[Relay][Frontend][ONNX] Raise error when user provides an input not in the onnx graph.

python/tvm/relay/frontend/onnx.py

@@ -2914,7 +2914,7 @@ def from_onnx(self, graph, opset, get_output_expr=False):
             else:
                 self._num_input += 1
                 if i_name in self._shape:
-                    i_shape = self._shape[i_name]
+                    i_shape = self._shape.pop(i_name)
                 else:
                     if "?" in str(i_shape):
                         warning_msg = (
@@ -2929,6 +2929,11 @@ def from_onnx(self, graph, opset, get_output_expr=False):
                     dtype = d_type
                 self._nodes[i_name] = new_var(i_name, shape=i_shape, dtype=dtype)
             self._inputs[i_name] = self._nodes[i_name]
+        assert (
+            len(self._shape) == 0
+        ), "User specified the shape for inputs that weren't found in the graph: " + str(
+            self._shape
+        )
         # get list of unsupported ops
         convert_map = _get_convert_map(opset)
         unsupported_ops = set()

tests/python/frontend/onnx/test_forward.py

@@ -19,6 +19,7 @@
 from onnx import helper, TensorProto, mapping, numpy_helper
 import torch
 import torchvision
+import pytest
 import tvm.topi.testing
 import tvm
 from tvm import relay
@@ -57,7 +58,7 @@ def get_tvm_output_with_vm(
         mod = relay.transform.DynamicToStatic()(mod)
 
     ex = relay.create_executor("vm", mod=mod, ctx=ctx, target=target)
-    result = ex.evaluate()(*input_data)
+    result = ex.evaluate()(*input_data, **params)
     if isinstance(result, tvm.runtime.NDArray):
         return result.asnumpy()
     return [r.asnumpy() for r in result]
@@ -500,7 +501,7 @@ def test_squeeze():
 
     model = helper.make_model(graph, producer_name="squeeze_test")
     x = np.random.uniform(size=in_shape).astype("float32")
-    verify_with_ort_with_inputs(model, [x], [out_shape])
+    verify_with_ort_with_inputs(model, [x], [out_shape], opset=11)
 
 
 @tvm.testing.uses_gpu
@@ -538,7 +539,7 @@ def test_unsqueeze():
     )
 
     model = helper.make_model(graph, producer_name="squeeze_test")
-    verify_with_ort(model, [in_shape])
+    verify_with_ort(model, [in_shape], opset=11)
 
 
 def verify_gather(in_shape, indices, axis, dtype):
@@ -1584,7 +1585,7 @@ def verify_pad_v11(indata, pads, mode="constant", value=0.0):
     pads = np.array(pads)
     #  onnx graph
     if mode in ["edge", "reflect"]:
-        inputs = [indata, pads]
+        inputs = [indata]
         outdata = np.pad(indata, pad_width=np_pads, mode=mode)
         node = helper.make_node("Pad", inputs=["input", "pads"], outputs=["output"], mode=mode)
         graph = helper.make_graph(
@@ -1600,7 +1601,7 @@ def verify_pad_v11(indata, pads, mode="constant", value=0.0):
             ],
         )
     else:
-        inputs = [indata, pads, np.array([value]).astype("float32")]
+        inputs = [indata]
         outdata = np.pad(indata, pad_width=np_pads, mode="constant", constant_values=value)
         node = helper.make_node(
             "Pad", inputs=["input", "pads", "constant_value"], outputs=["output"], mode="constant"
@@ -1663,7 +1664,7 @@ def verify_reduce_func(func, data, axis, keepdims):
 
     model = helper.make_model(graph, producer_name="reduce_test")
 
-    verify_with_ort_with_inputs(model, [data], [outshape])
+    verify_with_ort_with_inputs(model, [data], [outshape], opset=11)
 
 
 @tvm.testing.uses_gpu
@@ -4089,6 +4090,31 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
     verify_cumsum(data, 1, 1, 1, type="int32")
 
 
+def test_wrong_input():
+    node = helper.make_node(
+        "Softplus",
+        inputs=["X"],
+        outputs=["Y"],
+    )
+
+    graph = helper.make_graph(
+        [node],
+        "softplus_test",
+        inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, list([5]))],
+        outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, list([5]))],
+    )
+    model = helper.make_model(graph, producer_name="softplus_test")
+
+    # Check that the graph can import correctly with proper shape definitions.
+    correct_shape_dict = {"X": [5]}
+    relay.frontend.from_onnx(model, shape=correct_shape_dict)
+
+    # Check that an assertion is triggered when an input not in the graph is provided.
+    wrong_shape_dict = {"Z": [5]}
+    with pytest.raises(AssertionError):
+        relay.frontend.from_onnx(model, shape=wrong_shape_dict)
+
+
 if __name__ == "__main__":
     test_flatten()
     test_reshape()
@@ -4167,3 +4193,4 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
     test_maxunpool()
     test_softplus()
     test_cumsum()
+    test_wrong_input()