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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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