diff --git a/tf2onnx/onnx_opset/nn.py b/tf2onnx/onnx_opset/nn.py
index 2b1ce5442..027803781 100644
--- a/tf2onnx/onnx_opset/nn.py
+++ b/tf2onnx/onnx_opset/nn.py
@@ -1391,20 +1391,37 @@ def version_11(cls, ctx, node, **kwargs):
         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))
-        const_empty_float = ctx.make_const(utils.make_name("const_empty_float"), np.array([]).astype(np.float32))
         input_nchw = ctx.make_node("Transpose", [node.input[0]], {"perm": constants.NHWC_TO_NCHW})
-        shape_input = ctx.make_node("Shape", [input_nchw.output[0]])
-        sliced_shape = ctx.make_node("Slice", [shape_input.output[0], const_zero.output[0], const_two.output[0]])
-        size_int64 = ctx.make_node("Cast", [node.input[1]], attr={"to": onnx_pb.TensorProto.INT64})
-        concat_shape = ctx.make_node("Concat", [sliced_shape.output[0], size_int64.output[0]], {'axis': 0})
-        resize_inputs = [
-            input_nchw.output[0],
-            roi.output[0],
-            const_empty_float.output[0],
-            concat_shape.output[0]
-        ]
+        shape = ctx.get_shape(node.input[0])
+        if shape and shape[2] != -1 and shape[1] != -1 and node.inputs[1].is_const():
+            target_shape = node.inputs[1].get_tensor_value()
+            n, h, w, c = shape
+            nh, nw = target_shape
+            if "sizes" in node.attr:
+                sizes_val = np.array([1.0, 1.0, nh, nw]).astype(np.int64)
+                resize_params = ctx.make_const(utils.make_name("sizes"), sizes_val, raw=False)
+            else:  # scales
+                scale_val = np.array([1.0, 1.0, float(nh) / h, float(nw) / w]).astype(np.float32)
+                resize_params = ctx.make_const(utils.make_name("scales"), scale_val, raw=False)
+            resize_inputs = [
+                input_nchw.output[0],
+                roi.output[0],
+                resize_params.output[0]
+            ]
+        else:
+            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))
+            const_empty_float = ctx.make_const(utils.make_name("const_empty_float"), np.array([]).astype(np.float32))
+            shape_input = ctx.make_node("Shape", [input_nchw.output[0]])
+            sliced_shape = ctx.make_node("Slice", [shape_input.output[0], const_zero.output[0], const_two.output[0]])
+            size_int64 = ctx.make_node("Cast", [node.input[1]], attr={"to": onnx_pb.TensorProto.INT64})
+            concat_shape = ctx.make_node("Concat", [sliced_shape.output[0], size_int64.output[0]], {'axis': 0})
+            resize_inputs = [
+                input_nchw.output[0],
+                roi.output[0],
+                const_empty_float.output[0],
+                concat_shape.output[0]
+            ]
         transformation_mode = "asymmetric"
         nearest_mode = "floor"
         if "align_corners" in node.attr and node.attr["align_corners"].i: