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tf中WGAN-GP实战

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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

标签:map,GP,image,batch,dataset,tf,fn,WGAN
来源: https://blog.csdn.net/qq_46588746/article/details/120593601