diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index fa0eac9bb15e..a695e0002b34 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -368,8 +368,8 @@ def autopad(data, strides, kernel_shape, dilations, ndim, pad_type="constant", d
         ),
         dtype="int64",
     )
-    shape = _op.strided_slice(shape_of(data, dtype="int64"), [2], [ndim])
     # get input shape
+    shape = _op.strided_slice(shape_of(data, dtype="int64"), [2], [ndim])
 
     # set up integer constants
     zero = _op.const(0, dtype="int64")
@@ -410,12 +410,24 @@ def _impl_v1(cls, inputs, attr, params):
         data = inputs[0]
         input_shape = infer_shape(data)
         ndim = len(input_shape)
+
+        kernel_type = infer_type(inputs[1])
+        kernel_shapes = [get_const_tuple(kernel_type.checked_type.shape)]
+        if "kernel_shape" not in attr:
+            attr["kernel_shape"] = kernel_shapes[0][2:]
+
         if "auto_pad" in attr:
             attr["auto_pad"] = attr["auto_pad"].decode("utf-8")
             if attr["auto_pad"] in ("SAME_UPPER", "SAME_LOWER"):
                 # Warning: Convolution does not yet support dynamic shapes,
                 # one will need to run dynamic_to_static on this model after import
-                data = autopad(data, attr["strides"], attr["kernel_shape"], attr["dilations"], ndim)
+                data = autopad(
+                    data,
+                    attr.get("strides", [1] * (ndim - 2)),
+                    attr["kernel_shape"],
+                    attr.get("dilations", [1] * (ndim - 2)),
+                    ndim,
+                )
             elif attr["auto_pad"] == "VALID":
                 attr["pads"] = tuple([0 for i in range(ndim - 2)])
             elif attr["auto_pad"] == "NOTSET":
@@ -424,7 +436,6 @@ def _impl_v1(cls, inputs, attr, params):
                 msg = 'Value {} in attribute "auto_pad" of operator Conv is invalid.'
                 raise tvm.error.OpAttributeInvalid(msg.format(attr["auto_pad"]))
             attr.pop("auto_pad")
-
         out = AttrCvt(
             op_name=dimension_picker("conv"),
             transforms={
@@ -469,9 +480,9 @@ def _impl_v1(cls, inputs, attr, params):
                 # one will need to run dynamic_to_static on this model after import
                 data = autopad(
                     data,
-                    attr["strides"],
+                    attr.get("strides", [1] * (ndim - 2)),
                     attr["kernel_shape"],
-                    attr["dilations"],
+                    attr.get("dilations", [1] * (ndim - 2)),
                     ndim,
                     deconv=True,
                 )
diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index 1eaae6f30ea2..1a3d0d4ac6e0 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -2390,16 +2390,20 @@ def verify_conv(
             # groups=1
         )
     elif padding is None:
+        ## autopadding with unset default attributes
+        kwargs = {}
+        if not all([s == 1 for s in strides]):
+            kwargs["strides"] = strides
+        if not all([d == 1 for d in dilations]):
+            kwargs["dilations"] = dilations
+
         node = helper.make_node(
             "Conv",
             inputs=["x", "W"],
             outputs=["y"],
-            kernel_shape=kernel_shape,
             # Default values for other attributes:
-            strides=strides,
-            dilations=dilations,
-            # groups=1
             auto_pad=auto_pad,
+            **kwargs,
         )
     else:
         node = helper.make_node(