postprocess_model() shifted above to fix the issue 23508 in opencv/op…

…encv repository

testdata/dnn/onnx/generate_onnx_models.py

@@ -524,6 +524,37 @@ def generate_slice_neg_starts():
 
 generate_slice_neg_starts()
 
+def postprocess_model(model_path, inputs_shapes):
+    onnx_model = onnx.load(model_path)
+
+    def update_inputs_dims(model, input_dims):
+        """
+            This function updates the sizes of dimensions of the model's inputs to the values
+            provided in input_dims. if the dim value provided is negative, a unique dim_param
+            will be set for that dimension.
+        """
+        def update_dim(tensor, dim, i, j, dim_param_prefix):
+            dim_proto = tensor.type.tensor_type.shape.dim[j]
+            if isinstance(dim, int):
+                if dim >= 0:
+                    dim_proto.dim_value = dim
+                else:
+                    dim_proto.dim_param = dim_param_prefix + str(i) + '_' + str(j)
+            elif isinstance(dim, str):
+                dim_proto.dim_param = dim
+            else:
+                raise ValueError('Only int or str is accepted as dimension value, incorrect type: {}'.format(type(dim)))
+
+        for i, input_dim_arr in enumerate(input_dims):
+            for j, dim in enumerate(input_dim_arr):
+                update_dim(model.graph.input[i], dim, i, j, 'in_')
+
+        onnx.checker.check_model(model)
+        return model
+
+    onnx_model = update_inputs_dims(onnx_model, inputs_shapes)
+    onnx.save(onnx_model, model_path)
+
 input_2 = Variable(torch.randn(6, 6))
 custom_slice_list = [
     slice(1, 3, 1),
@@ -1916,36 +1947,6 @@ def forward(self, x):
 model = GatherMultiOutput()
 save_data_and_model("gather_multi_output", x, model)
 
-def postprocess_model(model_path, inputs_shapes):
-    onnx_model = onnx.load(model_path)
-
-    def update_inputs_dims(model, input_dims):
-        """
-            This function updates the sizes of dimensions of the model's inputs to the values
-            provided in input_dims. if the dim value provided is negative, a unique dim_param
-            will be set for that dimension.
-        """
-        def update_dim(tensor, dim, i, j, dim_param_prefix):
-            dim_proto = tensor.type.tensor_type.shape.dim[j]
-            if isinstance(dim, int):
-                if dim >= 0:
-                    dim_proto.dim_value = dim
-                else:
-                    dim_proto.dim_param = dim_param_prefix + str(i) + '_' + str(j)
-            elif isinstance(dim, str):
-                dim_proto.dim_param = dim
-            else:
-                raise ValueError('Only int or str is accepted as dimension value, incorrect type: {}'.format(type(dim)))
-
-        for i, input_dim_arr in enumerate(input_dims):
-            for j, dim in enumerate(input_dim_arr):
-                update_dim(model.graph.input[i], dim, i, j, 'in_')
-
-        onnx.checker.check_model(model)
-        return model
-
-    onnx_model = update_inputs_dims(onnx_model, inputs_shapes)
-    onnx.save(onnx_model, model_path)
 
 class UnsqueezeAndConv(nn.Module):
     def __init__(self):