def to_keras(self):
"""Convert a Onnx model to KerasNet model.
"""
# parse network inputs, aka parameters
for init_tensor in self.graph.initializer:
if not init_tensor.name.strip():
raise ValueError("Tensor's name is required.")
self.initializer[init_tensor.name] = OnnxInput(
name=init_tensor.name, zvalue=OnnxHelper.to_numpy(init_tensor))
# converting GraphProto message
# i: ValueInfoProto
for i in self.graph.input:
if i.name in self.initializer:
# we should have added that via graph._initializer
self._all_tensors[i.name] = self.initializer[i.name]
else:
self._inputs[i.name] = OnnxInput(
name=i.name, zvalue=OnnxHelper.get_shape_from_node(i))
self._all_tensors[i.name] = self._inputs[i.name]
# constructing nodes, nodes are stored as directed acyclic graph
# converting NodeProto message
for node in self.graph.node:
inputs = []
for i in node.input:
if i == "":
continue
if i not in self._all_tensors:
raise Exception("Cannot find {}".format(i))
inputs.append(self._all_tensors[i])
mapper = OperatorMapper.of(node, self.initializer, inputs)
# update inputs and all_tensors
for input in mapper.model_inputs:
# Only update the original inputs here.
if input.name in self._inputs:
self._inputs[input.name] = input.zvalue
self._all_tensors[input.name] = input.zvalue
tensor = mapper.to_tensor()
output_ids = list(node.output)
assert len(output_ids) == 1 or node.op_type == "Dropout",\
"Only support single output for now"
self._all_tensors[output_ids[0]] = OnnxInput(name=tensor.name,
zvalue=tensor)
output_tensors = []
for i in self.graph.output:
if i.name not in self._all_tensors:
raise Exception("The output haven't been calculate")
output_tensors.append(self._all_tensors[i.name].zvalue)
model = zmodels.Model(input=list(self._inputs.values()),
output=output_tensors)
return model
def run_node(node, inputs):
inputs_list = []
assert len(inputs) == len(list(node.input))
for node_input, input_data in zip(node.input, inputs):
inputs_list.append(OnnxInput(node_input, input_data))
mapper = OperatorMapper.of(node, set(), inputs_list)
out_tensor = mapper.to_tensor()
model = zmodels.Model(input=[i.zvalue for i in mapper.model_inputs], output=out_tensor)
data = [i.data for i in mapper.model_inputs]
output = model.forward(data if len(data) > 1 else data[0])
result = {}
if isinstance(output, list):
assert len(output) == len(node.output)
for o, no in zip(output, node.output):
result[no] = o
else:
result[node.output[0]] = output
return result
def run_node(node, inputs, is_training=False):
warnings.warn("deprecated in 0.10.0, and will be removed in future release")
inputs_list = []
assert len(inputs) == len(list(node.input))
for node_input, input_data in zip(node.input, inputs):
inputs_list.append(OnnxInput(node_input, input_data))
mapper = OperatorMapper.of(node, set(), inputs_list)
out_tensor = mapper.to_tensor()
model = zmodels.Model(input=[i.zvalue for i in mapper.model_inputs], output=out_tensor)
data = [i.data for i in mapper.model_inputs]
model.training(is_training)
output = model.forward(data if len(data) > 1 else data[0])
result = {}
if isinstance(output, list):
assert len(output) == len(node.output)
for o, no in zip(output, node.output):
result[no] = o
else:
result[node.output[0]] = output
return result
