TensorFlow将ckpt模型转为pb模型

「爱情、让人受尽委屈。」 2023-07-09 06:27 1阅读 0赞

# -*- coding: utf-8 -*-
    import tensorflow as tf
    from tensorflow.python.framework import graph_util
    import os
    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    os.environ['CUDA_VISIBLE_DEVICES'] = '1'
    import cv2
    import numpy as np
    
    
    def freeze_graph_test(pb_path, image_path):
        ''' :param pb_path:pb文件的路径 :param image_path:测试图片的路径 :return: '''
        with tf.Graph().as_default():
            output_graph_def = tf.GraphDef()
            with open(pb_path, "rb") as f:
                output_graph_def.ParseFromString(f.read())
                tf.import_graph_def(output_graph_def, name="")
            with tf.Session() as sess:
                sess.run(tf.global_variables_initializer())
    
                # 定义输入的张量名称,对应网络结构的输入张量
                # input:0作为输入图像,keep_prob:0作为dropout的参数,测试时值为1,is_training:0训练参数
                input_image_tensor = sess.graph.get_tensor_by_name("input:0")
    
                # 定义输出的张量名称
                output_tensor_name = sess.graph.get_tensor_by_name("Sigmoid:0")  # xuNet/Logits/logits
    
                # 读取测试图片
                im = np.reshape(cv2.imread(image_path, 0), (1, 512, 512, -1))
                # 测试读出来的模型是否正确，注意这里传入的是输出和输入节点的tensor的名字，不是操作节点的名字
                # out=sess.run("InceptionV3/Logits/SpatialSqueeze:0", feed_dict={'input:0': im,'keep_prob:0':1.0,'is_training:0':False})
                out = sess.run(output_tensor_name, feed_dict={ input_image_tensor: get_input(im)})
                print("out:{}".format(out))
                score = tf.nn.softmax(out, name='pre')
                class_id = tf.argmax(score, 1)
                pre_class = sess.run(class_id)
                print("pre class_id:{}".format(pre_class))
    
    
    def get_input(data):
        input = []
        for i in range(64):
            input.append(data)
        return np.reshape(input, (64, 1, 512, 512))
    
    
    def freeze_graph(input_checkpoint, output_graph):
        ''' :param input_checkpoint: :param output_graph: PB模型保存路径 :return: '''
        # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
        output_node_names = "Sigmoid"
        saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
        graph = tf.get_default_graph()  # 获得默认的图
        input_graph_def = graph.as_graph_def()  # 返回一个序列化的图代表当前的图
    
        with tf.Session() as sess:
            saver.restore(sess, input_checkpoint)  # 恢复图并得到数据
            node_names = [n.name for n in input_graph_def.node]
            print(node_names)
            with open('./nodes.txt', 'a+') as w:
                for node in node_names:
                    w.write(node + '\n')
    
            output_graph_def = graph_util.convert_variables_to_constants(  # 模型持久化，将变量值固定
                sess=sess,
                input_graph_def=input_graph_def,  # 等于:sess.graph_def
                output_node_names=output_node_names.split(","))  # 如果有多个输出节点，以逗号隔开
    
            with tf.gfile.GFile(output_graph, "wb") as f:  # 保存模型
                f.write(output_graph_def.SerializeToString())  # 序列化输出
            print("%d ops in the final graph." % len(output_graph_def.node))  # 得到当前图有几个操作节点
    
    
    if __name__ == '__main__':
        # 输入ckpt模型路径
        input_checkpoint = './model.ckpt-10000'
        # 输出pb模型的路径
        out_pb_path = "../model/frozen_model_10000.pb"
        # 调用freeze_graph将ckpt转为pb
        freeze_graph(input_checkpoint, out_pb_path)
        freeze_graph_test(out_pb_path, './1.pgm')

[reference1][]  
[reference2][]  
[reference3][]

[reference1]: https://blog.csdn.net/guyuealian/article/details/82218092
[reference2]: https://stackoverflow.com/questions/45864363/tensorflow-how-to-convert-meta-data-and-index-model-files-into-one-graph-pb
[reference3]: https://leimao.github.io/blog/Save-Load-Inference-From-TF-Frozen-Graph/