diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index e16b109ab564..ee80436a8a01 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -2143,18 +2143,24 @@ def _impl_v18(cls, bb, inputs, attr, params):
 
         # Convert scales to sizes if needed.
         if scales is not None:
-            assert isinstance(scales, relax.Constant), "Only constant scales currently supported."
-            scales = scales.data.numpy()
+            if isinstance(scales, relax.Constant):
+                scales = scales.data.numpy()
+            elif isinstance(scales, relax.expr.ShapeExpr):
+                scales = [int(val.value) for val in scales.values]
+            else:
+                assert f"Type {type(scales)} for scale is currently unsupported."
             sizes = []
 
             for i, dim in enumerate(x.struct_info.shape):
                 sizes.append(cast(scales[i] * dim, "int64"))
             sizes = sizes[2:]
         else:
-            assert isinstance(
-                sizes, relax.Constant
-            ), "Only constant output size currently supported."
-            sizes = sizes.data.numpy().astype("int64").tolist()[2:]
+            if isinstance(sizes, relax.Constant):
+                sizes = sizes.data.numpy().astype("int64").tolist()[2:]
+            elif isinstance(sizes, relax.expr.ShapeExpr):
+                sizes = [int(val.value) for val in sizes.values][2:]
+            else:
+                assert f"Type {type(size)} for size is currently unsupported."
 
         return relax.op.image.resize2d(
             x,
@@ -3751,6 +3757,7 @@ def _construct_nodes(self, graph: onnx.onnx_ml_pb2.GraphProto):
             # convert it to a tensor.
             shape_compatible_ops = [
                 "Reshape",
+                "Resize",
                 "ConstantOfShape",
                 "Gather",
                 "Slice",