_initializer(n.input[3]) is not None) and (
+                        len(model.get_initializer(n.input[3])) != 0
+                    )
+                    assert scales_exists ^ sizes_exists, (
+                        "%s: Either scales or the target output size must "
                         "be specified. Specifying both is prohibited." % n.name
-                        )  
-                    if (scales_exists): 
+                    )
+                    if scales_exists:
                         # Scales parameter is a 1d list of upsampling factors along each axis
                         scales = model.get_initializer(n.input[2])
                         scales = np.append(scales, np.asarray(1.0, dtype=np.float32))

tests/transformation/test_4d_conversion.py

@@ -327,6 +327,7 @@ def create_conv_upsample():
             model.set_initializer(tensor_name, gen_finn_dt_tensor(DataType["FLOAT32"], init_shape))
     return model
 
+
 def create_resize():
     """
     Creates an model for testing the 3D to 4D transform of the resize node.
@@ -346,18 +347,18 @@ def create_resize():
         name="Resize2",
         mode="nearest",
     )
-    
+
     in_resize1 = onnx.helper.make_tensor_value_info("in_resize1", onnx.TensorProto.FLOAT, [1, 32, 4])
-    out_resize1 = onnx.helper.make_tensor_value_info("out_resize1", onnx.TensorProto.FLOAT, [1, 32, 8]) 
-    out_resize2 = onnx.helper.make_tensor_value_info("out_resize2", onnx.TensorProto.FLOAT, [1, 32, 16]) 
-    
+    out_resize1 = onnx.helper.make_tensor_value_info("out_resize1", onnx.TensorProto.FLOAT, [1, 32, 8])
+    out_resize2 = onnx.helper.make_tensor_value_info("out_resize2", onnx.TensorProto.FLOAT, [1, 32, 16])
+
     roi_resize1 = onnx.helper.make_tensor_value_info("roi_resize1", onnx.TensorProto.FLOAT, [4])
     scales_resize1 = onnx.helper.make_tensor_value_info("scales_resize1", onnx.TensorProto.FLOAT, [])
     sizes_resize1 = onnx.helper.make_tensor_value_info("sizes_resize1", onnx.TensorProto.INT64, [3])
 
     roi_resize2 = onnx.helper.make_tensor_value_info("roi_resize2", onnx.TensorProto.FLOAT, [4])
     scales_resize2 = onnx.helper.make_tensor_value_info("scales_resize2", onnx.TensorProto.FLOAT, [3])
-    
+
     list_of_nodes = [
         resize_node1,
         resize_node2,
@@ -384,9 +385,10 @@ def create_resize():
     model = model.transform(InferShapes())
     model.set_initializer("sizes_resize1", np.array([1, 32, 8], dtype=np.int64))
     model.set_initializer("scales_resize1", np.array([], dtype=np.float32))
-    model.set_initializer("scales_resize2", np.array([1., 1., 2.], dtype=np.float32))
+    model.set_initializer("scales_resize2", np.array([1.0, 1.0, 2.0], dtype=np.float32))
     return model
 
+
 @pytest.mark.parametrize("test_model", ["Quartz", "VGG", "ConvUpsample", "Resize"])
 def test_4d_conversion(test_model):
     """