tf中WGAN-GP实战
文章目录
1. 任务
利用DCGAN对Anmie数据集生成
2. WGAN模型
# 定义WGAN-GP过程
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
class Generator(keras.Model):
def __init__(self):
super(Generator, self).__init__()
self.fc = layers.Dense(3*3*512)
self.conv1 = layers.Conv2DTranspose(256, 3, 3, 'valid')
self.bn1 = layers.BatchNormalization()
self.conv2 = layers.Conv2DTranspose(128, 5, 2, 'valid')
self.bn2 = layers.BatchNormalization()
# 最后一层输出的为输入D中的数据，需要与保持原数据（图片）的输入维度一致，所以设为三个卷积核
self.conv3 = layers.Conv2DTranspose(3, 4, 3, 'valid')
def call(self, inputs, training=None):
x = self.fc(inputs)
x = tf.reshape(x, [-1, 3, 3, 512])
# 为避免梯度弥散，用leaky_relu代替relu
x = tf.nn.leaky_relu(x)
# BatchNormalization层在训练和测试时行为不一致，需要标注是训练还是测试模式
x = tf.nn.leaky_relu(self.bn1(self.conv1(x), training=training))
x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))
x = self.conv3(x)
x = tf.tanh(x)
return x
class Discriminator(keras.Model):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = layers.Conv2D(64, 5, 3, 'valid')
self.conv2 = layers.Conv2D(128, 5, 3, 'valid')
self.bn2 = layers.BatchNormalization()
self.conv3 = layers.Conv2D(256, 5, 3, 'valid')
self.bn3 = layers.BatchNormalization()
# flatten用于自动打平，可以放到Sequential容器中，reshape不可放到Sequential容器中
self.flatten = layers.Flatten()
self.fc = layers.Dense(1)
def call(self, inputs, training=None):
x = tf.nn.leaky_relu(self.conv1(inputs))
x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))
x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training))
x = self.flatten(x)
output = self.fc(x)
return output
def main():
d = Discriminator()
g = Generator()
x = tf.random.normal([1, 64, 64, 3])
z = tf.random.normal([1, 100])
prob = d(x)
x_hat = g(z)
if __name__ == '__main__':
main()
3. WGAN训练
# WGAN-GP与DCGAN的区别只在损失函数部分
# WGAN-GP在损失函数部分添加了梯度惩罚项
import numpy as np
import tensorflow as tf
from tensorflow import keras
from PIL import Image
import glob
from wgan import Generator, Discriminator
from dataset import make_anime_dataset
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# dataset.py以把图片处理为64x64
def save_result(val_out, val_block_size, image_path, color_mode):
def preprocess(img):
img = ((img + 1.0) * 127.5).astype(np.uint8)
# img = img.astype(np.uint8)
return img
preprocesed = preprocess(val_out)
final_image = np.array([])
single_row = np.array([])
for b in range(val_out.shape[0]):
# concat image into a row
if single_row.size == 0:
single_row = preprocesed[b, :, :, :]
else:
single_row = np.concatenate((single_row, preprocesed[b, :, :, :]), axis=1)
# concat image row to final_image
if (b+1) % val_block_size == 0:
if final_image.size == 0:
final_image = single_row
else:
final_image = np.concatenate((final_image, single_row), axis=0)
# reset single row
single_row = np.array([])
if final_image.shape[2] == 1:
final_image = np.squeeze(final_image, axis=2)
Image.fromarray(final_image).save(image_path)
def celoss_zeros(output):
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=tf.zeros_like(output))
loss = tf.reduce_mean(loss)
return loss
def celoss_ones(output):
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=tf.ones_like(output))
loss = tf.reduce_mean(loss)
return loss
def gradient_penalty(discriminator, batch_x, fake_image):
# batch_x即真实图片
batch_size0 = batch_x.shape[0]
# t需改为与输入真实图片相同格式
# t为一个image全局共用，共有需要batch_size0（总数）个image
t = tf.random.uniform([batch_size0, 1, 1, 1])
t = tf.broadcast_to(t, batch_x.shape)
inter_plate = t * batch_x + (1-t) * fake_image
with tf.GradientTape() as tape:
# 对于tensor类型数据，更新梯度时必须加tape.watch
tape.watch([inter_plate])
d_inter_plate_output = discriminator(inter_plate, training=True)
grads = tape.gradient(d_inter_plate_output, inter_plate)
# 更改grads的维度（打平） [b,h,w,c]=>[b,-1]
grads = tf.reshape(grads, [grads.shape[0], -1])
gp = tf.norm(grads, axis=1) # 求2范数
gp = tf.reduce_mean((gp-1) ** 2)
return gp
def d_loss_fn(generator, discriminator, batch_z, batch_x, is_training):
fake_image = generator(batch_z, is_training)
d_fake_output = discriminator(fake_image, is_training)
d_real_output = discriminator(batch_x, is_training)
d_loss_fake = celoss_zeros(d_fake_output)
d_loss_real = celoss_ones(d_real_output)
gp = gradient_penalty(discriminator, batch_x, fake_image)
loss = d_loss_real + d_loss_fake + 10. * gp
return loss, gp
def g_loss_fn(generator, discriminator, batch_z, is_training):
fake_image = generator(batch_z, is_training)
d_fake_output = discriminator(fake_image, is_training)
loss = celoss_ones(d_fake_output)
return loss
def main():
z_dim = 100
epochs = 1000000
batch_size = 256
lr = 1e-3
is_training = True
img_path = glob.glob(r'E:\PyCharm Community Edition 2020.3.5\workspace\wgan\data\anime\*.jpg')
assert len(img_path) > 0
dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)
sample = next(iter(dataset))
dataset = dataset.repeat() # repeat即一直sample
db_iter = iter(dataset)
generator = Generator()
generator.build(input_shape=(None, z_dim))
discriminator = Discriminator()
discriminator.build(input_shape=(None, 64, 64, 3))
g_optimizer = tf.keras.optimizers.Adam(learning_rate=lr, beta_1=0.5) # GAN需设置Adam优化器的beta_1参数
d_optimizer = tf.keras.optimizers.Adam(learning_rate=lr, beta_1=0.5)
for epoch in range(epochs):
batch_z = tf.random.normal([batch_size, z_dim])
batch_x = next(db_iter)
with tf.GradientTape() as tape:
d_loss, gp = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training)
grads = tape.gradient(d_loss, discriminator.trainable_variables)
d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))
with tf.GradientTape() as tape:
g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
grads = tape.gradient(g_loss, generator.trainable_variables)
g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))
if epoch % 100 == 0:
print(epoch, 'd_loss:', float(d_loss), 'g_loss:', float(g_loss), 'gp:', float(gp))
z = tf.random.normal([100, z_dim])
fake_image = generator(z, training=False)
image_path = os.path.join('images', 'gan-%d.png'%epoch)
save_result(fake_image.numpy(), 10, image_path, color_mode='P')
if __name__ == '__main__':
main()
4. 数据集处理
import multiprocessing
import tensorflow as tf
def make_anime_dataset(img_paths, batch_size, resize=64, drop_remainder=True, shuffle=True, repeat=1):
@tf.function
def _map_fn(img):
img = tf.image.resize(img, [resize, resize])
img = tf.clip_by_value(img, 0, 255)
img = img / 127.5 - 1
return img
dataset = disk_image_batch_dataset(img_paths,
batch_size,
drop_remainder=drop_remainder,
map_fn=_map_fn,
shuffle=shuffle,
repeat=repeat)
img_shape = (resize, resize, 3)
len_dataset = len(img_paths) // batch_size
return dataset, img_shape, len_dataset
def batch_dataset(dataset,
batch_size,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
# set defaults
if n_map_threads is None:
n_map_threads = multiprocessing.cpu_count()
if shuffle and shuffle_buffer_size is None:
shuffle_buffer_size = max(batch_size * 128, 2048) # set the minimum buffer size as 2048
# [*] it is efficient to conduct `shuffle` before `map`/`filter` because `map`/`filter` is sometimes costly
if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size)
if not filter_after_map:
if filter_fn:
dataset = dataset.filter(filter_fn)
if map_fn:
dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads)
else: # [*] this is slower
if map_fn:
dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads)
if filter_fn:
dataset = dataset.filter(filter_fn)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
dataset = dataset.repeat(repeat).prefetch(n_prefetch_batch)
return dataset
def memory_data_batch_dataset(memory_data,
batch_size,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
"""Batch dataset of memory data.
Parameters
----------
memory_data : nested structure of tensors/ndarrays/lists
"""
dataset = tf.data.Dataset.from_tensor_slices(memory_data)
dataset = batch_dataset(dataset,
batch_size,
drop_remainder=drop_remainder,
n_prefetch_batch=n_prefetch_batch,
filter_fn=filter_fn,
map_fn=map_fn,
n_map_threads=n_map_threads,
filter_after_map=filter_after_map,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
repeat=repeat)
return dataset
def disk_image_batch_dataset(img_paths,
batch_size,
labels=None,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
"""Batch dataset of disk image for PNG and JPEG.
Parameters
----------
img_paths : 1d-tensor/ndarray/list of str
labels : nested structure of tensors/ndarrays/lists
"""
if labels is None:
memory_data = img_paths
else:
memory_data = (img_paths, labels)
def parse_fn(path, *label):
img = tf.io.read_file(path)
img = tf.image.decode_png(img, 3) # fix channels to 3
return (img,) + label
if map_fn: # fuse `map_fn` and `parse_fn`
def map_fn_(*args):
return map_fn(*parse_fn(*args))
else:
map_fn_ = parse_fn
dataset = memory_data_batch_dataset(memory_data,
batch_size,
drop_remainder=drop_remainder,
n_prefetch_batch=n_prefetch_batch,
filter_fn=filter_fn,
map_fn=map_fn_,
n_map_threads=n_map_threads,
filter_after_map=filter_after_map,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
repeat=repeat)
return dataset