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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/27 20:23:33 UTC
[GitHub] ptrendx closed pull request #12398: Add support for networks with
multiple outputs in ONNX exporter
ptrendx closed pull request #12398: Add support for networks with multiple outputs in ONNX exporter
URL: https://github.com/apache/incubator-mxnet/pull/12398
This is a PR merged from a forked repository.
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diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
index b02d970f9c2..48e59aedf58 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
@@ -228,8 +228,14 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# Deriving the output_label name.
output_label = sym.get_internals()[len(sym.get_internals()) - 1].name + "_label"
- # Determine output shape
- output_shape = MXNetGraph.infer_output_shape(sym, params, in_shape, output_label)
+ # Determine outputs shapes
+ input_names = [n for n in sym.list_inputs() if n not in params]
+ input_pairs = {n: in_shape[i] for i, n in enumerate(input_names)}
+ _, output_shapes, _ = sym.get_internals().infer_shape(**input_pairs)
+
+ output_suffix = '_output'
+ output_names = [
+ o[:-len(output_suffix)] for o in sym.list_outputs() if o.endswith(output_suffix)]
weights = MXNetGraph.convert_weights_to_numpy(params)
@@ -265,6 +271,7 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
weights=weights,
in_shape=in_shape[graph_input_idx],
in_type=in_type,
+ out_shape=output_shapes[idx],
proc_nodes=all_processed_nodes,
initializer=initializer,
index_lookup=index_lookup)
@@ -279,6 +286,7 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
weights=weights,
in_shape=in_shape,
in_type=in_type,
+ out_shape=output_shapes[idx],
proc_nodes=all_processed_nodes,
initializer=initializer,
index_lookup=index_lookup,
@@ -294,14 +302,14 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# If converted node is NodeProto, add it in processed nodes list
elif isinstance(converted_node, NodeProto):
onnx_processed_nodes.append(converted_node)
- if idx == (len(mx_graph) - 1):
+ if idx == (len(mx_graph) - 1) or converted_node.name in output_names:
# If converted node doesnt have name, use it from output field
if not converted_node.name:
onnx_processed_outputs.append(
make_tensor_value_info(
name=converted_node.output[0],
elem_type=in_type,
- shape=output_shape
+ shape=output_shapes[idx]
)
)
else:
@@ -309,7 +317,7 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
make_tensor_value_info(
name=converted_node.name,
elem_type=in_type,
- shape=output_shape
+ shape=output_shapes[idx]
)
)
if verbose:
diff --git a/tests/python-pytest/onnx/export/mxnet_export_test.py b/tests/python-pytest/onnx/export/mxnet_export_test.py
index 9f91369d667..b007d174035 100644
--- a/tests/python-pytest/onnx/export/mxnet_export_test.py
+++ b/tests/python-pytest/onnx/export/mxnet_export_test.py
@@ -101,10 +101,10 @@ def forward_pass(sym, arg, aux, data_names, input_data):
batch = namedtuple('Batch', ['data'])
mod.forward(batch([mx.nd.array(input_data)]), is_train=False)
- return mod.get_outputs()[0].asnumpy()
+ return [output.asnumpy() for output in mod.get_outputs()]
-def test_models(model_name, input_shape, output_shape):
+def test_models(model_name, input_shape, output_shape, test_extra_output=False):
""" Tests Googlenet model for both onnx import and export"""
model_path, inputs, outputs = get_test_files(model_name)
logging.info("Translating model from ONNX model zoo to Mxnet")
@@ -117,6 +117,12 @@ def test_models(model_name, input_shape, output_shape):
new_model_name = "exported_" + model_name + ".onnx"
onnx_file = os.path.join(dir_path, new_model_name)
+ if test_extra_output:
+ logging.info("Adding extra output to model")
+ sym_output = sym.get_internals()[sym.list_outputs()[0]]
+ id_output = mx.sym.identity(data=sym_output)
+ sym = mx.symbol.Group([sym_output, id_output])
+
logging.info("Translating converted model from mxnet to ONNX")
converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
@@ -133,11 +139,11 @@ def test_models(model_name, input_shape, output_shape):
logging.info("Running inference on onnx re-import model in mxnet")
# run test for each test file
for input_data, output_data in zip(inputs, outputs):
- result = forward_pass(sym, arg_params, aux_params, data_names, input_data)
-
- # verify the results
- npt.assert_equal(result.shape, output_data.shape)
- npt.assert_almost_equal(output_data, result, decimal=3)
+ results = forward_pass(sym, arg_params, aux_params, data_names, input_data)
+ for result in results:
+ # verify the results
+ npt.assert_equal(result.shape, output_data.shape)
+ npt.assert_almost_equal(output_data, result, decimal=3)
logging.info(model_name + " conversion successful")
@@ -153,7 +159,7 @@ def test_model_accuracy(model_name, input_shape):
expected_result= []
for input_data, output_data in zip(inputs, outputs):
- result = forward_pass(sym, arg_params, aux_params, data_names, input_data)
+ result = forward_pass(sym, arg_params, aux_params, data_names, input_data)[0]
expected_result.append(result)
params = {}
@@ -175,7 +181,7 @@ def test_model_accuracy(model_name, input_shape):
actual_result = []
for input_data, output_data in zip(inputs, outputs):
- result = forward_pass(sym, arg_params, aux_params, data_names, input_data)
+ result = forward_pass(sym, arg_params, aux_params, data_names, input_data)[0]
actual_result.append(result)
# verify the results
@@ -232,7 +238,7 @@ def test_square():
converted_model = onnx_mxnet.export_model(square, params, [np.shape(input1)], np.float32, "square.onnx")
sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
- result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)
+ result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)[0]
numpy_op = np.square(input1)
@@ -242,6 +248,7 @@ def test_square():
test_models("bvlc_googlenet", (1, 3, 224, 224), (1, 1000))
test_models("bvlc_reference_caffenet", (1, 3, 224, 224), (1, 1000))
test_models("bvlc_reference_rcnn_ilsvrc13", (1, 3, 224, 224), (1, 200))
+ test_models("bvlc_googlenet", (1, 3, 224, 224), (1, 1000), test_extra_output=True)
# Comparing MXNet inference result, since MXNet results don't match
# ONNX expected results due to AveragePool issue github issue(#10194)
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