Support for bicubic resize

tests/test_backend.py

@@ -2437,6 +2437,17 @@ def func(x, x_new_size_):
             return tf.identity(x_, name=_TFOUTPUT)
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: x_new_size})
 
+    @check_tf_min_version("2.0", "Results are slightly different in tf1")
+    @check_opset_min_version(11, "resize bicubic")
+    def test_resize_bicubic(self):
+        x_shape = [1, 15, 20, 2]
+        new_size_val = np.array([30, 40], dtype=np.int32)
+        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        def func(x, new_size):
+            y = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.BICUBIC)
+            return tf.identity(y, name=_TFOUTPUT)
+        _ = self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: new_size_val}, rtol=1e-6, atol=1e-5)
+
     @check_opset_min_version(10, "resize scale can less than 1")
     def test_resize_nearest_neighbor2(self):
         x_shape = [1, 300, 20, 2]

tf2onnx/onnx_opset/nn.py

@@ -978,10 +978,12 @@ def version_13(cls, ctx, node, **kwargs):
         cls.any_version_after11(13, ctx, node, **kwargs)
 
 
-@tf_op(["ResizeBilinear", "ResizeNearestNeighbor"])
+@tf_op(["ResizeBilinear", "ResizeNearestNeighbor", "ResizeBicubic"])
 class Resize:
     @classmethod
     def version_7(cls, ctx, node, **kwargs):
+        utils.make_sure(node.type != "ResizeBicubic", "Opset 11 is required for bicubic interpolation for node %s",
+                        node.name)
         mode = "linear" if node.type == "ResizeBilinear" else "nearest"
         node.type = "Upsample"
         shape = ctx.get_shape(node.input[0])
@@ -1009,7 +1011,16 @@ def version_10(cls, ctx, node, **kwargs):
 
     @classmethod
     def version_11(cls, ctx, node, **kwargs):
-        mode = "linear" if node.type == "ResizeBilinear" else "nearest"
+        cubic_coeff_a = None
+        exclude_outside = False
+        if node.type == "ResizeBilinear":
+            mode = "linear"
+        elif node.type == "ResizeBicubic":
+            mode = "cubic"
+            cubic_coeff_a = -0.5
+            exclude_outside = True
+        else:
+            mode = "nearest"
         roi = ctx.make_const(utils.make_name("roi"), np.array([]).astype(np.float32))
         const_zero = ctx.make_const(utils.make_name("const_zero"), np.array([0]).astype(np.int64))
         const_two = ctx.make_const(utils.make_name("const_two"), np.array([2]).astype(np.int64))
@@ -1035,9 +1046,11 @@ def version_11(cls, ctx, node, **kwargs):
                 nearest_mode = "round_prefer_ceil"
             else:
                 transformation_mode = "half_pixel"
-        resize = ctx.make_node("Resize", resize_inputs,
-                               attr={"mode": mode, "nearest_mode": nearest_mode,
-                                     "coordinate_transformation_mode": transformation_mode})
+        attr = {"mode": mode, "nearest_mode": nearest_mode, "coordinate_transformation_mode": transformation_mode,
+                "exclude_outside": exclude_outside}
+        if cubic_coeff_a is not None:
+            attr["cubic_coeff_a"] = cubic_coeff_a
+        resize = ctx.make_node("Resize", resize_inputs, attr=attr)
         shapes = node.output_shapes
         dtypes = node.output_dtypes
         ctx.remove_node(node.name)
@@ -1050,6 +1063,8 @@ def _convert_since_9(cls, ctx, node, op_type, use_target_size=False):
         # float32 out = ResizeBilinear/ResizeNearestNeighbor(T images, int size)
         # https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor
         # wants the input to be NHWC - adjust target_shape to this.
+        utils.make_sure(node.type != "ResizeBicubic", "Opset 11 is required for bicubic interpolation for node %s",
+                        node.name)
         mode = "linear" if node.type == "ResizeBilinear" else "nearest"
 
         # because onnxruntime only supports to scale the last two dims so transpose is inserted