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## @package onnx
# Module caffe2.python.onnx.backend_rep_cpp
from onnx.backend.base import BackendRep, namedtupledict
# This is a wrapper around C++ Caffe2BackendRep,
# mainly to handle the different input and output types for convenience of Python
class Caffe2CppRep(BackendRep):
def __init__(self, cpp_rep):
super(Caffe2CppRep, self).__init__()
self.__core = cpp_rep
self.__external_outputs = cpp_rep.external_outputs()
self.__external_inputs = cpp_rep.external_inputs()
self.__uninitialized_inputs = cpp_rep.uninitialized_inputs()
def init_net(self):
return self.__core.init_net()
def pred_net(self):
return self.__core.pred_net()
def external_outputs(self):
return self.__core.external_outputs()
def external_inputs(self):
return self.__core.external_inputs()
def run(self, inputs):
output_values = None
if isinstance(inputs, dict):
output_values = self.__core.run(inputs)
elif isinstance(inputs, list) or isinstance(inputs, tuple):
if len(inputs) != len(self.__uninitialized_inputs):
raise RuntimeError('Expected {} values for uninitialized '
'graph inputs ({}), but got {}.'.format(
len(self.__uninitialized_inputs),
', '.join(self.__uninitialized_inputs),
len(inputs)))
input_map = {}
for k, v in zip(self.__uninitialized_inputs, inputs):
input_map[k] = v
output_values = self.__core.run(input_map)
else:
# single input
output_values = self.__core.run([inputs])
return namedtupledict('Outputs', self.__external_outputs)(*output_values)
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