add debug info

examples/oneflow2onnx/models/test_alexnet.py

@@ -70,6 +70,7 @@ def test_alexnet():
     
     alexnet_graph = AlexNetGraph()
     alexnet_graph._compile(flow.randn(1, 3, 224, 224))
+    # print(alexnet_graph._full_graph_proto)
 
     with tempfile.TemporaryDirectory() as tmpdirname:
         flow.save(alexnet.state_dict(), tmpdirname)

oneflow_onnx/oneflow2onnx/flow2onnx.py

@@ -113,6 +113,9 @@ def FlowToOnnxNaive(graph, shape_override):
 
     def get_outputs(node):
         if is_user_op(node):
+            print(node)
+            print(type(node))
+            print(node.user_conf.output)
             obns = handler.flow_op.obn4op_type(get_op_type(node))
             if obns is None:
                 assert all([len(x.s) == 1 for x in node.user_conf.output.values()])

oneflow_onnx/oneflow2onnx/handlers/nn.py

@@ -254,6 +254,7 @@ class PoolOp:
         # T output = MaxPool(T input, @list(int) ksize, @list(int) strides, @string padding, @string data_format)
         # T Y = MaxPool(T X, @AttrType.STRING auto_pad, @AttrType.INTS kernel_shape, @AttrType.INTS pads,
         #               @AttrType.INTS strides)
+        print(node.output_tensor_names)
         if len(node.input_tensor_names) < 3:
             kernel_shape_flow = node.attrs["kernel_size"]
             strides_flow = node.attrs["stride"]
@@ -273,8 +274,6 @@ class PoolOp:
                 "padding_after", [0, 0]
             )
             node.attrs["pads"] = pads
-        _ConvConvertInputs(ctx, node, with_kernel=False)
-
 
 
 @flow_op(["pad"], onnx_op="Pad")