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Author: yuqJin

Diff for: Resnet/README.md

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+# tensorflow_models_learning
+> 老铁要是觉得不错，给个“star”
+## 1.生成record训练数据
+dataset已经包含了训练和测试的图片，请直接运行create_tf_record.py</br>
+> 对于InceptionNet V1:设置resize_height和resize_width = 224 </br>
+> 对于InceptionNet V3:设置resize_height和resize_width = 299 </br>
+> 其他模型，请根据输入需要设置resize_height和resize_width的大小</br>
+
+```
+if __name__ == '__main__':
+    # 参数设置
+    resize_height = 224  # 指定存储图片高度
+    resize_width = 224  # 指定存储图片宽度
+    shuffle=True
+    log=5
+    # 产生train.record文件
+    image_dir='dataset/train'
+    train_labels = 'dataset/train.txt'  # 图片路径
+    train_record_output = 'dataset/record/train{}.tfrecords'.format(resize_height)
+    create_records(image_dir,train_labels, train_record_output, resize_height, resize_width,shuffle,log)
+    train_nums=get_example_nums(train_record_output)
+    print("save train example nums={}".format(train_nums))
+
+    # 产生val.record文件
+    image_dir='dataset/val'
+    val_labels = 'dataset/val.txt'  # 图片路径
+    val_record_output = 'dataset/record/val{}.tfrecords'.format(resize_height)
+    create_records(image_dir,val_labels, val_record_output, resize_height, resize_width,shuffle,log)
+    val_nums=get_example_nums(val_record_output)
+    print("save val example nums={}".format(val_nums))
+
+    # 测试显示函数
+    # disp_records(train_record_output,resize_height, resize_width)
+    batch_test(train_record_output,resize_height, resize_width)
+
+```
+## 2.训练过程
+目前提供VGG、inception_v1、inception_v3、mobilenet_v以及resnet_v1的训练文件，只需要生成tfrecord数据，即可开始训练
+> 训练VGG请直接运行：vgg_train_val.py </br>
+> 训练inception_v1请直接运行：inception_v1_train_val.py </br>
+> 训练inception_v3请直接运行：inception_v3_train_val.py </br>
+> 训练mobilenet_v1请直接运行：mobilenet_train_val.py </br>
+> 其他模型，请参考训练文件进行修改</br>
+
+## 3.资源下载
+- 本项目详细说明，请参考鄙人博客资料：
+> 《使用自己的数据集训练GoogLenet InceptionNet V1 V2 V3模型》: https://panjinquan.blog.csdn.net/article/details/81560537 </br>
+> 《tensorflow实现将ckpt转pb文件》: https://panjinquan.blog.csdn.net/article/details/82218092 </br>
+> 《使用自己的数据集训练MobileNet、ResNet实现图像分类（TensorFlow）》https://panjinquan.blog.csdn.net/article/details/88252699
+> 预训练模型下载地址: https://download.csdn.net/download/guyuealian/10610847  </br>
+- 老铁要是觉得不错，给个“star”
+- tensorflow-gpu==1.4.0

Diff for: Resnet/convert_pb.py

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+# -*-coding: utf-8 -*-
+"""
+    @Project: tensorflow_models_nets
+    @File   : convert_pb.py
+    @Author : panjq
+    @E-mail : [email protected]
+    @Date   : 2018-08-29 17:46:50
+    @info   :
+    -通过传入 CKPT 模型的路径得到模型的图和变量数据
+    -通过 import_meta_graph 导入模型中的图
+    -通过 saver.restore 从模型中恢复图中各个变量的数据
+    -通过 graph_util.convert_variables_to_constants 将模型持久化
+"""
+
+import tensorflow as tf
+from create_tf_record import *
+from tensorflow.python.framework import graph_util
+
+resize_height = 299  # 指定图片高度
+resize_width = 299  # 指定图片宽度
+depths = 3
+
+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")
+            input_keep_prob_tensor = sess.graph.get_tensor_by_name("keep_prob:0")
+            input_is_training_tensor = sess.graph.get_tensor_by_name("is_training:0")
+
+            # 定义输出的张量名称
+            output_tensor_name = sess.graph.get_tensor_by_name("InceptionV3/Logits/SpatialSqueeze:0")
+
+            # 读取测试图片
+            im=read_image(image_path,resize_height,resize_width,normalization=True)
+            im=im[np.newaxis,:]
+            # 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: im,
+                                                        input_keep_prob_tensor:1.0,
+                                                        input_is_training_tensor:False})
+            print("out:{}".format(out))
+            score = tf.nn.softmax(out, name='pre')
+            class_id = tf.argmax(score, 1)
+            print("pre class_id:{}".format(sess.run(class_id)))
+
+
+def freeze_graph(input_checkpoint,output_graph):
+    '''
+
+    :param input_checkpoint:
+    :param output_graph: PB模型保存路径
+    :return:
+    '''
+    # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
+    # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
+
+    # 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 = "InceptionV3/Logits/SpatialSqueeze"
+    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
+
+    with tf.Session() as sess:
+        saver.restore(sess, input_checkpoint) #恢复图并得到数据
+        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=sess.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
+
+        # for op in sess.graph.get_operations():
+        #     print(op.name, op.values())
+
+def freeze_graph2(input_checkpoint,output_graph):
+    '''
+
+    :param input_checkpoint:
+    :param output_graph: PB模型保存路径
+    :return:
+    '''
+    # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
+    # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
+
+    # 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 = "InceptionV3/Logits/SpatialSqueeze"
+    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
+        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
+
+        # for op in graph.get_operations():
+        #     print(op.name, op.values())
+
+
+if __name__ == '__main__':
+    # 输入ckpt模型路径
+    input_checkpoint='models/model.ckpt-10000'
+    # 输出pb模型的路径
+    out_pb_path="models/pb/frozen_model.pb"
+    # 调用freeze_graph将ckpt转为pb
+    freeze_graph(input_checkpoint,out_pb_path)
+
+    # 测试pb模型
+    image_path = 'test_image/animal.jpg'
+    freeze_graph_test(pb_path=out_pb_path, image_path=image_path)

Diff for: Resnet/create_labels_files.py

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+# -*-coding:utf-8-*-
+"""
+    @Project: googlenet_classification
+    @File   : create_labels_files.py
+    @Author : panjq
+    @E-mail : [email protected]
+    @Date   : 2018-08-11 10:15:28
+"""
+
+import os
+import os.path
+
+
+def write_txt(content, filename, mode='w'):
+    """保存txt数据
+    :param content:需要保存的数据,type->list
+    :param filename:文件名
+    :param mode:读写模式:'w' or 'a'
+    :return: void
+    """
+    with open(filename, mode) as f:
+        for line in content:
+            str_line = ""
+            for col, data in enumerate(line):
+                if not col == len(line) - 1:
+                    # 以空格作为分隔符
+                    str_line = str_line + str(data) + " "
+                else:
+                    # 每行最后一个数据用换行符“\n”
+                    str_line = str_line + str(data) + "\n"
+            f.write(str_line)
+
+
+def get_files_list(dir):
+    '''
+    实现遍历dir目录下,所有文件(包含子文件夹的文件)
+    :param dir:指定文件夹目录
+    :return:包含所有文件的列表->list
+    '''
+    # parent:父目录, filenames:该目录下所有文件夹,filenames:该目录下的文件名
+    files_list = []
+    for parent, dirnames, filenames in os.walk(dir):
+        for filename in filenames:
+            # print("parent is: " + parent)
+            # print("filename is: " + filename)
+            # print(os.path.join(parent, filename))  # 输出rootdir路径下所有文件（包含子文件）信息
+            curr_file = parent.split(os.sep)[-1]
+            if curr_file == 'flower':
+                labels = 0
+            elif curr_file == 'guitar':
+                labels = 1
+            elif curr_file == 'animal':
+                labels = 2
+            elif curr_file == 'houses':
+                labels = 3
+            elif curr_file == 'plane':
+                labels = 4
+            files_list.append([os.path.join(curr_file, filename), labels])
+    return files_list
+
+
+if __name__ == '__main__':
+    train_dir = 'dataset/train'
+    train_txt = 'dataset/train.txt'
+    train_data = get_files_list(train_dir)
+    write_txt(train_data, train_txt, mode='w')
+
+    val_dir = 'dataset/val'
+    val_txt = 'dataset/val.txt'
+    val_data = get_files_list(val_dir)
+    write_txt(val_data, val_txt, mode='w')
+