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#!/usr/bin/python
# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tflite.Model
import tflite.SubGraph
import tflite.Operator
import tflite.OperatorCode
import tflite.BuiltinOperator
from operator_wrapping import Operator, EnumStrMaps
from tensor_wrapping import Tensor, SetTensorTypeStr
from operation import Operation
class OperatorParser(object):
def __init__(self, tf_model, tf_subgraph):
self.tf_model = tf_model
self.tf_subgraph = tf_subgraph
self.operators_in_list = list()
self.operators_per_type = dict()
# Tensor type string table
SetTensorTypeStr()
def Parse(self):
for operator_idx in range(self.tf_subgraph.OperatorsLength()):
tf_operator = self.tf_subgraph.Operators(operator_idx)
opcode_str = self.GetOpcodeStr(tf_operator)
input_tensors = self.GetInputTensors(tf_operator)
output_tensors = self.GetOutputTensors(tf_operator)
op = Operator(operator_idx, tf_operator, input_tensors, output_tensors,
opcode_str)
self.AppendOperator(op)
def GetOpcodeStr(self, tf_operator):
opcode_list_idx = tf_operator.OpcodeIndex()
opcode_id = self.tf_model.OperatorCodes(opcode_list_idx).BuiltinCode()
opcode_str = EnumStrMaps.BuiltinOpcode[opcode_id]
if opcode_id == 32:
# Custom operator
custom_operator = self.tf_model.OperatorCodes(tf_operator.OpcodeIndex())
custom_op_name = custom_operator.CustomCode().decode('utf-8')
opcode_str = opcode_str + "(" + custom_op_name + ")"
return opcode_str
def GetInputTensors(self, tf_operator):
operator_inputs = tf_operator.InputsAsNumpy()
return self.GetTensors(operator_inputs)
def GetOutputTensors(self, tf_operator):
operator_outputs = tf_operator.OutputsAsNumpy()
return self.GetTensors(operator_outputs)
def GetTensors(self, tf_tensors_index):
return_list = list()
for tensor_idx in tf_tensors_index:
# in case of optional input, tensor_idx == -1
if (tensor_idx < 0):
return_list.append(Tensor(tensor_idx, None, None))
continue
tf_tensor = self.tf_subgraph.Tensors(tensor_idx)
buffer_idx = tf_tensor.Buffer()
tf_buffer = self.tf_model.Buffers(buffer_idx)
return_list.append(Tensor(tensor_idx, tf_tensor, tf_buffer))
return return_list
def GetAllTensors(self):
return_list = list()
for tensor_idx in range(self.tf_subgraph.TensorsLength()):
if (tensor_idx < 0):
return_list.append(Tensor(tensor_idx, 0, 0))
continue
tf_tensor = self.tf_subgraph.Tensors(tensor_idx)
buffer_idx = tf_tensor.Buffer()
tf_buffer = self.tf_model.Buffers(buffer_idx)
return_list.append(Tensor(tensor_idx, tf_tensor, tf_buffer))
return return_list
def AppendOperator(self, operator):
self.operators_in_list.append(operator)
opcode_str = operator.opcode_str
if opcode_str not in self.operators_per_type:
self.operators_per_type[opcode_str] = list()
self.operators_per_type[opcode_str].append(operator)
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