[ONNX] [Relay] Resize Opset 13

python/tvm/relay/frontend/onnx.py

@@ -2697,6 +2697,40 @@ def _impl_v10(cls, inputs, attr, params):
 
     @classmethod
     def _impl_v11(cls, inputs, attr, params):
+        scale = inputs[2]
+        scale_shape = infer_shape(scale)
+        if len(inputs) == 4:
+            assert (
+                len(scale_shape) == 0 or scale_shape[0] == 0
+            ), "One of scale or size should be passed, not both."
+            size = inputs[3]
+        else:
+            assert len(scale_shape) != 0, "One of scale or size should be passed."
+            size = _op.cast(shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
+        return cls.v11_13_common(inputs, size, attr, params)
+
+    @classmethod
+    def _impl_v13(cls, inputs, attr, params):
+        scale = inputs[2]
+        size = inputs[3]
+        if size is not None:
+            assert scale is None, "One of scale or size should be passed, not both."
+        else:
+            scale_type = infer_type(scale)
+            scale_shape = scale_type.checked_type.shape
+            scale_dtype = scale_type.checked_type.dtype
+            assert len(scale_shape) != 0, "One of scale or size should be passed."
+            size = _op.cast(shape_of(inputs[0]), scale_dtype) * scale
+
+        return cls.v11_13_common(inputs, size, attr, params)
+
+    @classmethod
+    def v11_13_common(cls, inputs, size, attr, params):
+        """
+        Resize v11 and Resize v13 are identical except in how
+        they handle the passing of scale and size. This utility
+        provides the implementation for both
+        """
         ndims = len(infer_shape(inputs[0]))
         mode = attr.get("mode").decode("ascii")
         if mode == "nearest":
@@ -2715,16 +2749,6 @@ def _impl_v11(cls, inputs, attr, params):
         alpha = attr.get("cubic_coeff_a", -0.75)
         exclude = attr.get("exclude_outside", 0)
 
-        scale = inputs[2]
-        scale_shape = infer_shape(scale)
-        if len(inputs) == 4:
-            assert (
-                len(scale_shape) == 0 or scale_shape[0] == 0
-            ), "One of scale or size should be passed, not both."
-            size = inputs[3]
-        else:
-            assert len(scale_shape) != 0, "One of scale or size should be passed."
-            size = _op.cast(shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
         out_size = fold_constant(_op.strided_slice(size, [2], [4]))
         out = None
         if ndims == 3:

tests/python/frontend/onnx/test_forward.py

@@ -3970,6 +3970,7 @@ def verify(ishape, oshape, scales, mode, coord_trans="asymmetric", alpha=0.5, ex
             make_constant_node("scales", onnx.TensorProto.FLOAT, (len(scales),), scales),
         ]
         input_names = ["X", "roi", "scales"]
+
         if oshape != []:
             nodes.append(
                 make_constant_node("sizes", onnx.TensorProto.INT64, (len(oshape),), oshape)
@@ -4954,15 +4955,7 @@ def verify_eyelike(indata):
     "test_reduce_sum_keepdims_random",
     "test_reduce_sum_negative_axes_keepdims_example",
     "test_reduce_sum_negative_axes_keepdims_random",
-    "test_resize_downsample_sizes_cubic",
-    "test_resize_downsample_sizes_linear_pytorch_half_pixel",
-    "test_resize_downsample_sizes_nearest",
     "test_resize_tf_crop_and_resize",
-    "test_resize_upsample_sizes_cubic",
-    "test_resize_upsample_sizes_nearest",
-    "test_resize_upsample_sizes_nearest_ceil_half_pixel",
-    "test_resize_upsample_sizes_nearest_floor_align_corners",
-    "test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric",
     "test_rnn_seq_length",
     "test_round",
     "test_scan9_sum",