from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import csv
import os
import glob
import random
import collections
import math
import time
train_input_sample = "D:/test13/Sample16_new_revenge1/" # training data set dir
train_input_label = "D:/test13/Label16_new_revenge1/"
train_input_sample2 = "D:/test13/VS16_new_revenge1/" # validation data set dir
train_input_label2 = "D:/test13/VL16_new_revenge1/"
test_input_dir = "D:/result_contract/Otherkind/Tongue/20191119/testinput/"
test_input_label = "D:/result_contract/Otherkind/Tongue/20191119/testlabel"
test_output_dir = "D:/result_contract/Otherkind/Tongue/20191119/testoutput/"
train_output = "D:/Result_RE_Revenge109/training" # training images output dir
validation_output = "D:/Result_RE_Revenge109/validation/" # validation images output dir
checkpoint = "D:/Result_RE_Revenge109_checkpoint/" # model saving dir
# 创建目录
if not os.path.exists(train_output):
os.makedirs(train_output)
if not os.path.exists(validation_output):
os.makedirs(validation_output)
if not os.path.exists(checkpoint):
os.makedirs(checkpoint)
if not os.path.exists(test_output_dir):
os.makedirs(test_output_dir)
log = open(r'D:/Result_RE_Revenge109_checkpoint/log.txt', 'a') # log saving dir
loss_csv = open(r'D:/Result_RE_Revenge109_checkpoint/loss_csv.csv', 'a', newline='') # loss value saving dir
seed = None
max_steps = 20000 # number of training steps (0 to disable)
max_epochs = None # number of training epochs
progress_freq = 50 # display progress every progress_freq steps
trace_freq = 0 # trace execution every trace_freq steps
display_freq = 50 # write current training images every display_freq steps
validation_freq = 50
save_freq = None # save model every save_freq steps, 0 to disable
separable_conv = False # use separable convolutions in the generator
aspect_ratio = 1.0 # aspect ratio of output images (width/height)
batch_size = 2 # help="number of images in batch")
which_direction = "AtoB" # choices=["AtoB", "BtoA"])
ngf = 64 # help="number of generator filters in first conv layer")
ndf = 64 # help="number of discriminator filters in first conv layer")
scale_size = 256 # help="scale images to this size before cropping to 256x256")
validation_size = 256
flip = False # flip images horizontally
brightness = False
contrast = False
# hue = False
# saturation = False
gamma = False
lr = 0.0001 # initial learning rate for adam
beta1 = 0.5 # momentum term of adam
l1_weight = 100.0 # weight on L1 term for generator gradient
gan_weight = 1.0 # weight on GAN term for generator gradient
EPS = 1e-12 # Very small number, preventing gradient loss to 0
CROP_SIZE = 256 # Crop size of the image
# Named tuples for storing loaded data collections to create good models
Examples = collections.namedtuple("Examples", "paths, inputs, targets, count, steps_per_epoch")
# Model = collections.namedtuple("Model", "outputs, predict_real, predict_fake, discrim_loss,"
# "discrim_grads_and_vars, gen_loss_GAN, gen_loss_L1,"
# "gen_grads_and_vars, train, validate")
Model = collections.namedtuple("Model", "outputs, gen_loss_L1,"
"gen_grads_and_vars, train, validate")
# Image preprocessing [0, 1] => [-1, 1]
# def preprocess(image):
# with tf.name_scope("preprocess"):
# return image * 2 - 1
# Image post processing [-1, 1] => [0, 1]
# def deprocess(image):
# with tf.name_scope("deprocess"):
# return (image + 1) / 2
# 判别器的卷积定义，batch_input为 [ batch , 256 , 256 , 6 ]
# def discrim_conv(batch_input, out_channels, stride):
# # [ batch , 256 , 256 , 6 ] ===>[ batch , 258 , 258 , 6 ]
# padded_input = tf.pad(batch_input, [[0, 0], [1, 1], [1, 1], [0, 0]], mode="CONSTANT")
# '''
# [0,0]: 第一维batch大小不扩充
# [1,1]：第二维图像宽度左右各扩充一列，用0填充
# [1,1]：第三维图像高度上下各扩充一列，用0填充
# [0,0]：第四维图像通道不做扩充
# '''
# return tf.layers.conv2d(padded_input, out_channels, kernel_size=4, strides=(stride, stride), padding="valid",
# kernel_initializer=tf.random_normal_initializer(0, 0.02))
# 生成器的卷积定义，卷积核为4*4，步长为2，输出图像为输入的一半
def gen_conv(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
return tf.layers.separable_conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
return tf.layers.conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding="same",
kernel_initializer=initializer)
def gen_conv2(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
return tf.layers.separable_conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
out_put = tf.layers.conv2d(batch_input, out_channels, kernel_size=3, strides=(1, 1), padding="same",
kernel_initializer=initializer)
out_put2 = tf.layers.conv2d(out_put, out_channels, kernel_size=3, strides=(1, 1), padding="same",
kernel_initializer=initializer)
out_put3 = tf.layers.max_pooling2d(inputs=out_put2, pool_size=[2, 2], strides=2, padding="same")
return out_put3
def gen_conv3(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
return tf.layers.separable_conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
out_put = tf.layers.conv2d(batch_input, out_channels, kernel_size=3, strides=(1, 1), padding="same",
kernel_initializer=initializer)
out_put2 = tf.layers.conv2d(out_put, out_channels, kernel_size=3, strides=(1, 1), padding="same")
return out_put2
# 生成器的反卷积定义
def gen_deconv(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
_b, h, w, _c = batch_input.shape
resized_input = tf.image.resize_images(batch_input, [h * 2, w * 2],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return tf.layers.separable_conv2d(resized_input, out_channels, kernel_size=4, strides=(1, 1), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
return tf.layers.conv2d_transpose(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding="same",
kernel_initializer=initializer)
def gen_deconv2(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
_b, h, w, _c = batch_input.shape
resized_input = tf.image.resize_images(batch_input, [h * 2, w * 2],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return tf.layers.separable_conv2d(resized_input, out_channels, kernel_size=4, strides=(1, 1), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
out_put = tf.layers.conv2d_transpose(batch_input, out_channels, kernel_size=3, strides=(2, 2), padding="same",
kernel_initializer=initializer)
out_put2 = tf.layers.conv2d(out_put, out_channels, kernel_size=3, strides=(1, 1), padding="same",
kernel_initializer=initializer)
out_put3 = tf.layers.conv2d(out_put2, out_channels, kernel_size=3, strides=(1, 1), padding="same")
return out_put3
def gen_deconv3(batch_input, out_channels):
# [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]
initializer = tf.random_normal_initializer(0, 0.02)
if separable_conv:
_b, h, w, _c = batch_input.shape
resized_input = tf.image.resize_images(batch_input, [h * 2, w * 2],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return tf.layers.separable_conv2d(resized_input, out_channels, kernel_size=4, strides=(1, 1), padding="same",
depthwise_initializer=initializer, pointwise_initializer=initializer)
else:
out_put4 = tf.layers.conv2d(batch_input, out_channels, kernel_size=1, strides=(1, 1), padding="same",
kernel_initializer=initializer)
return out_put4
# 定义LReLu激活函数
def lrelu(x, a):
with tf.name_scope("lrelu"):
# adding these together creates the leak part and linear part
# then cancels them out by subtracting/adding an absolute value term
# leak: a*x/2 - a*abs(x)/2
# linear: x/2 + abs(x)/2
# this block looks like it has 2 inputs on the graph unless we do this
x = tf.identity(x)
return (0.5 * (1 + a)) * x + (0.5 * (1 - a)) * tf.abs(x)
# 批量归一化图像
def batchnorm(inputs):
return tf.layers.batch_normalization(inputs, axis=3, epsilon=1e-5, momentum=0.1, training=True,
gamma_initializer=tf.random_normal_initializer(1.0, 0.02))
# 检查图像的维度
def check_image(image):
assertion = tf.assert_equal(tf.shape(image)[-1], 3, message="image must have 3 color channels")
with tf.control_dependencies([assertion]):
image = tf.identity(image)
if image.get_shape().ndims not in (3, 4):
raise ValueError("image must be either 3 or 4 dimensions")
# make the last dimension 3 so that you can unstack the colors
shape = list(image.get_shape())
shape[-1] = 3
image.set_shape(shape)
return image
# 去除文件的后缀，获取文件名
def get_name(path):
# os.path.basename(),返回path最后的文件名。若path以/或\结尾，那么就会返回空值。
# os.path.splitext(),分离文件名与扩展名；默认返回(fname,fextension)元组
name, _ = os.path.splitext(os.path.basename(path))
return name
# 加载数据集，从文件读取-->解码-->归一化--->拆分为输入和目标-->像素转为[-1,1]-->转变形状
def load_examples(input_sample, input_label, shuffle, trans):
if input_sample is None or not os.path.exists(input_sample):
raise Exception("input_dir does not exist")
if input_label is None or not os.path.exists(input_label):
raise Exception("input_dir2 does not exist")
# 匹配第一个参数的路径中所有的符合条件的文件，并将其以list的形式返回。
input_path_sample = glob.glob(os.path.join(input_sample, "*.jpg"))
input_path_label = glob.glob(os.path.join(input_label, "*.jpg"))
# 图像解码器
decode = tf.image.decode_jpeg
if len(input_path_sample) == 0:
raise Exception("input_sample contains no image files")
if len(input_path_label) == 0:
raise Exception("input_label contains no image files")
# 如果文件名是数字，则用数字进行排序，否则用字母排序
if all(get_name(path).isdigit() for path in input_path_sample):
input_path_sample = sorted(input_path_sample, key=lambda path: int(get_name(path)))
else:
input_path_sample = sorted(input_path_sample)
if all(get_name(path).isdigit() for path in input_path_label):
input_path_label = sorted(input_path_label, key=lambda path: int(get_name(path)))
else:
input_path_label = sorted(input_path_label)
# sess = tf.Session()
with tf.name_scope("load_images"):
# 把我们需要的全部文件打包为一个tf内部的queue类型，之后tf开文件就从这个queue中取目录了，
# 如果是训练模式时，shuffle为True
if shuffle == 1:
path_queue = tf.train.slice_input_producer([input_path_sample, input_path_label], shuffle=True)
if shuffle == 0:
path_queue = tf.train.slice_input_producer([input_path_sample, input_path_label], shuffle=False)
# Read的输出将是一个文件名（key）和该文件的内容（value,每次读取一个文件，分多次读取）。
# reader = tf.WholeFileReader()
paths = input_path_sample
samples = tf.read_file(path_queue[0])
labels = tf.read_file(path_queue[1])
# 对文件进行解码并且对图片作归一化处理
raw_input_sample = decode(samples)
raw_input_label = decode(labels)
raw_input_sample = tf.image.convert_image_dtype(raw_input_sample, dtype=tf.float32) # 归一化处理
raw_input_label = tf.image.convert_image_dtype(raw_input_label, dtype=tf.float32)
# 判断两个值知否相等，如果不等抛出异常
assertion_sample = tf.assert_equal(tf.shape(raw_input_sample)[2], 3, message="image does not have 3 channels")
assertion_label = tf.assert_equal(tf.shape(raw_input_label)[2], 3, message="image2 does not have 3 channels")
'''
对于control_dependencies这个管理器，只有当里面的操作是一个op时，才会生效，也就是先执行传入的
参数op，再执行里面的op。如果里面的操作不是定义的op，图中就不会形成一个节点，这样该管理器就失效了。
tf.identity是返回一个一模一样新的tensor的op，这会增加一个新节点到gragh中，这时control_dependencies就会生效.
'''
with tf.control_dependencies([assertion_sample]):
raw_input_sample = tf.identity(raw_input_sample)
raw_input_sample.set_shape([None, None, 3])
with tf.control_dependencies([assertion_label]):
raw_input_label = tf.identity(raw_input_label)
raw_input_label.set_shape([None, None, 3])
# 图像值由[0,1]--->[-1, 1]
# width = tf.shape(raw_input)[1] # [height, width, channels]
# a_images = preprocess(raw_input_sample)
# b_images = preprocess(raw_input_label)
a_images = raw_input_sample
b_images = raw_input_label
# 这里的which_direction为：BtoA
if which_direction == "AtoB":
inputs, targets = [a_images, b_images]
elif which_direction == "BtoA":
inputs, targets = [b_images, a_images]
else:
raise Exception("invalid direction")
# synchronize seed for image operations so that we do the same operations to both
# input and output images
# seed = random.randint(0, 2 ** 31 - 1)
# 图像预处理，翻转、改变形状
with tf.name_scope("input_images"):
if trans == 0:
input_images = notransform(inputs)
if trans == 1:
inputs = transform(inputs)
input_images = transform2(inputs)
# input_images = random_erasing(inputs)
# input_images.set_shape([256, 256, 3])
with tf.name_scope("target_images"):
if trans == 0:
target_images = notransform(targets)
if trans == 1:
target_images = transform(targets)
# 获得输入图像、目标图像的batch块
paths_batch, inputs_batch, targets_batch = tf.train.batch([paths, input_images, target_images],
batch_size=batch_size)
steps_per_epoch = int(math.ceil(len(input_path_sample) / batch_size))
return Examples(
paths=paths_batch, # 输入的文件名块
inputs=inputs_batch, # 输入的图像块
targets=targets_batch, # 目标图像块
count=len(input_path_sample), # 数据集的大小
steps_per_epoch=steps_per_epoch, # batch的个数
)
def random_erasing(img, probability=0.5, sl=0.005, sh=0.05, r1=0.5):
height = tf.shape(img)[0]
width = tf.shape(img)[1]
channel = tf.shape(img)[2]
area = tf.cast(width*height, tf.float32)
erase_area_low_bound = tf.cast(tf.round(tf.sqrt(sl * area * r1)), tf.int32)
erase_area_up_bound = tf.cast(tf.round(tf.sqrt((sh * area) / r1)), tf.int32)
h_upper_bound = tf.minimum(erase_area_up_bound, height)
w_upper_bound = tf.minimum(erase_area_up_bound, width)
h = tf.random_uniform([], erase_area_low_bound, h_upper_bound, tf.int32)
w = tf.random_uniform([], erase_area_low_bound, w_upper_bound, tf.int32)
erase_area = tf.cast(tf.random_uniform([h, w, channel], -255, 255, tf.int32), tf.float32)
erase_area_img = tf.image.resize_image_with_crop_or_pad(erase_area, 256, 256)
erase_area_img = tf.image.resize_images(erase_area_img, [512, 512], method=tf.image.ResizeMethod.BILINEAR)
# offset1 = tf.cast(tf.floor(tf.random_uniform([2], 0, 512 - 384 + 1, seed=seed)), dtype=tf.int32)
# offset2 = tf.cast(tf.floor(tf.random_uniform([2], 0, 512 - 256 + 1, seed=seed)), dtype=tf.int32)
# offset1 = tf.cast(tf.floor(tf.random_uniform([2], 512 - 320, 512 - 256 + 1, seed=seed)), dtype=tf.int32)
# offset2 = tf.cast(tf.floor(tf.random_uniform([2], 0, 512 - 256 + 1, seed=seed)), dtype=tf.int32)
offset1 = tf.cast(tf.floor(tf.random_uniform([2], 0, 512 - 256 + 1, seed=seed)), dtype=tf.int32)
offset2 = tf.cast(tf.floor(tf.random_uniform([2], 0, 512 - 256 + 1, seed=seed)), dtype=tf.int32)
erase_area_img = tf.image.crop_to_bounding_box(erase_area_img, offset1[0], offset2[1], 256, 256)
img_v = img + erase_area_img
return tf.cond(tf.random_uniform([], 0, 1) > probability, lambda: img, lambda: img_v)
# x1 = tf.random_uniform([], 0, height+1 - h, tf.int32)
# y1 = tf.random_uniform([], 0, width+1 - w, tf.int32)
#
# erase_area = tf.cast(tf.random.uniform([h, w, channel], 0, 255, tf.int32), tf.uint8)
#
# erasing_img = img[x1:x1 + h, y1:y1 + w, :].assign(erase_area)
#
# return tf.cond(tf.random.uniform([], 0, 1) > probability, lambda: img, lambda: erasing_img)
# 图像预处理，翻转、改变形状
def transform(image):
r = image
if flip:
r = tf.image.random_flip_left_right(r, seed=seed)
# r = tf.image.random_flip_up_down(r, seed=seed)
# area produces a nice downscaling, but does nearest neighbor for upscaling
# assume we're going to be doing downscaling here
r = tf.image.resize_images(r, [scale_size, scale_size], method=tf.image.ResizeMethod.AREA)
offset = tf.cast(tf.floor(tf.random_uniform([2], 0, scale_size - CROP_SIZE + 1, seed=seed)), dtype=tf.int32)
if scale_size > CROP_SIZE:
r = tf.image.crop_to_bounding_box(r, offset[0], offset[1], CROP_SIZE, CROP_SIZE)
elif scale_size < CROP_SIZE:
raise Exception("scale size cannot be less than crop size")
return r
# 图像预处理
def transform2(image):
r = image
r1 = image
r2 = image
r3 = image
r4 = image
r5 = image
r6 = image
a = random.uniform(1, 2)
# b = random.uniform(0.5, 1.5)
b = 1
if brightness:
r1 = tf.image.random_brightness(r1, max_delta=0.2)
if contrast:
r1 = tf.image.random_contrast(r1, lower=0.5, upper=1.5)
if gamma:
r1 = tf.image.adjust_gamma(r1, gain=a, gamma=b)
# if saturation:
# r1 = tf.image.random_saturation(r1, lower=1, upper=2)
# if hue:
# r1 = tf.image.random_hue(r1, max_delta=0.5)
if brightness:
r2 = tf.image.random_brightness(r2, max_delta=0.2)
if gamma:
r2 = tf.image.adjust_gamma(r2, gain=a, gamma=b)
if contrast:
r2 = tf.image.random_contrast(r2, lower=0.5, upper=1.5)
if contrast:
r3 = tf.image.random_contrast(r3, lower=0.5, upper=1.5)
if brightness:
r3 = tf.image.random_brightness(r3, max_delta=0.2)
if gamma:
r3 = tf.image.adjust_gamma(r3, gain=a, gamma=b)
if contrast:
r4 = tf.image.random_contrast(r4, lower=0.5, upper=1.5)
if gamma:
r4 = tf.image.adjust_gamma(r4, gain=a, gamma=b)
if brightness:
r4 = tf.image.random_brightness(r4, max_delta=0.2)
if gamma:
r5 = tf.image.adjust_gamma(r5, gain=a, gamma=b)
if brightness:
r5 = tf.image.random_brightness(r5, max_delta=0.2)
if contrast:
r5 = tf.image.random_contrast(r5, lower=0.5, upper=1.5)
if gamma:
r6 = tf.image.adjust_gamma(r6, gain=a, gamma=b)
if contrast:
r6 = tf.image.random_contrast(r6, lower=0.5, upper=1.5)
if brightness:
r6 = tf.image.random_brightness(r6, max_delta=0.2)
r12 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r1, lambda: r2)
r34 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r3, lambda: r4)
r56 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r5, lambda: r6)
r1234 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r12, lambda: r34)
r5678 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r56, lambda: r)
r12345678 = tf.cond(tf.random_uniform([], 0, 1) > 0.5, lambda: r1234, lambda: r5678)
return r12345678
def notransform(image):
r = image
# area produces a nice downscaling, but does nearest neighbor for upscaling
# assume we're going to be doing downscaling here
r = tf.image.resize_images(r, [validation_size, validation_size], method=tf.image.ResizeMethod.AREA)
offset = tf.cast(tf.floor(tf.random_uniform([2], 0, 1, seed=seed)), dtype=tf.int32)
r = tf.image.crop_to_bounding_box(r, offset[0], offset[1], validation_size, validation_size)
return r
# 创建生成器，这是一个编码解码器的变种，输入输出均为：256*256*3, 像素值为[-1,1]
def create_generator(generator_inputs, generator_outputs_channels):
layers = []
# encoder_1: [batch, 256, 256, in_channels] => [batch, 128, 128, ngf]
with tf.variable_scope("encoder_1"):
output = gen_conv(generator_inputs, ngf) # ngf is the number of convolution kernels of
# the first convolutional layer. Default is 64.
layers.append(output)
layer_specs = [
ngf * 2, # encoder_2: [batch, 128, 128, ngf] => [batch, 64, 64, ngf * 2]
ngf * 4, # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4]
ngf * 8, # encoder_4: [batch, 32, 32, ngf * 4] => [batch, 16, 16, ngf * 8]
ngf * 8, # encoder_5: [batch, 16, 16, ngf * 8] => [batch, 8, 8, ngf * 8]
ngf * 8, # encoder_6: [batch, 8, 8, ngf * 8] => [batch, 4, 4, ngf * 8]
ngf * 8, # encoder_7: [batch, 4, 4, ngf * 8] => [batch, 2, 2, ngf * 8]
ngf * 8, # encoder_8: [batch, 2, 2, ngf * 8] => [batch, 1, 1, ngf * 8]
]
# 卷积的编码器
for out_channels in layer_specs:
with tf.variable_scope("encoder_%d" % (len(layers) + 1)):
# 对最后一层使用激活函数
rectified = lrelu(layers[-1], 0.2)
# [batch, in_height, in_width, in_channels] => [batch, in_height/2, in_width/2, out_channels]
convolved = gen_conv(rectified, out_channels)
output = batchnorm(convolved)
layers.append(output)
layer_specs = [
(ngf * 8, 0.5), # decoder_8: [batch, 1, 1, ngf * 8] => [batch, 2, 2, ngf * 8 * 2]
(ngf * 8, 0.5), # decoder_7: [batch, 2, 2, ngf * 8 * 2] => [batch, 4, 4, ngf * 8 * 2]
(ngf * 8, 0.5), # decoder_6: [batch, 4, 4, ngf * 8 * 2] => [batch, 8, 8, ngf * 8 * 2]
(ngf * 8, 0.0), # decoder_5: [batch, 8, 8, ngf * 8 * 2] => [batch, 16, 16, ngf * 8 * 2]
(ngf * 8, 0.0), # decoder_4: [batch, 16, 16, ngf * 8 * 2] => [batch, 32, 32, ngf * 4 * 2]
(ngf * 4, 0.0), # decoder_3: [batch, 32, 32, ngf * 4 * 2] => [batch, 64, 64, ngf * 2 * 2]
(ngf * 2, 0.0), # decoder_2: [batch, 64, 64, ngf * 2 * 2] => [batch, 128, 128, ngf * 2]
]
# 卷积的解码器
num_encoder_layers = len(layers) # 8
for decoder_layer, (out_channels, dropout) in enumerate(layer_specs):
skip_layer = num_encoder_layers - decoder_layer - 1
with tf.variable_scope("decoder_%d" % (skip_layer + 1)):
if decoder_layer == 0:
# first decoder layer doesn't have skip connections
# since it is directly connected to the skip_layer
input = layers[-1]
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv(rectified, out_channels)
# elif decoder_layer == 6:
# input = layers[-1]
# elif decoder_layer == 5:
# input = layers[-1]
# elif decoder_layer == 4:
# input = layers[-1]
# elif decoder_layer == 3:
# input = layers[-1]
# elif decoder_layer == 2:
# input = layers[-1]
# elif decoder_layer == 4:
# input = layers[-1]
# # input = tf.concat([layers[-1], layers[3]], axis=3)
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv2(rectified, out_channels)
else:
input = tf.concat([layers[-1], layers[skip_layer]], axis=3)
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv(rectified, out_channels)
rectified = tf.nn.relu(input)
# rectified = tf.nn.leaky_relu(input)
# [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
output = gen_deconv(rectified, out_channels)
output = batchnorm(output)
if dropout > 0.0:
output = tf.nn.dropout(output, keep_prob=1 - dropout)
layers.append(output)
# decoder_1: [batch, 128, 128, ngf * 2] => [batch, 256, 256, generator_outputs_channels]
with tf.variable_scope("decoder_1"):
# input = tf.concat([layers[-1], layers[0]], axis=3)
# input = tf.concat([layers[-1], layers[1]], axis=3)
input = layers[-1]
rectified = tf.nn.relu(input)
# rectified = tf.nn.leaky_relu(input)
output = gen_deconv(rectified, generator_outputs_channels)
output = tf.tanh(output)
layers.append(output)
return layers[-1]
# 创建判别器，输入生成的图像和真实的图像：两个[batch,256,256,3],元素值值[-1,1]，输出:[batch,30,30,1],元素值为概率
# def create_discriminator(discrim_inputs, discrim_targets):
# n_layers = 3
# layers = []
#
# # 2x [batch, height, width, in_channels] => [batch, height, width, in_channels * 2]
# input = tf.concat([discrim_inputs, discrim_targets], axis=3)
#
# # layer_1: [batch, 256, 256, in_channels * 2] => [batch, 128, 128, ndf]
# with tf.variable_scope("layer_1"):
# convolved = discrim_conv(input, ndf, stride=2)
# rectified = lrelu(convolved, 0.2)
# layers.append(rectified)
#
# # layer_2: [batch, 128, 128, ndf] => [batch, 64, 64, ndf * 2]
# # layer_3: [batch, 64, 64, ndf * 2] => [batch, 32, 32, ndf * 4]
# # layer_4: [batch, 32, 32, ndf * 4] => [batch, 31, 31, ndf * 8]
# for i in range(n_layers):
# with tf.variable_scope("layer_%d" % (len(layers) + 1)):
# out_channels = ndf * min(2 ** (i + 1), 8)
# stride = 1 if i == n_layers - 1 else 2 # last layer here has stride 1
# convolved = discrim_conv(layers[-1], out_channels, stride=stride)
# normalized = batchnorm(convolved)
# rectified = lrelu(normalized, 0.2)
# layers.append(rectified)
#
# # layer_5: [batch, 31, 31, ndf * 8] => [batch, 30, 30, 1]
# with tf.variable_scope("layer_%d" % (len(layers) + 1)):
# convolved = discrim_conv(rectified, out_channels=1, stride=1)
# output = tf.sigmoid(convolved)
# layers.append(output)
#
# return layers[-1]
def create_generator2(generator_inputs, generator_outputs_channels):
layers = []
# encoder_1: [batch, 256, 256, in_channels] => [batch, 128, 128, ngf]
with tf.variable_scope("encoder_1"):
output = gen_conv2(generator_inputs, ngf) # ngf is the number of convolution kernels of
# the first convolutional layer. Default is 64.
layers.append(output)
layer_specs = [
ngf * 2, # encoder_2: [batch, 128, 128, ngf] => [batch, 64, 64, ngf * 2]
ngf * 4, # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4]
ngf * 8, # encoder_4: [batch, 32, 32, ngf * 4] => [batch, 16, 16, ngf * 8]
# ngf * 8, # encoder_5: [batch, 16, 16, ngf * 8] => [batch, 8, 8, ngf * 8]
# ngf * 8, # encoder_6: [batch, 8, 8, ngf * 8] => [batch, 4, 4, ngf * 8]
# ngf * 8, # encoder_7: [batch, 4, 4, ngf * 8] => [batch, 2, 2, ngf * 8]
# ngf * 8, # encoder_8: [batch, 2, 2, ngf * 8] => [batch, 1, 1, ngf * 8]
]
# 卷积的编码器
for out_channels in layer_specs:
with tf.variable_scope("encoder_%d" % (len(layers) + 1)):
# 对最后一层使用激活函数
rectified = lrelu(layers[-1], 0.2)
# [batch, in_height, in_width, in_channels] => [batch, in_height/2, in_width/2, out_channels]
convolved = gen_conv2(rectified, out_channels)
output = batchnorm(convolved)
layers.append(output)
layer_specs = [
# (ngf * 8, 0.5), # decoder_8: [batch, 1, 1, ngf * 8] => [batch, 2, 2, ngf * 8 * 2]
# (ngf * 8, 0.5), # decoder_7: [batch, 2, 2, ngf * 8 * 2] => [batch, 4, 4, ngf * 8 * 2]
# (ngf * 8, 0.5), # decoder_6: [batch, 4, 4, ngf * 8 * 2] => [batch, 8, 8, ngf * 8 * 2]
# (ngf * 8, 0.0), # decoder_5: [batch, 8, 8, ngf * 8 * 2] => [batch, 16, 16, ngf * 8 * 2]
(ngf * 8, 0.0), # decoder_4: [batch, 16, 16, ngf * 8 * 2] => [batch, 32, 32, ngf * 4 * 2]
(ngf * 4, 0.0), # decoder_3: [batch, 32, 32, ngf * 4 * 2] => [batch, 64, 64, ngf * 2 * 2]
(ngf * 2, 0.0), # decoder_2: [batch, 64, 64, ngf * 2 * 2] => [batch, 128, 128, ngf * 2]
]
# 卷积的解码器
num_encoder_layers = len(layers) # 8
for decoder_layer, (out_channels, dropout) in enumerate(layer_specs):
skip_layer = num_encoder_layers - decoder_layer - 1
with tf.variable_scope("decoder_%d" % (skip_layer + 1)):
if decoder_layer == 0:
# first decoder layer doesn't have skip connections
# since it is directly connected to the skip_layer
input = layers[-1]
input = gen_conv3(input, 1024)
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv(rectified, out_channels)
# elif decoder_layer == 6:
# input = layers[-1]
# elif decoder_layer == 5:
# input = layers[-1]
# elif decoder_layer == 4:
# input = layers[-1]
# elif decoder_layer == 3:
# input = layers[-1]
elif decoder_layer == 2:
input = layers[-1]
elif decoder_layer == 1:
input = layers[-1]
# elif decoder_layer == 4:
# input = layers[-1]
# # input = tf.concat([layers[-1], layers[3]], axis=3)
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv2(rectified, out_channels)
else:
input = tf.concat([layers[-1], layers[skip_layer]], axis=3)
# rectified = tf.nn.relu(input)
# # rectified = tf.nn.leaky_relu(input)
# # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
# output = gen_deconv(rectified, out_channels)
rectified = tf.nn.relu(input)
# rectified = tf.nn.leaky_relu(input)
# [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
output = gen_deconv2(rectified, out_channels)
output = batchnorm(output)
if dropout > 0.0:
output = tf.nn.dropout(output, keep_prob=1 - dropout)
layers.append(output)
# decoder_1: [batch, 128, 128, ngf * 2] => [batch, 256, 256, generator_outputs_channels]
with tf.variable_scope("decoder_1"):
# input = tf.concat([layers[-1], layers[0]], axis=3)
# input = tf.concat([layers[-1], layers[1]], axis=3)
input = layers[-1]
rectified = tf.nn.relu(input)
# rectified = tf.nn.leaky_relu(input)
output = gen_deconv2(rectified, 64)
output = gen_deconv3(output, generator_outputs_channels)
# output = tf.tanh(output)
# output = tf.nn.softmax(output)
# output = tf.nn.relu(output)
output = tf.sigmoid(output)
layers.append(output)
return layers[-1]
# 创建Pix2Pix模型，inputs和targets形状为：[batch_size, height, width, channels]
def create_model(inputs, targets):
with tf.variable_scope("generator1"):
out_channels = int(targets.get_shape()[-1])
outputs = create_generator2(inputs, out_channels)
# outputs = UNet(inputs)
# create two copies of discriminator, one for real pairs and one for fake pairs
# they share the same underlying variables
# with tf.name_scope("real_discriminator"):
# with tf.variable_scope("discriminator"):
# # 2x [batch, height, width, channels] => [batch, 30, 30, 1]
# predict_real = create_discriminator(inputs, targets) # 条件变量图像和真实图像
# with tf.name_scope("fake_discriminator"):
# with tf.variable_scope("discriminator", reuse=True):
# # 2x [batch, height, width, channels] => [batch, 30, 30, 1]
# predict_fake = create_discriminator(inputs, outputs) # 条件变量图像和生成的图像
# The discriminator loses, the discriminator wants V(G, D) as large as possible
# with tf.name_scope("discriminator_loss"):
# # minimizing -tf.log will try to get inputs to 1
# # predict_real => 1
# # predict_fake => 0
# discrim_loss = tf.reduce_mean(-(tf.log(predict_real + EPS) + tf.log(1 - predict_fake + EPS)))
# Generator loss, the generator wants V(G, D) to be as small as possible
with tf.name_scope("generator_loss"):
# predict_fake => 1
# abs(targets - outputs) => 0
# gen_loss_GAN = tf.reduce_mean(-tf.log(predict_fake + EPS))
# gen_loss_L1 = tf.reduce_mean(tf.abs(targets - outputs))
# gen_loss_L1 = tf.reduce_mean(tf.square(targets - outputs))
gen_loss_L1 = -tf.reduce_mean((targets + EPS) * tf.log(outputs + EPS) + (1 - targets + EPS) * tf.log(1 - outputs + EPS))
# gen_loss = gen_loss_GAN * gan_weight + gen_loss_L1 * l1_weight
# 判别器训练
# with tf.name_scope("discriminator_train"):
# # 判别器需要优化的参数
# discrim_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminator")]
# # 优化器定义
# discrim_optim = tf.train.AdamOptimizer(lr, beta1)
# # 计算损失函数对优化参数的梯度
# discrim_grads_and_vars = discrim_optim.compute_gradients(discrim_loss, var_list=discrim_tvars)
# # 更新该梯度所对应的参数的状态，返回一个op
# discrim_train = discrim_optim.apply_gradients(discrim_grads_and_vars)
# 生成器训练
with tf.name_scope("generator_train"):
# with tf.control_dependencies([discrim_train]):
# 生成器需要优化的参数列表
gen_tvars = [var for var in tf.trainable_variables() if var.name.startswith("generator1")]
# 定义优化器
gen_optim = tf.train.AdamOptimizer(lr, beta1)
# 计算需要优化的参数的梯度
gen_grads_and_vars = gen_optim.compute_gradients(gen_loss_L1, var_list=gen_tvars)
# gen_grads_and_vars = gen_optim.compute_gradients(gen_loss, var_list=gen_tvars)
# 更新该梯度所对应的参数的状态，返回一个op
gen_train = gen_optim.apply_gradients(gen_grads_and_vars)
'''
在采用随机梯度下降算法训练神经网络时，使用 tf.train.ExponentialMovingAverage 滑动平均操作的意义在于
提高模型在测试数据上的健壮性（robustness）。tensorflow 下的 tf.train.ExponentialMovingAverage 需要
提供一个衰减率（decay）。该衰减率用于控制模型更新的速度。该衰减率用于控制模型更新的速度，
ExponentialMovingAverage 对每一个（待更新训练学习的）变量（variable）都会维护一个影子变量
（shadow variable）。影子变量的初始值就是这个变量的初始值，
shadow_variable=decay×shadow_variable+(1−decay)×variable
'''
ema = tf.train.ExponentialMovingAverage(decay=0.99)
# update_losses = ema.apply([discrim_loss, gen_loss_GAN, gen_loss_L1])
update_losses = ema.apply([gen_loss_L1])
global_step = tf.train.get_or_create_global_step()
incr_global_step = tf.assign(global_step, global_step + 1)
return Model(
# predict_real=predict_real, # 条件变量(输入图像)和真实图像之间的概率值，形状为；[batch,30,30,1]
# predict_fake=predict_fake, # 条件变量(输入图像)和生成图像之间的概率值，形状为；[batch,30,30,1]
# discrim_loss=ema.average(discrim_loss), # 判别器损失
# discrim_grads_and_vars=discrim_grads_and_vars, # 判别器需要优化的参数和对应的梯度
# gen_loss_GAN=ema.average(gen_loss_GAN), # 生成器的损失
gen_loss_L1=ema.average(gen_loss_L1), # 生成器的 L1损失
gen_grads_and_vars=gen_grads_and_vars, # 生成器需要优化的参数和对应的梯度
outputs=outputs, # 生成器生成的图片
train=tf.group(update_losses, incr_global_step, gen_train), # 打包需要run的操作op
validate=gen_loss_L1
)
# 保存图像
def save_images(output_dir, fetches, step=None):
image_dir = os.path.join(output_dir)
if not os.path.exists(image_dir):
os.makedirs(image_dir)
filesets = []
for i, in_path in enumerate(fetches["paths"]):
# name, _ = os.path.splitext(os.path.basename(in_path.decode("utf8")))
name, _ = os.path.splitext(os.path.basename(str(in_path)))
fileset = {"name": name, "step": step}
for kind in ["inputs", "outputs", "targets"]:
filename = name + "-" + kind + ".jpg"
if step is not None:
filename = "%08d-%s" % (step, filename)
fileset[kind] = filename
out_path = os.path.join(image_dir, filename)
contents = fetches[kind][i]
with open(out_path, "wb") as f:
f.write(contents)
filesets.append(fileset)
return filesets
# 保存图像
def save_images_test(output_dir, fetches, step=None):
image_dir = os.path.join(output_dir)
if not os.path.exists(image_dir):
os.makedirs(image_dir)
filesets = []
for i, in_path in enumerate(fetches["paths"]):
# name, _ = os.path.splitext(os.path.basename(in_path.decode("utf8")))
name, _ = os.path.splitext(os.path.basename(str(in_path)))
fileset = {"name": name, "step": step}
for kind in ["outputs"]:
filename = name + "-" + kind + ".jpg"
if step is not None:
filename = "%08d-%s" % (step, filename)
fileset[kind] = filename
out_path = os.path.join(image_dir, filename)
contents = fetches[kind][i]
with open(out_path, "wb") as f:
f.write(contents)
filesets.append(fileset)
return filesets
# 转变图像的尺寸、并且将[0,1]--->[0,255]
def convert(image):
if aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
# 将数据的类型转换为8位无符号整型
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# 主函数
def train():
csv_write = csv.writer(loss_csv, dialect='excel')
# 设置随机数种子的值
global seed
if seed is None:
seed = random.randint(0, 2 ** 31 - 1)
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
# # 创建目录
# if not os.path.exists(train_output):
# os.makedirs(train_output)
# if not os.path.exists(validation_output):
# os.makedirs(validation_output)
# if not os.path.exists(checkpoint):
# os.makedirs(checkpoint)
# 加载数据集，得到输入数据和目标数据并把范围变为 :[-1,1]
examples = load_examples(train_input_sample, train_input_label, 1, 0)
print("load successful ! examples count = %d" % examples.count)
print("load successful ! examples count = %d" % examples.count, file=log)
examples2 = load_examples(train_input_sample2, train_input_label2, 1, 0)
print("load successful ! examples2 count = %d" % examples2.count)
print("load successful ! examples2 count = %d" % examples2.count, file=log)
# 创建模型，inputs和targets是：[batch_size, height, width, channels]
# 返回值：
inputs_pd_8 = tf.placeholder(tf.uint8, [None, 256, 256, 3], name='input_image')
target_pd_8 = tf.placeholder(tf.uint8, [None, 256, 256, 3], name='target')
inputs_pd = tf.image.convert_image_dtype(inputs_pd_8, dtype=tf.float32)
target_pd = tf.image.convert_image_dtype(target_pd_8, dtype=tf.float32)
model = create_model(inputs_pd, target_pd)
print("create model successful!")
print("create model successful!", file=log)
# 图像处理[-1, 1] => [0, 1]
# inputs = deprocess(inputs_pd)
# targets = deprocess(target_pd)
# outputs = deprocess(model.outputs)
# inputs2 = deprocess(inputs_pd)
# targets2 = deprocess(target_pd)
# outputs2 = deprocess(model.outputs)
inputs = inputs_pd
targets = target_pd
outputs = model.outputs
inputs2 = inputs_pd
targets2 = target_pd
outputs2 = model.outputs
# 把[0,1]的像素点转为RGB值：[0,255]
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("convert_inputs"):
converted_inputs2 = convert(inputs2)
with tf.name_scope("convert_targets"):
converted_targets2 = convert(targets2)
with tf.name_scope("convert_outputs"):
converted_outputs2 = convert(outputs2)
# 对图像进行编码以便于保存
with tf.name_scope("encode_images"):
display_fetches = {
"paths": examples.paths,
# tf.map_fn接受一个函数对象和集合，用函数对集合中每个元素分别处理
"inputs": tf.map_fn(tf.image.encode_jpeg, converted_inputs, dtype=tf.string, name="input_jpegs"),
"targets": tf.map_fn(tf.image.encode_jpeg, converted_targets, dtype=tf.string, name="target_jpegs"),
"outputs": tf.map_fn(tf.image.encode_jpeg, converted_outputs, dtype=tf.string, name="output_jpegs"),
}
with tf.name_scope("encode_images"):
display_fetches2 = {
"paths": examples2.paths,
# tf.map_fn接受一个函数对象和集合，用函数对集合中每个元素分别处理
"inputs": tf.map_fn(tf.image.encode_jpeg, converted_inputs2, dtype=tf.string,
name="input_jpegs"),
"targets": tf.map_fn(tf.image.encode_jpeg, converted_targets2, dtype=tf.string,
name="target_jpegs"),
"outputs": tf.map_fn(tf.image.encode_jpeg, converted_outputs2, dtype=tf.string,
name="output_jpegs"),
}
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
# 只保存最新一个checkpoint
saver = tf.train.Saver(max_to_keep=10)
new_loss_v = 10
min_loss_v = 10
train_loss = 10
train_fetches = {}
val_fetches = {}
init = tf.global_variables_initializer()
result = {} # for train
result2 = {} # for validation
with tf.Session() as sess:
sess.run(init)
print("parameter_count =", sess.run(parameter_count))
print("parameter_count =", sess.run(parameter_count), file=log)
# if max_epochs is not None:
# max_steps = examples.steps_per_epoch * max_epochs # 400X200=8000
# 因为是从文件中读取数据，所以需要启动start_queue_runners()
# 这个函数将会启动输入管道的线程，填充样本到队列中，以便出队操作可以从队列中拿到样本。
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# 运行训练集
print("begin trainning......")
print("begin trainning......", file=log)
print("max_steps:", max_steps)
print("max_steps:", max_steps, file=log)
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)
print("step:", step)
# 执行正常的train
# 定义一个需要run的所有操作的字典
train_fetches = {
"train": model.train # 只有添加这个才会进行参数更新
}
# progress_freq为 100，每100次计算一次三个损失，显示进度
if should(progress_freq):
# train_fetches["discrim_loss"] = model.discrim_loss
# train_fetches["gen_loss_GAN"] = model.gen_loss_GAN
train_fetches["gen_loss_L1"] = model.gen_loss_L1
# display_freq为 100，每100次保存一次输入、目标、输出的图像
if should(display_freq):
train_fetches["display"] = display_fetches
# 获取真实数据：
train_inputs_real, train_targets_real = sess.run([examples.inputs, examples.targets])
train_input_dict = {inputs_pd: train_inputs_real, target_pd: train_targets_real}
results = sess.run(train_fetches, feed_dict=train_input_dict)
if should(display_freq):
print("saving display training images")
save_images(train_output, results["display"], step=step)
# progress_freq为 100，每100次打印一次三种损失的大小，显示进度
if should(progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(step / examples.steps_per_epoch)
train_step = (step - 1) % examples.steps_per_epoch + 1
rate = (step + 1) * batch_size / (time.time() - start)
remaining = (max_steps - step) * batch_size / rate
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60))
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60), file=log)
# print("discrim_loss", results["discrim_loss"])
# print("discrim_loss", results["discrim_loss"], file=log)
# print("gen_loss_GAN", results["gen_loss_GAN"])
# print("gen_loss_GAN", results["gen_loss_GAN"], file=log)
# print("gen_loss_L1", results["gen_loss_L1"])
# print("gen_loss_L1", results["gen_loss_L1"], file=log)
print("gen_loss_cross_entropy", results["gen_loss_L1"])
print("gen_loss_cross_entropy", results["gen_loss_L1"], file=log)
new_loss = results["gen_loss_L1"]
# dis_loss = results["discrim_loss"]
# GAN_loss = results["gen_loss_GAN"]
if should(validation_freq): # 每一百步计算一次 validation
val_fetches["gen_loss_L1"] = model.validate
val_fetches["display"] = display_fetches2
i = 0
sum_loss_v = 0
print("start display validation images and calculate validation loss")
while i <= examples2.count:
val_inputs_real, val_targets_real = sess.run([examples2.inputs, examples2.targets])
val_input_dict = {inputs_pd: val_inputs_real, target_pd: val_targets_real}
results2 = sess.run(val_fetches, feed_dict=val_input_dict)
sum_loss_v = sum_loss_v + results2["gen_loss_L1"]
i = i + 1
save_images(validation_output, results2["display"], step=step)
avg_loss_v = sum_loss_v / i
# print("gen_loss_L1_validation", avg_loss_v)
# print("gen_loss_L1_validation", avg_loss_v, file=log)
print("gen_loss_cross_entropy_validation", avg_loss_v)
print("gen_loss_cross_entropy_validation", avg_loss_v, file=log)
new_loss_v = avg_loss_v
# stu = [step + 1, new_loss_v, new_loss, dis_loss, GAN_loss]
stu = [step + 1, new_loss_v, new_loss]
csv_write.writerow(stu)
# 只保存lose最低的一次模型
# if should(save_freq):
if new_loss_v < min_loss_v and train_loss >= new_loss:
min_loss_v = new_loss_v
train_loss = new_loss
print("saving model")
print("saving model", file=log)
saver.save(sess, os.path.join(checkpoint, "model"), global_step=step)
tf.train.write_graph(sess.graph.as_graph_def(), checkpoint, 'graph_node.pbtxt', as_text=True)
log.close()
loss_csv.close()
def test():
# 设置随机数种子的值
global seed
if seed is None:
seed = random.randint(0, 2 ** 31 - 1)
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
# 创建目录
if not os.path.exists(test_output_dir):
os.makedirs(test_output_dir)
if checkpoint is None:
raise Exception("checkpoint required for test mode")
# disable these features in test mode
scale_size = CROP_SIZE
flip = False
# 加载数据集，得到输入数据和目标数据
examples = load_examples(test_input_dir, test_input_label, 0, 0)
print("load successful ! examples count = %d" % examples.count)
# 创建模型，inputs和targets是：[batch_size, height, width, channels]
model = create_model(examples.inputs, examples.targets)
print("create model successful!")
# 图像处理[-1, 1] => [0, 1]
# inputs = deprocess(examples.inputs)
# targets = deprocess(examples.targets)
# outputs = deprocess(model.outputs)
inputs = examples.inputs
targets = examples.targets
outputs = model.outputs
# 把[0,1]的像素点转为RGB值：[0,255]
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
# 对图像进行编码以便于保存
with tf.name_scope("encode_images"):
display_fetches = {
"paths": examples.paths,
# tf.map_fn接受一个函数对象和集合，用函数对集合中每个元素分别处理
# "inputs": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name="input_pngs"),
# "targets": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name="target_pngs"),
"outputs": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name="output_pngs"),
}
sess = tf.InteractiveSession()
saver = tf.train.Saver(max_to_keep=10)
start = time.time()
ckpt = tf.train.get_checkpoint_state(checkpoint)
saver.restore(sess, ckpt.model_checkpoint_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for step in range(examples.count):
results = sess.run(display_fetches)
save_images_test(test_output_dir, results, step=step)
print("Total Time:", (time.time() - start))
print("Per Image Time:", (time.time() - start) / examples.count)
if __name__ == '__main__':
train()
# test()
