Fix ConvTranspose: enhance attribute check (onnx#3000)

* add check for using auto_pad and pads simultaneously

* fix description for auto_pads == SAME_UPPER

* update docs for operator

* fix the old one as well

* add a test

* Revert "fix description for auto_pads == SAME_UPPER"

This reverts commit e75e287.

* Revert "update docs for operator"

This reverts commit 70952c0.

* Revert "fix the old one as well"

This reverts commit 8a0482d.

Co-authored-by: Ashwini Khade <askhade@microsoft.com>
Signed-off-by: Chun-Wei Chen <jacky82226@gmail.com>

onnx/defs/nn/defs.cc

@@ -1191,6 +1191,10 @@ void convTransposeShapeInference(InferenceContext& ctx) {
     if (pads.size() != n_input_dims * 2) {
       fail_shape_inference("Attribute pads has incorrect size");
     }
+    const auto* auto_pad_attr = ctx.getAttribute("auto_pad");
+    if (nullptr != auto_pad_attr) {
+      fail_shape_inference("The pads attribute cannot be used simultaneously with auto_pad attribute");
+    }
   } else {
     pads.assign(n_input_dims * 2, 0);
     const auto* auto_pad_attr = ctx.getAttribute("auto_pad");

onnx/test/shape_inference_test.py

@@ -1537,6 +1537,16 @@ def test_conv_transpose_with_group_and_output_shape(self):  # type: () -> None
             [])
         self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 64, 36, 36))])
 
+    def test_conv_transpose_with_pads_and_auto_pads(self):  # type: () -> None
+        # This test should fail because pads cannot be used simultaneously with auto_pad
+        graph = self._make_graph(
+            [('X', TensorProto.FLOAT, (1, 1, 2, 2)),
+             ('W', TensorProto.FLOAT, (1, 1, 3, 3)),
+             ('B', TensorProto.FLOAT, (1, ))],
+            [make_node('ConvTranspose', ['X', 'W', 'B'], 'Y', auto_pad="SAME_UPPER", strides=[1, 1], pads=[0, 1, 1, 0])],
+            [])
+        self.assertRaises(RuntimeError, onnx.shape_inference.infer_shapes, helper.make_model(graph))
+
     def test_mvn_function_output_shape(self):  # type: () -> None
         graph = self._make_graph(
             [('X', TensorProto.FLOAT, (25, 48, 16, 16))],