Unit tests for ONNX parser input tensors

src/omlt/io/onnx_parser.py

@@ -74,14 +74,14 @@ def parse_network(self, graph, scaling_object, input_bounds):
                     size.append(dim.dim_value)
                     dim_value *= dim.dim_value
             if dim_value is None:
-                raise ValueError(f"All dimensions in {graph} input tensor have 0 value.")
+                raise ValueError(f"All dimensions in graph \"{graph.name}\" input tensor have 0 value.")
             assert network_input is None
             network_input = InputLayer(size)
             self._node_map[input.name] = network_input
             network.add_layer(network_input)
 
         if network_input is None:
-            raise ValueError(f"No valid input layer found in {graph}.")
+            raise ValueError(f"No valid input layer found in graph \"{graph.name}\".")
 
         self._nodes = nodes
         self._nodes_by_output = nodes_by_output
@@ -116,7 +116,7 @@ def parse_network(self, graph, scaling_object, input_bounds):
                     value = _parse_constant_value(node)
                     self._constants[output] = value
             else:
-                raise ValueError(f"Nodes must have inputs or have op_type \"Constant\". {node} has no inputs and op_type {node.op_type}")
+                raise ValueError(f"Nodes must have inputs or have op_type \"Constant\". Node \"{node.name}\" has no inputs and op_type \"{node.op_type}\".")
 
         # traverse graph
         self._node_stack = list(inputs)

tests/io/test_onnx_parser.py

@@ -4,6 +4,7 @@
 
 if onnx_available:
     from omlt.io.onnx import load_onnx_neural_network
+    from omlt.io.onnx_parser import NetworkParser
 
 
 @pytest.mark.skipif(not onnx_available, reason="Need ONNX for this test")
@@ -105,3 +106,34 @@ def test_maxpool(datadir):
     assert layers[3].output_size == [3, 2, 1]
     for layer in layers[1:]:
         assert layer.kernel_depth == 3
+
+@pytest.mark.skipif(not onnx_available, reason="Need ONNX for this test")
+def test_input_tensor_invalid_dims(datadir):
+    model = onnx.load(datadir.file("keras_linear_131.onnx"))
+    model.graph.input[0].type.tensor_type.shape.dim[1].dim_value = 0
+    parser = NetworkParser()
+    with pytest.raises(ValueError) as excinfo:
+        parser.parse_network(model.graph,None,None)
+    expected_msg = "All dimensions in graph \"tf2onnx\" input tensor have 0 value."
+    assert str(excinfo.value) == expected_msg
+
+@pytest.mark.skipif(not onnx_available, reason="Need ONNX for this test")
+def test_no_input_layers(datadir):
+    model = onnx.load(datadir.file("keras_linear_131.onnx"))
+    model.graph.input.remove(model.graph.input[0])
+    parser = NetworkParser()
+    with pytest.raises(ValueError) as excinfo:
+        parser.parse_network(model.graph,None,None)
+    expected_msg = "No valid input layer found in graph \"tf2onnx\"."
+    assert str(excinfo.value) == expected_msg
+
+@pytest.mark.skipif(not onnx_available, reason="Need ONNX for this test")
+def test_node_no_inputs(datadir):
+    model = onnx.load(datadir.file("keras_linear_131.onnx"))
+    while (len(model.graph.node[0].input) > 0):
+        model.graph.node[0].input.pop()
+    parser = NetworkParser()
+    with pytest.raises(ValueError) as excinfo:
+        parser.parse_network(model.graph,None,None)
+    expected_msg = "Nodes must have inputs or have op_type \"Constant\". Node \"StatefulPartitionedCall/keras_linear_131/dense/MatMul\" has no inputs and op_type \"MatMul\"."
+    assert str(excinfo.value) == expected_msg