fix test regressions

python/tvm/relay/frontend/onnx.py

@@ -1972,7 +1972,6 @@ def _impl_v10(cls, inputs, attr, params):
 
         scale = inputs[1]
         size = _op.cast(shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
-        size = _op.cast(size, "int64")
         layout = "NCHW"  # ONNX assumes NCHW layout
         out_size = fold_constant(_op.strided_slice(size, [2], [4]))
         return _op.image.resize(inputs[0], out_size, layout, method, "asymmetric")
@@ -1999,7 +1998,6 @@ def _impl_v11(cls, inputs, attr, params):
         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
-        size = _op.cast(size, "int64")
 
         coord_trans = attr.get("coordinate_transformation_mode")
         if coord_trans in [b"pytorch_half_pixel", b"half_pixel"]:

python/tvm/relay/op/image/image.py

@@ -67,7 +67,7 @@ def resize(
         The resized result.
     """
     if isinstance(size, Constant):
-        size = list(size.data.asnumpy())
+        size = list(size.data.asnumpy().astype("int32"))
     if isinstance(size, Expr):
         return _dyn_make.resize(
             data, size, layout, method, coordinate_transformation_mode, out_dtype

tests/python/relay/test_op_grad_level3.py

@@ -146,7 +146,7 @@ def test_zeros_ones_grad_const_ints():
 
 def test_zeros_ones_grad_const_expr():
     # when shape is static (i.e. not an input), there is no gradient at all
-    shape_const = relay.const(np.array([2, 3, 4]), dtype="int32")
+    shape_const = relay.const(np.array([2, 3, 4]), dtype="int32") * relay.const(1, dtype="int32")
     static_ty = relay.TensorType([2, 3, 4], dtype="float32")
     dyn_ty = relay.TensorType([relay.Any(), relay.Any(), relay.Any()], dtype="float32")
     expected_ty_static = relay.TupleType([static_ty, relay.TupleType([])])