image = load(image_file)
image = preprocess_image_test(image)
return image
加载男性图片，构建训练数据集
train_man = tf.data.Dataset.list_files('./man2woman/trainA/*.jpg')
train_man = train_man.map(load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_man = train_man.shuffle(BUFFER_SIZE)
train_man = train_man.batch(BATCH_SIZE, drop_remainder=True)
加载女性图片，构建训练数据集
train_woman = tf.data.Dataset.list_files('./man2woman/trainB/*.jpg')
train_woman = train_woman.map(load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_woman = train_woman.shuffle(BUFFER_SIZE)
train_woman = train_woman.batch(BATCH_SIZE, drop_remainder=True)
模型构建
在 CycleGAN 中，使用实例归一化代替批归一化，但在 tensorflow 中，未包含实例归一化层，因此需要自行实现。
class InstanceNormalization(tf.keras.layers.Layer):
"""Instance Normalization Layer."""
def init(self, epsilon=1e-5):
super(InstanceNormalization, self).init()
self.epsilon = epsilon
def build(self, input_shape):
self.scale = self.add_weight(
name='scale',
shape=input_shape[-1:],
initializer=tf.random_normal_initializer(1., 0.02),
trainable=True)
self.offset = self.add_weight(
name='offset',
shape=input_shape[-1:],
initializer='zeros',
trainable=True)
def call(self, x):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + self.epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offset
为了减少代码量，定义上采样块和下采样块：
下采样块
def downsample(filters, size, norm_type='batchnorm', apply_norm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
if apply_norm:
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
上采样快
def upsample(filters, size, norm_type='batchnorm', apply_dropout=False):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
接下来构建生成器：
def unet_generator(output_channels, norm_type='batchnorm'):
down_stack = [
downsample(64, 4, norm_type, apply_norm=False),
downsample(128, 4, norm_type),
downsample(256, 4, norm_type),
downsample(512, 4, norm_type),
downsample(512, 4, norm_type),
downsample(512, 4, norm_type),
downsample(512, 4, norm_type),
downsample(512, 4, norm_type),
]
up_stack = [
upsample(512, 4, norm_type, apply_dropout=True),
upsample(512, 4, norm_type, apply_dropout=True),
upsample(512, 4, norm_type, apply_dropout=True),
upsample(512, 4, norm_type),
upsample(256, 4, norm_type),
upsample(128, 4, norm_type),
upsample(64, 4, norm_type),
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(
output_channels, 4, strides=2,
padding='same', kernel_initializer=initializer,
activation='tanh') # (bs, 256, 256, 3)
concat = tf.keras.layers.Concatenate()
inputs = tf.keras.layers.Input(shape=[None, None, 3])
x = inputs
Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1])
Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
x = concat([x, skip])
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
构建鉴别器：
def discriminator(norm_type='batchnorm', target=True):
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape=[None, None, 3], name='input_image')
x = inp
if target:
tar = tf.keras.layers.Input(shape=[None, None, 3], name='target_image')
x = tf.keras.layers.concatenate([inp, tar]) # (bs, 256, 256, channels*2)
down1 = downsample(64, 4, norm_type, False)(x) # (bs, 128, 128, 64)
down2 = downsample(128, 4, norm_type)(down1) # (bs, 64, 64, 128)
down3 = downsample(256, 4, norm_type)(down2) # (bs, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)
conv = tf.keras.layers.Conv2D(
512, 4, strides=1, kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (bs, 31, 31, 512)
if norm_type.lower() == 'batchnorm':
norm1 = tf.keras.layers.BatchNormalization()(conv)
elif norm_type.lower() == 'instancenorm':
norm1 = InstanceNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(norm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)
last = tf.keras.layers.Conv2D(
1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)
if target:
return tf.keras.Model(inputs=[inp, tar], outputs=last)
else:
return tf.keras.Model(inputs=inp, outputs=last)
实例化生成器与鉴别器：
generator_g = unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
generator_f = unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
discriminator_x = discriminator(norm_type='instancenorm', target=False)
discriminator_y = discriminator(norm_type='instancenorm', target=False)
损失函数与优化器的定义：
loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)
鉴别器损失
def discriminator_loss(real, generated):
real_loss = loss_obj(tf.ones_like(real), real)
generated_loss = loss_obj(tf.zeros_like(generated), generated)
total_disc_loss = real_loss + generated_loss
return total_disc_loss * 0.5
生成器损失
def generator_loss(generated):
return loss_obj(tf.ones_like(generated), generated)
循环一致性损失
def calc_cycle_loss(real_image, cycled_image):
loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))
return LAMBDA * loss1
identity loss
def identity_loss(real_image, same_image):
loss = tf.reduce_mean(tf.abs(real_image - same_image))
return LAMBDA * 0.5 * loss
优化器
generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
训练结果可视化函数
创建 generate_images 函数用于在训练过程中查看模型效果.
def generate_images(model, test_input):
prediction = model(test_input)
plt.figure(figsize=(12, 12))
display_list = [test_input[0], prediction[0]]
title = ['Input Image', 'Predicted Image']
for i in range(2):
plt.subplot(1, 2, i+1)
plt.title(title[i])
getting the pixel values between [0, 1] to plot it.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
plt.savefig('results/{}.png'.format(int(time.time())))
训练步骤
首先需要定义训练函数：
@tf.function
def train_step(real_x, real_y):
with tf.GradientTape(persistent=True) as tape:
Generator G translates X -> Y
Generator F translates Y -> X.
fake_y = generator_g(real_x, training=True)
cycled_x = generator_f(fake_y, training=True)
fake_x = generator_f(real_y, training=True)
cycled_y = generator_g(fake_x, training=True)
same_x and same_y are used for identity loss.
same_x = generator_f(real_x, training=True)
same_y = generator_g(real_y, training=True)
disc_real_x = discriminator_x(real_x, training=True)
disc_real_y = discriminator_y(real_y, training=True)
disc_fake_x = discriminator_x(fake_x, training=True)
disc_fake_y = discriminator_y(fake_y, training=True)
calculate the loss
gen_g_loss = generator_loss(disc_fake_y)
gen_f_loss = generator_loss(disc_fake_x)
total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)
Total generator loss = adversarial loss + cycle loss
total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)
total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)
disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)
disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)
Calculate the gradients for generator and discriminator
generator_g_gradients = tape.gradient(total_gen_g_loss, generator_g.trainable_variables)
generator_f_gradients = tape.gradient(total_gen_f_loss, generator_f.trainable_variables)
discriminator_x_gradients = tape.gradient(disc_x_loss, discriminator_x.trainable_variables)
discriminator_y_gradients = tape.gradient(disc_y_loss, discriminator_y.trainable_variables)
Apply the gradients to the optimizer
generator_g_optimizer.apply_gradients(zip(generator_g_gradients, generator_g.trainable_variables))
generator_f_optimizer.apply_gradients(zip(generator_f_gradients, generator_f.trainable_variables))
discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients, discriminator_x.trainable_variables))
discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients, discriminator_y.trainable_variables))
最后进行模型的训练：
for epoch in range(EPOCHS):
start = time.time()
n = 0
for image_x, image_y in tf.data.Dataset.zip((train_man, train_woman)):
train_step(image_x, image_y)
generate_images(generator_g, sample_man)
做了那么多年开发，自学了很多门编程语言，我很明白学习资源对于学一门新语言的重要性，这些年也收藏了不少的Python干货，对我来说这些东西确实已经用不到了，但对于准备自学Python的人来说，或许它就是一个宝藏，可以给你省去很多的时间和精力。
别在网上瞎学了，我最近也做了一些资源的更新，只要你是我的粉丝，这期福利你都可拿走。
我先来介绍一下这些东西怎么用，文末抱走。
（1）Python所有方向的学习路线（新版）
这是我花了几天的时间去把Python所有方向的技术点做的整理，形成各个领域的知识点汇总，它的用处就在于，你可以按照上面的知识点去找对应的学习资源，保证自己学得较为全面。
最近我才对这些路线做了一下新的更新，知识体系更全面了。
（2）Python学习视频
包含了Python入门、爬虫、数据分析和web开发的学习视频，总共100多个，虽然没有那么全面，但是对于入门来说是没问题的，学完这些之后，你可以按照我上面的学习路线去网上找其他的知识资源进行进阶。
（3）100多个练手项目
我们在看视频学习的时候，不能光动眼动脑不动手，比较科学的学习方法是在理解之后运用它们，这时候练手项目就很适合了，只是里面的项目比较多，水平也是参差不齐，大家可以挑自己能做的项目去练练。
（4）200多本电子书
这些年我也收藏了很多电子书，大概200多本，有时候带实体书不方便的话，我就会去打开电子书看看，书籍可不一定比视频教程差，尤其是权威的技术书籍。
基本上主流的和经典的都有，这里我就不放图了，版权问题，个人看看是没有问题的。
（5）Python知识点汇总
知识点汇总有点像学习路线，但与学习路线不同的点就在于，知识点汇总更为细致，里面包含了对具体知识点的简单说明，而我们的学习路线则更为抽象和简单，只是为了方便大家只是某个领域你应该学习哪些技术栈。
（6）其他资料
还有其他的一些东西，比如说我自己出的Python入门图文类教程，没有电脑的时候用手机也可以学习知识，学会了理论之后再去敲代码实践验证，还有Python中文版的库资料、MySQL和HTML标签大全等等，这些都是可以送给粉丝们的东西。
这些都不是什么非常值钱的东西，但对于没有资源或者资源不是很好的学习者来说确实很不错，你要是用得到的话都可以直接抱走，关注过我的人都知道，这些都是可以拿到的。
