Tensorflow模型量化4 --pb转tflite(uint8量化)小结_tflite量化 custom op

Tensorflow模型量化4 --pb转tflite小结(uint8量化)

1. 实验环境：tensorflow-gpu1.15+cuda10.0

模型的fp16量化和int8量化我之前有写，参考：

龟龟：Tensorflow模型量化实践2--量化自己训练的模型​zhuanlan.zhihu.com

这次发现uint8量化时有参数设置，所以准备是从头再梳理一遍

2.参与量化的模型：

训练tensorflow-object-detection API 得到的ssdlite_mobilenet _v2模型，导出为frozen_inference_graph.pb

3.获取输入输出节点

进行frozen_inference_graph.pb模型解析，得到输入输出节点信息

代码入下：

"""
code by zzg
"""
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto() 
config.gpu_options.allow_growth = True 
 
with tf.Session() as sess:
    with open('frozen_inference_graph_resnet.pb','rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
 
        tf.import_graph_def(graph_def, name='')
        tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
        for tensor_name in tensor_name_list:
             print(tensor_name,'\n')

之后找到输入节点在预处理之后入下所示：

找到输出节点在后处理之前，如下图所示：

 

4.量化（pb->tflite）

4.1方法一：利用TFLiteConverter

'''
code by zzg 2020-04-27
'''
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto() 
config.gpu_options.allow_growth = True 
 
graph_def_file = "frozen_inference_graph.pb"
 
input_names = ["FeatureExtractor/MobilenetV2/MobilenetV2/input"]
output_names = ["concat", "concat_1"]
input_tensor = {input_names[0]:[1,300,300,3]}
 
 
#uint8 quant
converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_names, output_names, input_tensor)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,tf.lite.OpsSet.SELECT_TF_OPS]
converter.allow_custom_ops=True
 
converter.inference_type = tf.uint8    #tf.lite.constants.QUANTIZED_UINT8
input_arrays = converter.get_input_arrays()
converter.quantized_input_stats = {input_arrays[0]: (127.5, 127.5)} # mean, std_dev
converter.default_ranges_stats = (0, 255)
 
tflite_uint8_model = converter.convert()
open("uint8.tflite", "wb").write(tflite_uint8_model)

4.2方法二：利用TOCO

toco  --graph_def_file 
./frozen_inference_graph.pb 
--output_file test.tflite 
--input_format=TENSORFLOW_GRAPHDEF 
--output_format=TFLITE 
--inference_type=QUANTIZED_UINT8 
--input_shape='1,300,300,3' --input_array='FeatureExtractor/MobilenetV2/MobilenetV2/input' --output_array='concat,concat_1' 
--std_dev_value 127.5 
--mean_value 127.5
--default_ranges_min 0 
--default_ranges_max 255

补充重点：uint8量化时的参数设置

01.由于是进行uint8量化，所以输出范围为[0,255]

即default_ranges_min =0，default_ranges_max=255

02.std_dev_value和mean_value参数

参考：https://www.cnblogs.com/sdu20112013/p/11960552.html

结论:
训练时模型的输入tensor的值在不同范围时,对应的mean_values,std_dev_values分别如下：

• range (0,255) then mean = 0, std_dev = 1
• range (-1,1) then mean = 127.5, std_dev = 127.5
• range (0,1) then mean = 0, std_dev = 255

我查看了我的输入tensor范围是[-1,1], 所以我设置参数为 mean = 127.5, std_dev = 127.5

 

5.tflite测试

在转换完成后，进行tflie解析测试，验证最后转换成功。

代码入下：

'''
code by zzg 2020-04-30
'''
import tensorflow as tf
import numpy as np
InputSize = 300
 
def test_tflite(input_test_tflite_file):
    interpreter = tf.lite.Interpreter(model_path = input_test_tflite_file)
    tensor_details = interpreter.get_tensor_details()
    for i in range(0,len(tensor_details)):
        # print("tensor:", i, tensor_details[i])
        interpreter.allocate_tensors()
 
    input_details = interpreter.get_input_details()
    print("=======================================")
    print("input :", str(input_details))
    output_details = interpreter.get_output_details()
    print("ouput :", str(output_details))
    print("=======================================")
    new_img = np.random.uniform(0,1,(1,InputSize,InputSize,3))
    # image_np_expanded = np.expand_dims(new_img, axis=0)
    new_img = new_img.astype('uint8')# 类型也要满足要求
 
    interpreter.set_tensor(input_details[0]['index'],new_img)
    # 注意注意，我要调用模型了
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    print("test_tflite finish!")
 
intput_tflite_file = "uint8.tflite"
test_tflite(intput_tflite_file)

最后显示如下：

 

补充：获取输入输出节点的话采用神经网络模型可视化工具Netron更加方便直观

参考：

模型结构可视化神器--Netron(支持tf, caffe, keras,mxnet等多种框架)​blog.csdn.net

轻量好用的神经网络模型可视化工具netron_网络_Mingyong_Zhuang的技术博客-CSDN博客​blog.csdn.net

 

安装比较简单:windows直接安装.exe文件，linux直接 pip install netron即可