pix2pix算法代码

pix2pix算法是一种图像翻译（image-to-image translation）算法，其主要作用是将一种图像转换为另一种图像，例如将黑白线条图转换为彩色图。

以下是一个基于TensorFlow实现的pix2pix算法的简单代码示例：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 定义生成器模型
def generator_model():
    inputs = tf.keras.layers.Input(shape=[256,256,3])
    conv1 = tf.keras.layers.Conv2D(64, [4,4], strides=[2,2], padding='same', activation='relu')(inputs)
    conv2 = tf.keras.layers.Conv2D(128, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(conv1)
    norm2 = tf.keras.layers.BatchNormalization()(conv2)
    conv3 = tf.keras.layers.Conv2D(256, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm2)
    norm3 = tf.keras.layers.BatchNormalization()(conv3)
    conv4 = tf.keras.layers.Conv2D(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm3)
    norm4 = tf.keras.layers.BatchNormalization()(conv4)
    conv5 = tf.keras.layers.Conv2D(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm4)
    norm5 = tf.keras.layers.BatchNormalization()(conv5)
    conv6 = tf.keras.layers.Conv2D(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm5)
    norm6 = tf.keras.layers.BatchNormalization()(conv6)
    conv7 = tf.keras.layers.Conv2D(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm6)
    norm7 = tf.keras.layers.BatchNormalization()(conv7)
    conv8 = tf.keras.layers.Conv2DTranspose(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm7)
    norm8 = tf.keras.layers.BatchNormalization()(conv8)
    drop8 = tf.keras.layers.Dropout(0.5)(norm8)
    conv9 = tf.keras.layers.Conv2DTranspose(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(drop8)
    norm9 = tf.keras.layers.BatchNormalization()(conv9)
    drop9 = tf.keras.layers.Dropout(0.5)(norm9)
    conv10 = tf.keras.layers.Conv2DTranspose(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(drop9)
    norm10 = tf.keras.layers.BatchNormalization()(conv10)
    drop10 = tf.keras.layers.Dropout(0.5)(norm10)
    conv11 = tf.keras.layers.Conv2DTranspose(256, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(drop10)
    norm11 = tf.keras.layers.BatchNormalization()(conv11)
    conv12 = tf.keras.layers.Conv2DTranspose(128, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm11)
    norm12 = tf.keras.layers.BatchNormalization()(conv12)
    conv13 = tf.keras.layers.Conv2DTranspose(64, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm12)
    norm13 = tf.keras.layers.BatchNormalization()(conv13)
    conv14 = tf.keras.layers.Conv2DTranspose(3, [4,4], strides=[2,2], padding='same', activation='tanh')(norm13)
    return tf.keras.models.Model(inputs=inputs, outputs=conv14)

# 定义判别器模型
def discriminator_model():
    inputs = tf.keras.layers.Input(shape=[256,256,6])
    conv1 = tf.keras.layers.Conv2D(64, [4,4], strides=[2,2], padding='same', activation='relu')(inputs)
    conv2 = tf.keras.layers.Conv2D(128, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(conv1)
    norm2 = tf.keras.layers.BatchNormalization()(conv2)
    conv3 = tf.keras.layers.Conv2D(256, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm2)
    norm3 = tf.keras.layers.BatchNormalization()(conv3)
    conv4 = tf.keras.layers.Conv2D(512, [4,4], strides=[2,2], padding='same', activation='relu', use_bias=False)(norm3)
    norm4 = tf.keras.layers.BatchNormalization()(conv4)
    outputs = tf.keras.layers.Conv2D(1, [4,4], strides=[1,1], padding='same')(norm4)
    return tf.keras.models.Model(inputs=inputs, outputs=outputs)

# 定义损失函数
def generator_loss(disc_generated_output, gen_output, target):
    gan_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)(tf.ones_like(disc_generated_output), disc_generated_output)
    l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
    total_gen_loss = gan_loss + (100 * l1_loss)
    return total_gen_loss

def discriminator_loss(disc_real_output, disc_generated_output):
    real_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)(tf.ones_like(disc_real_output), disc_real_output)
    generated_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)(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)

# 定义训练循环
@tf.function
def train_step(input_image, target):
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        # 生成器生成一张假图像
        gen_output = generator(input_image, training=True)

        # 将真实图像和假图像合并
        disc_input = tf.concat([input_image, gen_output], axis=-1)

        # 判别器判别真实图像和假图像
        disc_real_output = discriminator([input_image, target], training=True)
        disc_generated_output = discriminator([input_image, gen_output], training=True)

        # 计算生成器和判别器的损失函数
        gen_loss = generator_loss(disc_generated_output, gen_output, target)
        disc_loss = discriminator_loss(disc_real_output, disc_generated_output)

    # 计算生成器和判别器的梯度并更新模型参数
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 加载数据集
def load_images(path):
    images = []
    for image_path in sorted(tf.io.gfile.glob(path)):
        image = tf.io.read_file(image_path)
        image = tf.image.decode_jpeg(image)
        image = tf.cast(image, tf.float32)
        image = (image / 127.5) - 1
        images.append(image)
    return images

# 定义训练参数
BUFFER_SIZE = 400
BATCH_SIZE = 1
EPOCHS = 200
PATH = './datasets/facades'

# 加载数据集
input_images = load_images(PATH+'/train/*.jpg')
target_images = load_images(PATH+'/train/*.png')

# 将数据集打包在一起
train_dataset = tf.data.Dataset.from_tensor_slices((input_images, target_images)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

# 定义生成器和判别器
generator = generator_model()
discriminator = discriminator_model()

# 训练模型
for epoch in range(EPOCHS):
    print('Epoch', epoch+1)
    for input_image, target in train_dataset:
        train_step(input_image, target)
    if (epoch+1) % 10 == 0:
        # 取一张测试图像进行测试
        test_input = input_images[0]
        test_target = target_images[0]
        test_input = tf.expand_dims(test_input, 0)
        test_target = tf.expand_dims(test_target, 0)
        
        # 生成一张假图像
        test_prediction = generator(test_input, training=True)
        
        # 将图像还原到0到1之间
        test_prediction = (test_prediction + 1) / 2.0
        test_target = (test_target + 1) / 2.0
        
        # 显示结果
        plt.figure(figsize=(15,15))
        display_list = [test_input[0], test_target[0], test_prediction[0]]
        title = ['Input Image', 'Ground Truth', 'Predicted Image']
        for i in range(3):
            plt.subplot(1, 3, i+1)
            plt.title(title[i])
            # 获取图像像素值
            plt.imshow(display_list[i])
            # 隐藏坐标轴
            plt.axis('off')
        plt.show()

在这个示例中，我们定义了一个生成器模型和一个判别器模型，并分别使用二元交叉熵损失函数和Adam优化器进行训练。在训练过程中，我们将图像分别输入到生成器和判别器进行训练，并计算生成器和判别器的损失函数。最后，我们使用matplotlib库将训练过程中的结果可视化展示出来。