1 生成器判别器实现

import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layersclass Generator(keras.Model):def __init__(self):super(Generator, self).__init__()# z: [b, 100] => [b, 3*3*512] => [b, 3, 3, 512] => [b, 64, 64, 3]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()self.conv3 = layers.Conv2DTranspose(3, 4, 3, 'valid')def call(self, inputs, training=None):# [z, 100] => [z, 3*3*512]x = self.fc(inputs)x = tf.reshape(x, [-1, 3, 3, 512])x = tf.nn.leaky_relu(x)#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 xclass Discriminator(keras.Model):def __init__(self):super(Discriminator, self).__init__()# [b, 64, 64, 3] => [b, 1]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()# [b, h, w ,c] => [b, -1]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))# [b, h, w, c] => [b, -1]x = self.flatten(x)# [b, -1] => [b, 1]logits = self.fc(x)return logitsdef main():d = Discriminator()g = Generator()x = tf.random.normal([2, 64, 64, 3])z = tf.random.normal([2, 100])prob = d(x)print(prob)x_hat = g(z)print(x_hat.shape)if __name__ == '__main__':main()

loss function

重点改变

''' WGAN gradient_penalty '''
def gradient_penalty(discriminator, batch_x, fake_image):batchsz = batch_x.shape[0]# [b, h, w, c]t = tf.random.uniform([batchsz, 1, 1, 1])# [b, 1, 1, 1] => [b, h, w, c]t = tf.broadcast_to(t, batch_x.shape)interplate = t * batch_x + (1 - t) * fake_imagewith tf.GradientTape() as tape:tape.watch([interplate])d_interplote_logits = discriminator(interplate, training=True)grads = tape.gradient(d_interplote_logits, interplate)# grads:[b, h, w, c] => [b, -1]grads = tf.reshape(grads, [grads.shape[0], -1])gp = tf.norm(grads, axis=1) #[b]gp = tf.reduce_mean( (gp-1)**2 )return gp
def celoss_ones(logits):# [b, 1]# [b] = [1, 1, 1, 1,]# loss = tf.keras.losses.categorical_crossentropy(y_pred=logits,#                                                y_true=tf.ones_like(logits))return - tf.reduce_mean(logits)def celoss_zeros(logits):# [b, 1]# [b] = [1, 1, 1, 1,]# loss = tf.keras.losses.categorical_crossentropy(y_pred=logits,#                                                y_true=tf.zeros_like(logits))return tf.reduce_mean(logits)def d_loss_fn(generator, discriminator, batch_z, batch_x, is_training):# 1. treat real image as real# 2. treat generated image as fakefake_image = generator(batch_z, is_training)d_fake_logits = discriminator(fake_image, is_training)d_real_logits = discriminator(batch_x, is_training)d_loss_real = celoss_ones(d_real_logits)d_loss_fake = celoss_zeros(d_fake_logits)gp = gradient_penalty(discriminator, batch_x, fake_image)loss = d_loss_real + d_loss_fake + 10. * gpreturn loss, gpdef g_loss_fn(generator, discriminator, batch_z, is_training):fake_image = generator(batch_z, is_training)d_fake_logits = discriminator(fake_image, is_training)loss = celoss_ones(d_fake_logits)return loss

3 WGAN 完整代码

import  os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import  numpy as np
import  tensorflow as tf
from    tensorflow import kerasfrom    PIL import Image
import  glob
from    .gan import Generator, Discriminatorfrom    .dataset import make_anime_datasetdef 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 imgpreprocesed = preprocess(val_out)final_image = np.array([])single_row = np.array([])for b in range(val_out.shape[0]):# concat image into a rowif single_row.size == 0:single_row = preprocesed[b, :, :, :]else:single_row = np.concatenate((single_row, preprocesed[b, :, :, :]), axis=1)# concat image row to final_imageif (b+1) % val_block_size == 0:if final_image.size == 0:final_image = single_rowelse:final_image = np.concatenate((final_image, single_row), axis=0)# reset single rowsingle_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_ones(logits):# [b, 1]# [b] = [1, 1, 1, 1,]# loss = tf.keras.losses.categorical_crossentropy(y_pred=logits,#                                                y_true=tf.ones_like(logits))return - tf.reduce_mean(logits)def celoss_zeros(logits):# [b, 1]# [b] = [1, 1, 1, 1,]# loss = tf.keras.losses.categorical_crossentropy(y_pred=logits,#                                                y_true=tf.zeros_like(logits))return tf.reduce_mean(logits)def gradient_penalty(discriminator, batch_x, fake_image):batchsz = batch_x.shape[0]# [b, h, w, c]t = tf.random.uniform([batchsz, 1, 1, 1])# [b, 1, 1, 1] => [b, h, w, c]t = tf.broadcast_to(t, batch_x.shape)interplate = t * batch_x + (1 - t) * fake_imagewith tf.GradientTape() as tape:tape.watch([interplate])d_interplote_logits = discriminator(interplate, training=True)grads = tape.gradient(d_interplote_logits, interplate)# grads:[b, h, w, c] => [b, -1]grads = tf.reshape(grads, [grads.shape[0], -1])gp = tf.norm(grads, axis=1) #[b]gp = tf.reduce_mean( (gp-1)**2 )return gpdef d_loss_fn(generator, discriminator, batch_z, batch_x, is_training):# 1. treat real image as real# 2. treat generated image as fakefake_image = generator(batch_z, is_training)d_fake_logits = discriminator(fake_image, is_training)d_real_logits = discriminator(batch_x, is_training)d_loss_real = celoss_ones(d_real_logits)d_loss_fake = celoss_zeros(d_fake_logits)gp = gradient_penalty(discriminator, batch_x, fake_image)loss = d_loss_real + d_loss_fake + 10. * gpreturn loss, gpdef g_loss_fn(generator, discriminator, batch_z, is_training):fake_image = generator(batch_z, is_training)d_fake_logits = discriminator(fake_image, is_training)loss = celoss_ones(d_fake_logits)return lossdef main():tf.random.set_seed(233)np.random.seed(233)assert tf.__version__.startswith('2.')# hyper parametersz_dim = 100epochs = 3000000batch_size = 512learning_rate = 0.0005is_training = Trueimg_path = glob.glob(r'C:\Users\Jackie\Downloads\faces\*.jpg')assert len(img_path) > 0dataset, img_shape, _ = make_anime_dataset(img_path, batch_size)print(dataset, img_shape)sample = next(iter(dataset))print(sample.shape, tf.reduce_max(sample).numpy(),tf.reduce_min(sample).numpy())dataset = dataset.repeat()db_iter = iter(dataset)generator = Generator() generator.build(input_shape = (None, z_dim))discriminator = Discriminator()discriminator.build(input_shape=(None, 64, 64, 3))z_sample = tf.random.normal([100, z_dim])g_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)d_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)for epoch in range(epochs):for _ in range(5):batch_z = tf.random.normal([batch_size, z_dim])batch_x = next(db_iter)# train Dwith 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))batch_z = tf.random.normal([batch_size, z_dim])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)img_path = os.path.join('images', 'wgan-%d.png'%epoch)save_result(fake_image.numpy(), 10, img_path, color_mode='P')if __name__ == '__main__':main()

数据集加载与转换 dataset.py

import multiprocessingimport tensorflow as tfdef make_anime_dataset(img_paths, batch_size, resize=64, drop_remainder=True, shuffle=True, repeat=1):# @tf.functiondef _map_fn(img):img = tf.image.resize(img, [resize, resize])# img = tf.image.random_crop(img,[resize, resize])# img = tf.image.random_flip_left_right(img)# img = tf.image.random_flip_up_down(img)img = tf.clip_by_value(img, 0, 255)img = img / 127.5 - 1 #-1~1return imgdataset = 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_sizereturn dataset, img_shape, len_datasetdef 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 defaultsif 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 costlyif 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 slowerif 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 datasetdef 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 datasetdef 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 strlabels : nested structure of tensors/ndarrays/lists"""if labels is None:memory_data = img_pathselse:memory_data = (img_paths, labels)def parse_fn(path, *label):img = tf.io.read_file(path)img = tf.image.decode_jpeg(img, channels=3)  # fix channels to 3return (img,) + labelif map_fn:  # fuse `map_fn` and `parse_fn`def map_fn_(*args):return map_fn(*parse_fn(*args))else:map_fn_ = parse_fndataset = 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

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tensorflow WGAN 实现

1 生成器判别器实现

loss function

3 WGAN 完整代码

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