diff --git a/testdata/dnn/onnx/data/input_reshape_and_conv_parameter_dims.npy b/testdata/dnn/onnx/data/input_reshape_and_conv_parameter_dims.npy
new file mode 100644
index 000000000..1b7ca316f
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diff --git a/testdata/dnn/onnx/data/output_reshape_and_conv_parameter_dims.npy b/testdata/dnn/onnx/data/output_reshape_and_conv_parameter_dims.npy
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index 000000000..55b777010
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diff --git a/testdata/dnn/onnx/generate_onnx_models.py b/testdata/dnn/onnx/generate_onnx_models.py
index 783718008..87a47ef99 100644
--- a/testdata/dnn/onnx/generate_onnx_models.py
+++ b/testdata/dnn/onnx/generate_onnx_models.py
@@ -887,3 +887,48 @@ def forward(self, x, kernel, bias):
 x = Variable(torch.randn(1, 2, 2))
 model = Expand(shape=[2, -1, -1, -1])
 save_data_and_model("expand_neg_batch", x, model)
+
+def postprocess_model(model_path):
+    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, [[3, 'height', 'width']])
+    onnx.save(onnx_model, model_path)
+
+class ReshapeAndConv(nn.Module):
+    def __init__(self):
+        super(ReshapeAndConv, self).__init__()
+        self.conv = nn.Conv2d(3, 3, kernel_size=1, stride=1, padding=0)
+    def forward(self, x):
+        x = x.unsqueeze(axis=0)
+        out = self.conv(x)
+        return out
+
+x = Variable(torch.randn(3, 10, 10))
+model = ReshapeAndConv()
+save_data_and_model("reshape_and_conv_parameter_dims", x, model)
+postprocess_model("models/reshape_and_conv_parameter_dims.onnx")
diff --git a/testdata/dnn/onnx/models/reshape_and_conv_parameter_dims.onnx b/testdata/dnn/onnx/models/reshape_and_conv_parameter_dims.onnx
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index 000000000..409a3a7b5
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