def test_caffe2_to_onnx_value_info(self):
caffe2_net = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
model = ModelHelper(name='caffe2-to-onnx-test')
brew.relu(model, ["X"], "Y")
caffe2_net.write(model.net.Proto().SerializeToString())
caffe2_net.flush()
args = [caffe2_net.name, '--output', output.name]
self.assertRaisesRegexp(Exception,
'value info',
self._run_command, caffe2_to_onnx, args)
args.extend([
'--value-info',
json.dumps({
'X': (onnx_pb2.TensorProto.FLOAT, (2, 2)),
})])
result = self._run_command(caffe2_to_onnx, args)
onnx_model = onnx_pb2.ModelProto()
onnx_model.ParseFromString(output.read())
self.assertEqual(len(onnx_model.graph.node), 1)
self.assertEqual(onnx_model.graph.node[0].op_type, 'Relu')
self.assertEqual(len(onnx_model.graph.initializer), 0)
def test_caffe2_to_onnx(self):
caffe2_net = tempfile.NamedTemporaryFile()
caffe2_init_net = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
model = ModelHelper(name='caffe2-to-onnx-test')
brew.relu(model, ["X"], "Y")
caffe2_net.write(model.net.Proto().SerializeToString())
caffe2_net.flush()
init_model = ModelHelper(name='caffe2-to-onnx-init-test')
init_model.net.GivenTensorFill([], 'X', shape=[2, 2],
values=np.zeros((2, 2)).flatten().astype(float))
caffe2_init_net.write(init_model.net.Proto().SerializeToString())
caffe2_init_net.flush()
result = self._run_command(
caffe2_to_onnx, [
caffe2_net.name,
'--caffe2-init-net', caffe2_init_net.name,
'--output', output.name,
])
onnx_model = onnx_pb2.ModelProto()
onnx_model.ParseFromString(output.read())
self.assertEqual(len(onnx_model.graph.node), 1)
self.assertEqual(onnx_model.graph.node[0].op_type, 'Relu')
self.assertEqual(len(onnx_model.graph.initializer), 1)
self.assertEqual(onnx_model.graph.initializer[0].name, onnx_model.graph.input[0].name)
def onnx_to_caffe2(onnx_model, output, init_net_output):
onnx_model_proto = onnx_pb2.ModelProto()
onnx_model_proto.ParseFromString(onnx_model.read())
graph_def = onnx_model_proto.graph
init_net, predict_net = c2.onnx_graph_to_caffe2_net(graph_def)
init_net_output.write(init_net.SerializeToString())
output.write(predict_net.SerializeToString())
def _load_model(model_file):
"""Load a ONNX model file from disk
Parameters
----------
model_file: str
Path where the model file path is (protobuf file)
Returns
-------
model: A ONNX protobuf model
"""
from onnx import onnx_pb2
from mmdnn.conversion.common.IR.IR_graph import load_protobuf_from_file
model = onnx_pb2.ModelProto()
load_protobuf_from_file(model, model_file)
print("ONNX model file [%s] loaded successfully." % model_file)
return model
def onnx_to_coreml(onnx_model, output):
onnx_model_proto = onnx_pb2.ModelProto()
onnx_model_proto.ParseFromString(onnx_model.read())
coreml_model = convert(onnx_model_proto)
coreml_model.save(output)
def onnx_to_coreml(onnx_model, output): # type: (IO[str], str) -> None
onnx_model_proto = onnx_pb2.ModelProto()
onnx_model_proto.ParseFromString(onnx_model.read())
coreml_model = convert(onnx_model_proto)
coreml_model.save(output)
