pix2pix GAN 用于low dose ct denoising的代码

以下是使用pix2pix GAN进行低剂量CT去噪的示例代码：

import tensorflow as tf
import tensorflow_addons as tfa
import os

# 定义生成器模型
def generator():
    inputs = tf.keras.layers.Input(shape=[None, None, 1])
    
    # 编码器
    down_stack = [
        downsample(64, 4, apply_batchnorm=False),  # (batch_size, 128, 128, 64)
        downsample(128, 4),  # (batch_size, 64, 64, 128)
        downsample(256, 4),  # (batch_size, 32, 32, 256)
        downsample(512, 4),  # (batch_size, 16, 16, 512)
        downsample(512, 4),  # (batch_size, 8, 8, 512)
        downsample(512, 4),  # (batch_size, 4, 4, 512)
        downsample(512, 4),  # (batch_size, 2, 2, 512)
        downsample(512, 4),  # (batch_size, 1, 1, 512)
    ]
    
    up_stack = [
        upsample(512, 4, apply_dropout=True),  # (batch_size, 2, 2, 1024)
        upsample(512, 4, apply_dropout=True),  # (batch_size, 4, 4, 1024)
        upsample(512, 4, apply_dropout=True),  # (batch_size, 8, 8, 1024)
        upsample(512, 4),  # (batch_size, 16, 16, 1024)
        upsample(256, 4),  # (batch_size, 32, 32, 512)
        upsample(128, 4),  # (batch_size, 64, 64, 256)
        upsample(64, 4),  # (batch_size, 128, 128, 128)
    ]
    
    initializer = tf.random_normal_initializer(0., 0.02)
    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
                                           strides=2,
                                           padding='same',
                                           kernel_initializer=initializer)  # (batch_size, 256, 256, 3)
    
    x = inputs
    
    skips = []
    for down in down_stack:
        x = down(x)
        skips.append(x)
        
    skips = reversed(skips[:-1])
    
    for up, skip in zip(up_stack, skips):
        x = up(x)
        x = tf.keras.layers.Concatenate()([x, skip])
        
    x = last(x)
    
    return tf.keras.Model(inputs=inputs, outputs=x)

# 定义判别器模型
def discriminator():
    initializer = tf.random_normal_initializer(0., 0.02)
    
    inp = tf.keras.layers.Input(shape=[None, None, 1], name='input_image')
    tar = tf.keras.layers.Input(shape=[None, None, 1], name='target_image')
    
    x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)
    
    down1 = downsample(64, 4, False)(x)  # (batch_size, 128, 128, 64)
    down2 = downsample(128, 4)(down1)  # (batch_size, 64, 64, 128)
    down3 = downsample(256, 4)(down2)  # (batch_size, 32, 32, 256)
    
    zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3)  # (batch_size, 34, 34, 256)
    conv = tf.keras.layers.Conv2D(512, 4, strides=1,
                                  kernel_initializer=initializer,
                                  use_bias=False)(zero_pad1)  # (batch_size, 31, 31, 512)
    
    batchnorm1 = tfa.layers.InstanceNormalization()(conv)
    
    leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
    
    zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu)  # (batch_size, 33, 33, 512)
    
    last = tf.keras.layers.Conv2D(1, 4, strides=1,
                                  kernel_initializer=initializer)(zero_pad2)  # (batch_size, 30, 30, 1)
    
    return tf.keras.Model(inputs=[inp, tar], outputs=last)

# 定义下采样函数
def downsample(filters, size, apply_batchnorm=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_batchnorm:
        result.add(tfa.layers.InstanceNormalization())
        
    result.add(tf.keras.layers.LeakyReLU())
    
    return result

# 定义上采样函数
def upsample(filters, size, 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))
    
    result.add(tfa.layers.InstanceNormalization())
    
    if apply_dropout:
        result.add(tf.keras.layers.Dropout(0.5))
        
    result.add(tf.keras.layers.ReLU())
    
    return result

# 定义损失函数
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def generator_loss(disc_generated_output, gen_output, target):
    gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
    
    l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
    
    total_gen_loss = gan_loss + (LAMBDA * l1_loss)
    
    return total_gen_loss, gan_loss, l1_loss

def discriminator_loss(disc_real_output, disc_generated_output):
    real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
    
    generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
    
    total_disc_loss = real_loss + generated_loss
    
    return total_disc_loss

# 定义优化器
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)

# 定义检查点
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
                                 discriminator_optimizer=discriminator_optimizer,
                                 generator=generator(),
                                 discriminator=discriminator())

# 定义训练循环
@tf.function
def train_step(input_image, target, step):
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        gen_output = generator()(input_image, training=True)
        
        disc_real_output = discriminator()([input_image, target], training=True)
        disc_generated_output = discriminator()([input_image, gen_output], training=True)
        
        gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target)
        disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
        
    generator_gradients = gen_tape.gradient(gen_total_loss,
                                            generator().trainable_variables)
    discriminator_gradients = disc_tape.gradient(disc_loss,
                                                 discriminator().trainable_variables)
    
    generator_optimizer.apply_gradients(zip(generator_gradients,
                                            generator().trainable_variables))
    discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
                                                discriminator().trainable_variables))
    
    if step % 100 == 0:
        print('Step {}, Generator Loss: {}, Discriminator Loss: {}'.format(step, gen_total_loss, disc_loss))

# 训练模型
def fit(train_ds, epochs, test_ds):
    for epoch in range(epochs):
        for input_image, target in train_ds:
            train_step(input_image, target, step)
            
        if (epoch + 1) % 10 == 0:
            checkpoint.save(file_prefix=checkpoint_prefix)
            
        print('Epoch {}, Loss: {}'.format(epoch, total_loss))
        
# 加载数据集
def load_data():
    # 加载训练数据和测试数据
    train_dataset = ...
    test_dataset = ...
    
    return train_dataset, test_dataset

# 设置超参数
OUTPUT_CHANNELS = 1
LAMBDA = 100

# 加载数据集
train_dataset, test_dataset = load_data()

# 训练模型
fit(train_dataset, epochs=150, test_ds=test_dataset)

请注意，上述代码只是一个示例，你需要根据你的数据集和需求进行适当的调整和修改。此外，为了完整运行代码，你还需要实现`load_data()`函数来加载训练和测试数据集，并根据你的需求进行适当的预处理。