tensorflow模型转换成tensorflow lite模型

1、转换mobilenet_v1_1.0_224模型

之前实践过，但是由于长时间没做，当时也没写笔记所以后续也浪费了一点时间

对应的google已经训练好的模型可以在这里下载

https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md

其中frozen_graph的输入文件使用到的有mobilenet_v1_1.0_224.ckpt.*+mobilenet_v1_1.0_224_eval.pbtxt

使用的命令如下：

freeze_graph

--input_graph=C:\Users\judy.yuan\_bazel_judy.yuan\i7fa2ce7\execroot\org_tensorflow\bazel-out\x64_windows-opt\bin\tensorflow\lite\toco\test\mobilenet_v1_1.0_224_eval.pbtxt

--input_checkpoint=C:\Users\judy.yuan\_bazel_judy.yuan\i7fa2ce7\execroot\org_tensorflow\bazel-out\x64_windows-opt\bin\tensorflow\lite\toco\test\mobilenet_v1_1.0_224.ckpt

--output_graph=C:\Users\judy.yuan\_bazel_judy.yuan\i7fa2ce7\execroot\org_tensorflow\bazel-out\x64_windows-opt\bin\tensorflow\lite\toco\test\mobilenet_v1_1.0_224_frozen_judy.pb

--output_node_names=MobilenetV1/Predictions/Reshape_1

执行该命令之后会生成frozen的pb文件

 

生成冻图之后需要的是生成tflite的文件

toco

--input_file=C:\Users\hui.yuan\_bazel_judy.yuan\i7fa2ce7\execroot\org_tensorflow\bazel-out\x64_windows-opt\bin\tensorflow\lite\toco\test\mobilenet_v1_1.0_224_frozen_judy.pb

--output_file=C:\Users\hui.yuan\_bazel_judy.yuan\i7fa2ce7\execroot\org_tensorflow\bazel-out\x64_windows-opt\bin\tensorflow\lite\toco\test\mobilenet_v1_1.0_224_frozen_judy.tflite

--input_shape="1,224, 224,3"

--input_array=input

--output_array=MobilenetV1/Predictions/Reshape_1

2、转换自己训练的module

第一种方法是直接在toco cmd

toco --input_file=****_frozen.pb --output_file=****.tflite --input_shape="1,49" --input_array=inputs/input --output_array=layer5/logits

执行该命令一定需要在toco应用程序所在目录

还有一种方法目前正在尝试

import tensorflow as tf
convert=tf.lite.TFLiteConverter.from_frozen_graph("model_proc_mobile_fps.pb",input_arrays=["inputs/input"],output_arrays=["layer5/logits"],
                                                  input_shapes={"inputs/input":[1,49]})
convert.post_training_quantize=False
tflite_model=convert.convert()
open("quantized_model.tflite","wb").write(tflite_model)

其中对应的tensorflow的版本为1.13.1

 

进行toco转换的时候需要输入--input_array= 和 --output_array= 这些信息可以由下面这个脚本得出

    gf = tf.GraphDef()
    gf.ParseFromString(open('save/model.pb','rb').read())
    for n in gf.node:
        print ( n.name +' ===> '+n.op )

实例

import tensorflow as tf
import numpy as np
from tensorflow.python.framework import graph_util

with tf.Session(graph=tf.Graph()) as sess:
    # 使用 NumPy 生成假数据(phony data), 总共 100 个点.
    with tf.name_scope("input"):
        x = tf.placeholder(tf.float32, [1, 10], name='input0')
    
    
    x_data = np.float32(np.random.rand(1, 10)) # 随机输入
    print(x_data)
    
    # 构造一个线性模型
    # 
    with tf.name_scope('bias'):
        b = tf.Variable(tf.zeros([1]), name='b')
        print(b)
    with tf.name_scope('weight'):
        W = tf.Variable(tf.random_uniform([1, 1], -1.0, 1.0), name='weight')
        print(W)
    with tf.name_scope('output'):
        y = tf.matmul(W,x) + b
        print(y)
    
    """
    # 最小化方差
    with tf.name_scope('mean'):
        loss = tf.reduce_mean(tf.square(y))
        print("loss")
        print(loss)
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    print("optimizer")
    print(optimizer)
    train = optimizer.minimize(loss)
    print("train")
    print(train)
    """
    
    # 初始化变量
    init = tf.initialize_all_variables()
    
    # 启动图 (graph)
    sess.run(init)
    
    # 拟合平面
    for step in range(0, 201):
        
        #sess.run(train, feed_dict)
        if step % 20 == 0:
            print(step, sess.run(W), sess.run(b))
    input_x = np.float32([[1,2,0,0,0,0,0,0,0,0]])
    feed_dict = {x: input_x}
    print(sess.run(y, feed_dict))
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['output/add'])
    
    saver = tf.train.Saver()
    model_path = "kk/model.ckpt"
    save_path = saver.save(sess, model_path)
    
    with tf.gfile.GFile('kk/model.pb', mode='wb') as f: #模型的名字是model.pb
        f.write(constant_graph.SerializeToString())
    
    gf = tf.compat.v1.GraphDef()
    gf.ParseFromString(open('kk/model.pb','rb').read())
    print("\n\n\n")
    for n in gf.node:
        print ( n.name +' ===> '+n.op )
    
    
    convert=tf.lite.TFLiteConverter.from_frozen_graph("kk/model.pb",input_arrays=["input/input0"],output_arrays=["output/add"],
                                                  input_shapes={"input/input0":[1,10]})
    convert.post_training_quantize=False
    tflite_model=convert.convert()
    open("kk/model.tflite","wb").write(tflite_model)
    

输入是10组数据，输出也是10组数据

1, 2, 0, 0, 0, 0, 0, 0, 0, 0],

放在手机中解析后，使用模型推理出来的结果如下：

Loaded model model.tflite
resolved reporter
num 0batch 1
invoked
average time: 0.011 ms
Inference output 0 value is -0.0405481
Inference output 1 value is -0.0810962
Inference output 2 value is 0
Inference output 3 value is 0
Inference output 4 value is 0
Inference output 5 value is 0
Inference output 6 value is 0
Inference output 7 value is 0
Inference output 8 value is 0
Inference output 9 value is 0
grade(0-4), Inference grade is :2
num 1batch 1