def test_make_tensor_value_info(self): # type: () -> None
vi = helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 4))
checker.check_value_info(vi)
# scalar value
vi = helper.make_tensor_value_info('Y', TensorProto.FLOAT, ())
checker.check_value_info(vi)
def test_make_tensor_value_info(self): # type: () -> None
vi = helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 4))
checker.check_value_info(vi)
# scalar value
vi = helper.make_tensor_value_info('Y', TensorProto.FLOAT, ())
checker.check_value_info(vi)
def test_make_sparse_tensor_value_info(self) -> None:
vi = helper.make_sparse_tensor_value_info('X', TensorProto.FLOAT, (2, 3))
checker.check_value_info(vi)
# scalar value
vi = helper.make_sparse_tensor_value_info('Y', TensorProto.FLOAT, ())
checker.check_value_info(vi)
def _conv_json_to_onnx(self, export_path):
tflite_model = self._model
tflite_tensors = self._model.subgraphs.tensors
operator_codes = self._model.operator_codes
# graph name
onnx_graph_name = self._model_name
# graph input& output
input_node_indices = tflite_model.subgraphs.inputs
output_node_indices = tflite_model.subgraphs.outputs
onnx_graph_inputs = [
self._create_value_info(tflite_tensors[index])
for index in input_node_indices
]
onnx_graph_outputs = [
self._create_value_info(tflite_tensors[index])
for index in output_node_indices
]
# onnx_graph_outputs = [helper.make_tensor_value_info(name=tflite_tensors[output_node_indices[0]].name,
# elem_type=mapping.NP_TYPE_TO_TENSOR_TYPE[TFLITE_TENSOR_TYPE_TO_NP_TYPE[tflite_tensors[output_node_indices[0]].tensor_type]],
# shape=(1,20,4)),
# helper.make_tensor_value_info(name=tflite_tensors[output_node_indices[1]].name,
# elem_type=mapping.NP_TYPE_TO_TENSOR_TYPE[TFLITE_TENSOR_TYPE_TO_NP_TYPE[tflite_tensors[output_node_indices[1]].tensor_type]],
# shape=(20,1)),
# helper.make_tensor_value_info(name=tflite_tensors[output_node_indices[2]].name,
# elem_type=mapping.NP_TYPE_TO_TENSOR_TYPE[TFLITE_TENSOR_TYPE_TO_NP_TYPE[tflite_tensors[output_node_indices[2]].tensor_type]],
# shape=(20,1)),
# helper.make_tensor_value_info(name=tflite_tensors[output_node_indices[3]].name,
# elem_type=mapping.NP_TYPE_TO_TENSOR_TYPE[TFLITE_TENSOR_TYPE_TO_NP_TYPE[tflite_tensors[output_node_indices[3]].tensor_type]],
# shape=(1,))]
nodes = []
for (i, o) in enumerate(tflite_model.subgraphs.operators): #
# todo: op & version adaptive
op_type_name = operator_codes[o.opcode_index].builtin_code
# inputs
tflite_input_tensors = [tflite_tensors[idx] for idx in o.inputs]
tflite_output_tensors = [tflite_tensors[idx] for idx in o.outputs]
tflite_input_buffers = [
tflite_model.np_buffers[tensor.buffer]
for tensor in tflite_input_tensors
]
tflite_output_buffers = [
tflite_model.np_buffers[tensor.buffer]
for tensor in tflite_output_tensors
]
ops = tflite_ops.clsmembers
if hasattr(ops[op_type_name], "TFLITE_OP"
) and ops[op_type_name].TFLITE_OP == op_type_name:
nodes.append(ops[op_type_name].create_onnx_node(
operator=o,
operator_idx=i,
inputs=tflite_input_tensors,
outputs=tflite_output_tensors,
input_buffers=tflite_input_buffers,
output_buffers=tflite_output_buffers,
data_format=self.data_format))
else:
raise ValueError("OP Not Suppoted")
# onnx node
onnx_nodes = []
for n in nodes:
for on in n.onnx_nodes:
checker.check_node(on)
onnx_nodes.append(on)
# print(getattr(n, "version_1", None))
# graph value_info
output_value_info_list = []
graph_value_info = []
output_scalar_names = []
register_names = [input.name for input in onnx_graph_inputs]
#register_names.extend([output.name for output in onnx_graph_outputs])
for index in output_node_indices:
if (tflite_tensors[index].shape != []):
register_names.append(tflite_tensors[index].name)
else:
output_scalar_names.append(tflite_tensors[index].name)
for n in nodes:
for value_info in n.onnx_value_infos:
if value_info.name in register_names:
continue
register_names.append(value_info.name)
checker.check_value_info(value_info)
graph_value_info.append(value_info)
if value_info.name in output_scalar_names:
output_value_info_list.append(value_info)
# remake output value info
for index in output_node_indices:
if (tflite_tensors[index].shape != []):
output_value_info_list.append(
self._create_value_info(tflite_tensors[index]))
onnx_graph_outputs = output_value_info_list
onnx_tensors = self._create_onnx_tensor(nodes)
graph = self._create_onnx_graph(nodes=onnx_nodes,
name=onnx_graph_name,
inputs=onnx_graph_inputs,
outputs=onnx_graph_outputs,
initializer=onnx_tensors,
doc_string=tflite_model.description,
value_info=graph_value_info)
onnx_model = self._create_onnx_model(graph)
inferred_model = shape_inference.infer_shapes(onnx_model)
save(inferred_model,
path.join(export_path, '{0}.onnx'.format(self.model_name)))
