FCN—tensorflow版本代码超详解

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代码共有四个文件，分别如下：
FCN.py
vggnet函数：

# 根据载入的权重建立原始的 VGGNet 的网络
def vgg_net(weights, image):
layers = (
    'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
    'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
    'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
    'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
    'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4'
)
net = {}
current = image
for i, name in enumerate(layers):
    kind = name[:4]
    if kind == 'conv':
        kernels, bias = weights[i][0][0][0][0]
        # matconvnet: weights are [width, height, in_channels, out_channels]
        # tensorflow: weights are [height, width, in_channels, out_channels]
        kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w")  # k值转置
        bias = utils.get_variable(bias.reshape(-1), name=name + "_b")  # b转成一维
        current = utils.conv2d_basic(current, kernels, bias)  # 图像卷积完加b
        print("当前形状：", np.shape(current))
    elif kind == 'relu':
        current = tf.nn.relu(current, name=name)
        if debug:
            utils.add_activation_summary(current)
    elif kind == 'pool':
        current = utils.avg_pool_2x2(current) # 平均池化
        print("当前形状：", np.shape(current))
    net[name] = current
return net

inference函数：

 # FCN的网络结构定义，网络中用到的参数是迁移VGG训练好的参数
def inference(image, keep_prob):  # 输入图像和dropout值
    """
    Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
    """
    # 加载模型数据，获得标准化均值
    print("原始图像：", np.shape(image))
    model_data = utils.get_model_data(model_path)
    mean = model_data['normalization'][0][0][0]  # 通过字典获取mean值,vgg模型参数里有normaliza这个字典，三个0用来去虚维找到mean
    mean_pixel = np.mean(mean, axis=(0, 1))
    weights = np.squeeze(model_data['layers'])  # 从数组的形状中删除单维度条目，获得vgg权重

    # 图像预处理
    processed_image = utils.process_image(image, mean_pixel)  # 图像减平均值实现标准化
    print("预处理后的图像:", np.shape(processed_image))

    with tf.variable_scope("inference"):
        # 建立原始的VGGNet-19网络

        print("开始建立VGG网络：")
        image_net = vgg_net(weights, processed_image)

        # 在VGGNet-19之后添加 一个池化层和三个卷积层
        conv_final_layer = image_net["conv5_3"]  # 14*14*512
        print("VGG处理后的图像：", np.shape(conv_final_layer))

        pool5 = utils.max_pool_2x2(conv_final_layer)  # w，h/32 =7*7*512

        print("pool5：", np.shape(pool5))

        W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
        b6 = utils.bias_variable([4096], name="b6")
        conv6 = utils.conv2d_basic(pool5, W6, b6)  # 1*1*4096
        relu6 = tf.nn.relu(conv6, name="relu6")
        if debug:
            utils.add_activation_summary(relu6)
        relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)

        print("conv6:", np.shape(relu_dropout6))

        W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
        b7 = utils.bias_variable([4096], name="b7")
        conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)  # 1*1*4096
        relu7 = tf.nn.relu(conv7, name="relu7")
        if debug:
            utils.add_activation_summary(relu7)
        relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)

        print("conv7:", np.shape(relu_dropout7))

        W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
        b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
        conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)  # 第8层卷积层 分类2类 1*1*2

        print("conv8:", np.shape(conv8))
        # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")

        # 对卷积后的结果进行反卷积操作
        deconv_shape1 = image_net["pool4"].get_shape()  # 将pool4 即1/16结果尺寸拿出来 做融合 [b,h,w,c]
        W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")# 扩大两倍  所以stride = 2  kernel_size = 4
        b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
        conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))  #14*14*512
        fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")  # 将pool4和conv_t1拼接，逐像素相加

        print("pool4 and de_conv8 ==> fuse1:", np.shape(fuse_1))  # (14, 14, 512)

        deconv_shape2 = image_net["pool3"].get_shape()  # 获得pool3尺寸 是原图大小的1/8
        W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")# 输出通道数为pool3通道数，输入通道数为pool4通道数
        b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
        conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))# 将上一层融合结果fuse_1在扩大两倍，输出尺寸和pool3相同
        fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")

        print("pool3 and deconv_fuse1 ==> fuse2:", np.shape(fuse_2))  # (28, 28, 256)

        shape = tf.shape(image)  # 获得原始图像大小
        deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])  # 矩阵拼接
        W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
        b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
        conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)

        print("conv_t3:", [np.shape(image)[1], np.shape(image)[2], NUM_OF_CLASSESS])  # (224,224,2)

        annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")  # (224,224,1)目前理解是每个像素点所有通道取最大值

    return tf.expand_dims(annotation_pred, dim=3), conv_t3   # 从第三维度扩展形成[b,h,w,c] 其中c=1，即224*224*1*1

main主函数：

# 主函数
def main(argv=None):
    keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
    image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
    annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")

    print("setting up vgg initialized conv layers ...")

    # 定义好FCN的网络模型
    pred_annotation, logits = inference(image, keep_probability)
    # 定义损失函数，这里使用交叉熵的平均值作为损失函数
    loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                          labels=tf.squeeze(annotation,
                                                                                            squeeze_dims=[3]),
                                                                          name="entropy")))
    # 返回需要训练的变量列表
    trainable_var = tf.trainable_variables()

    if debug:
        for var in trainable_var:
            utils.add_to_regularization_and_summary(var)
    train_op = train(loss, trainable_var)

    # 加载数据集
    print("Setting up image reader...")

    train_records, valid_records = scene_parsing.read_dataset(data_dir, data_name)

    print("训练集的大小:", len(train_records))
    print("验证集的大小:", len(valid_records))

    print("Setting up dataset reader")
    
    image_options = {'resize': True, 'resize_size': IMAGE_SIZE}

    if mode == 'train':
        train_dataset_reader = dataset.BatchDatset(train_records, image_options)  # 读取图片 产生类对象 其中包含所有图片信息
    validation_dataset_reader = dataset.BatchDatset(valid_records, image_options)

    # 开始训练模型
    sess = tf.Session()
    print("Setting up Saver...")
    saver = tf.train.Saver()  # 保存模型类实例化

    sess.run(tf.global_variables_initializer())  # 变量初始化
    ckpt = tf.train.get_checkpoint_state(logs_dir)
    if ckpt and ckpt.model_checkpoint_path:  # 如果存在checkpoint文件 则恢复sess
        saver.restore(sess, ckpt.model_checkpoint_path)
        print("Model restored...")

    if mode == "train":
        for itr in xrange(MAX_ITERATION):
            train_images, train_annotations = train_dataset_reader.next_batch(batch_size)
            print(np.shape(train_images), np.shape(train_annotations))
            feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85}
            sess.run(train_op, feed_dict=feed_dict)
            print("step:", itr)

            if itr % 10 == 0:
                train_loss = sess.run(loss, feed_dict=feed_dict)
                print("Step: %d, Train_loss:%g" % (itr, train_loss))

            if itr % 500 == 0:
                valid_images, valid_annotations = validation_dataset_reader.next_batch(batch_size)
                valid_loss = sess.run(loss, feed_dict={image: valid_images, annotation: valid_annotations,
                                                       keep_probability: 1.0})
                print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))

                saver.save(sess, logs_dir + "model.ckpt", itr)  # 保存模型

    elif mode == "visualize":
        valid_images, valid_annotations = validation_dataset_reader.get_random_batch(batch_size)

        pred = sess.run(pred_annotation, feed_dict={image: valid_images, annotation: valid_annotations,  # 预测结果
                                                    keep_probability: 1.0})
        valid_annotations = np.squeeze(valid_annotations, axis=3)
        pred = np.squeeze(pred, axis=3)

        for itr in range(batch_size):
            utils.save_image(valid_images[itr].astype(np.uint8), logs_dir, name="inp_" + str(5 + itr))
            utils.save_image(valid_annotations[itr].astype(np.uint8), logs_dir, name="gt_" + str(5 + itr))
            utils.save_image(pred[itr].astype(np.uint8), logs_dir, name="pred_" + str(5 + itr))
            print("Saved image: %d" % itr)

    else:  # 测试模式
        since = time.time()  # 时间模块

        test_image = misc.imread('G:\\yuantu8.jpg')
        resize_image = misc.imresize(test_image, [224, 224], interp='nearest')
        a = np.expand_dims(resize_image, axis=0)
        a = np.array(a)

        pred = sess.run(pred_annotation, feed_dict={image: a, keep_probability: 1.0})  # 预测测试结果

        pred = np.squeeze(pred, axis=3)  # 从数组的形状中删除单维条目，即把shape中为1的维度去掉
        # utils.save_image(pred[0].astype(np.uint8), logs_dir, name="pred_" + str(5))
        utils.save_image(pred[0].astype(np.uint8),'G:/2/', name="pred_" + str(5))
        print("Saved image: succeed")

        time_elapsed = time.time() - since
        print('Training complete in {:.0f}m {:.0f}s'.format(
            time_elapsed // 60, time_elapsed % 60))  # 打印出来时间

BatchDatestreader.py

# coding=utf-8
import numpy as np
import scipy.misc as misc


# 批量读取数据集的类
class BatchDatset:
    files = []
    images = []
    annotations = []
    image_options = {}
    batch_offset = 0
    epochs_completed = 0

    def __init__(self, records_list, image_options={}):
        """
          Intialize a generic file reader with batching for list of files
        :param records_list: list of file records to read -
          sample record:
           {'image': f, 'annotation': annotation_file, 'filename': filename}
        :param image_options: A dictionary of options for modifying the output image
          Available options:
            resize = True/ False
            resize_size = #size of output image - does bilinear resize
            color=True/False
        """
        print("Initializing Batch Dataset Reader...")
        print(image_options)
        self.files = records_list
        self.image_options = image_options
        self._read_images()

    def _read_images(self):
        self.__channels = True

        # 读取训练集图像
        self.images = np.array([self._transform(filename['image']) for filename in self.files])
        self.__channels = False

        # 读取label的图像，由于label图像是二维的，这里需要扩展为三维
        self.annotations = np.array(
            [np.expand_dims(self._transform(filename['annotation']), axis=3) for filename in self.files])
        print(self.images.shape)
        print(self.annotations.shape)

    # 把图像转为 numpy数组
    def _transform(self, filename):
        image = misc.imread(filename)
        if self.__channels and len(image.shape) < 3:  # make sure images are of shape(h,w,3)
            image = np.array([image for i in range(3)])

        if self.image_options.get("resize", False) and self.image_options["resize"]:
            resize_size = int(self.image_options["resize_size"])
            resize_image = misc.imresize(image, [resize_size, resize_size], interp='nearest')  # 使用最近邻插值法resize图片
        else:
            resize_image = image

        return np.array(resize_image)

    def get_records(self):
        return self.images, self.annotations  # 返回图片和标签全路径

    def reset_batch_offset(self, offset=0):
        self.batch_offset = offset

    def next_batch(self, batch_size):
        start = self.batch_offset  # 当前第几个batch
        self.batch_offset += batch_size  # 读取下一个batch  所有offset偏移量+batch_size
        if self.batch_offset > self.images.shape[0]:  # 如果下一个batch的偏移量超过了图片总数说明完成了一个epoch
            # Finished epoch
            self.epochs_completed += 1  # epochs完成总数+1
            print("****************** Epochs completed: " + str(self.epochs_completed) + "******************")
            # Shuffle the data
            perm = np.arange(self.images.shape[0])  # arange生成数组(0 - len-1) 获取图片索引
            np.random.shuffle(perm)  # 对图片索引洗牌
            self.images = self.images[perm]  # 洗牌之后的图片顺序
            self.annotations = self.annotations[perm]
            # Start next epoch
            start = 0  # 下一个epoch从0开始
            self.batch_offset = batch_size  # 已完成的batch偏移量

        end = self.batch_offset   # 开始到结束self.batch_offset   self.batch_offset+batch_size
        return self.images[start:end], self.annotations[start:end]  # 取出batch

    def get_random_batch(self, batch_size):  # 按照一个batch_size一个块，进行对所有图片总数进行随机操作，相当于洗牌工作
        indexes = np.random.randint(0, self.images.shape[0], size=[batch_size]).tolist()
        return self.images[indexes], self.annotations[indexes]

read_MITSceneParsingData.py

# coding=utf-8
import numpy as np
import os
import random
from six.moves import cPickle as pickle
from tensorflow.python.platform import gfile
import glob

import TensorflowUtils as utils

# DATA_URL = 'http://sceneparsing.csail.mit.edu/data/ADEChallengeData2016.zip'
DATA_URL = 'http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip'


def read_dataset(data_dir, data_name):
    pickle_filename = "MITSceneParsing.pickle"
    pickle_filepath = os.path.join(data_dir, pickle_filename)

    if not os.path.exists(pickle_filepath):  # 不存在文件
        utils.maybe_download_and_extract(data_dir, DATA_URL, is_zipfile=True)  # 不存在文件 则下载
        SceneParsing_folder = os.path.splitext(DATA_URL.split("/")[-1])[0]  # ADEChallengeData2016
        result = create_image_lists(os.path.join(data_dir, data_name))
        print("Pickling ...")
        with open(pickle_filepath, 'wb') as f:
            pickle.dump(result, f, pickle.HIGHEST_PROTOCOL)
    else:
        print("Found pickle file!")

    with open(pickle_filepath, 'rb') as f:  # 打开pickle文件
        result = pickle.load(f)
        training_records = result['training']
        validation_records = result['validation']
        del result

    return training_records, validation_records


'''
  返回一个字典:
  image_list{ 
           "training":[{'image': image_full_name, 'annotation': annotation_file, 'image_filename': },......],
           "validation":[{'image': image_full_name, 'annotation': annotation_file, 'filename': filename},......]
           }
'''


def create_image_lists(image_dir):
    if not gfile.Exists(image_dir):
        print("Image directory '" + image_dir + "' not found.")
        return None
    directories = ['training', 'validation']
    image_list = {}

    for directory in directories:  # 训练集和验证集 分别制作
        file_list = []
        image_list[directory] = []

        # 获取images目录下所有的图片名
        file_glob = os.path.join(image_dir, "images", directory, '*.' + 'jpg')
        file_list.extend(glob.glob(file_glob))  # 加入文件列表  包含所有图片文件全路径+文件名字  如 Data_zoo/MIT_SceneParsing/ADEChallengeData2016/images/training/hi.jpg

        if not file_list:
            print('No files found')
        else:
            for f in file_list:  # 扫描文件列表   这里f对应文件全路径
                # 注意注意，下面的分割符号，在window上为：\\,在Linux撒花姑娘为 : /
                filename = os.path.splitext(f.split("\\")[-1])[0]  # 图片名前缀
                annotation_file = os.path.join(image_dir, "annotations", directory, filename + '.png')
                if os.path.exists(annotation_file):
                    record = {'image': f, 'annotation': annotation_file, 'filename': filename}#  image:图片全路径， annotation:标签全路径， filename:图片名字
                    image_list[directory].append(record)
                else:
                    print("Annotation file not found for %s - Skipping" % filename)

        random.shuffle(image_list[directory])  # 对图片列表进行洗牌
        no_of_images = len(image_list[directory])  # 包含图片文件的个数
        print('No. of %s files: %d' % (directory, no_of_images))

    return image_list

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