add onnx resize v10 and unit test (apache#6726)

python/tvm/relay/frontend/onnx.py

@@ -1870,6 +1870,25 @@ def _impl_v7(cls, inputs, attr, params):
 class Resize(OnnxOpConverter):
     """Operator converter for Resize"""
 
+    @classmethod
+    def _impl_v10(cls, inputs, attr, params):
+        mode = attr.get("mode")
+        if mode == b"nearest":
+            method = "nearest_neighbor"
+        elif mode == b"linear":
+            method = "bilinear"
+        else:
+            raise tvm.error.OpAttributeInvalid(
+                'Value {} in attribute "mode" of operator Resize is not valid.'.format(mode)
+            )
+
+        scale = inputs[1]
+        size = _op.cast(_op.shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
+
+        layout = "NCHW"  # ONNX assumes NCHW layout
+        out_size = _op.strided_slice(size, [2], [4])
+        return _op.image.resize(inputs[0], out_size, layout, method, "asymmetric")
+
     @classmethod
     def _impl_v11(cls, inputs, attr, params):
         mode = attr.get("mode")
@@ -1891,9 +1910,7 @@ def _impl_v11(cls, inputs, attr, params):
             size = inputs[3]
         else:
             assert len(scale_shape) != 0, "One of scale or size should be passed."
-            size = (
-                _op.cast(_op.shape_of(inputs[0]), infer_type(scale).type_annotation.dtype) * scale
-            )
+            size = _op.cast(_op.shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
 
         coord_trans = attr.get("coordinate_transformation_mode")
         if coord_trans in [b"pytorch_half_pixel", b"half_pixel"]:

tests/python/frontend/onnx/test_forward.py

@@ -3525,6 +3525,36 @@ def verify(ishape, oshape, scales, mode, coord_trans):
     verify([1, 16, 32, 32], [], [1, 1, 2, 2], "nearest", "asymmetric")
     verify([1, 16, 32, 32], [], [1, 1, 0.5, 0.5], "linear", "half_pixel")
 
+    def verify_opset_10(ishape, scales, mode):
+        nodes = [
+            make_constant_node("scales", onnx.TensorProto.FLOAT, (len(scales),), scales),
+        ]
+        input_names = ["X", "scales"]
+        nodes.append(
+            helper.make_node(
+                "Resize",
+                inputs=input_names,
+                outputs=["Y"],
+                mode=mode,
+            )
+        )
+
+        oshape = [round(dim * scale) for (dim, scale) in zip(ishape, scales)]
+        graph = helper.make_graph(
+            nodes,
+            "resize_test",
+            inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, ishape)],
+            outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, oshape)],
+        )
+
+        model = helper.make_model(graph, producer_name="resize_test")
+        model.opset_import[0].version = 10
+
+        verify_with_ort(model, [ishape], oshape, use_vm=True, freeze_params=True)
+
+    verify_opset_10([1, 16, 32, 32], [1, 1, 2, 2], "nearest")
+    verify_opset_10([1, 16, 32, 32], [1, 1, 0.5, 0.5], "linear")
+
 
 @tvm.testing.uses_gpu
 def test_nonzero():