diff --git a/python/tvm/driver/tvmc/frontends.py b/python/tvm/driver/tvmc/frontends.py
index 976159d0ff5b..bb54b82cceca 100644
--- a/python/tvm/driver/tvmc/frontends.py
+++ b/python/tvm/driver/tvmc/frontends.py
@@ -161,16 +161,7 @@ def load(self, path):
         # pylint: disable=E1101
         model = onnx.load(path)
 
-        # pylint: disable=E1101
-        name = model.graph.input[0].name
-
-        # pylint: disable=E1101
-        proto_shape = model.graph.input[0].type.tensor_type.shape.dim
-        shape = [d.dim_value for d in proto_shape]
-
-        shape_dict = {name: shape}
-
-        return relay.frontend.from_onnx(model, shape_dict)
+        return relay.frontend.from_onnx(model)
 
 
 class TensorflowFrontend(Frontend):
diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index d65f5676fb33..377b739650ca 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -2737,6 +2737,12 @@ def from_onnx(model, shape=None, dtype="float32", opset=None, freeze_params=Fals
                 warnings.warn(str(e))
     except ImportError:
         pass
+
+    # if no explicit input's shape came from user, then initialize shape as it is defined in onnx model
+    if shape is None:
+        shape = {}
+        for i in model.graph.input:
+            shape[i.name] = [dim.dim_value for dim in i.type.tensor_type.shape.dim]
     g = GraphProto(shape, dtype)
     graph = model.graph
     if opset is None: