用gan对visdrone数据集进行扩增的代码段

以下是使用GAN对VisDrone数据集进行扩增的代码段：

首先，安装必要的库，如TensorFlow、Keras和NumPy。导入VisDrone数据集并对其进行预处理：

import tensorflow as tf
from tensorflow import keras
import numpy as np
import cv2

# Load VisDrone dataset
visdrone = keras.datasets.visdrone.load_data()

# Preprocess data
x_train, y_train = visdrone[0]
x_test, y_test = visdrone[1]

# Normalize pixel values
x_train, x_test = x_train / 255.0, x_test / 255.0

# Convert labels to one-hot encoding
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)

接下来，定义生成器和判别器模型：

def make_generator_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size

    model.add(keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model

def make_discriminator_model():
    model = keras.Sequential()
    model.add(keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                     input_shape=[28, 28, 1]))
    model.add(keras.layers.LeakyReLU())
    model.add(keras.layers.Dropout(0.3))

    model.add(keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(keras.layers.LeakyReLU())
    model.add(keras.layers.Dropout(0.3))

    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(1))

    return model

然后，定义生成器和判别器的损失函数和优化器：

cross_entropy = keras.losses.BinaryCrossentropy(from_logits=True)

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

generator_optimizer = keras.optimizers.Adam(1e-4)
discriminator_optimizer = keras.optimizers.Adam(1e-4)

最后，训练GAN模型：

@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, 100])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    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))

def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)

        # Generate and save images every 10 epochs
        if epoch % 10 == 0:
            generate_and_save_images(generator,
                                     epoch + 1,
                                     seed)

在训练过程中，使用生成器生成一些新的图片，并将其保存到文件中：

def generate_and_save_images(model, epoch, test_input):
    predictions = model(test_input, training=False)

    fig = plt.figure(figsize=(4, 4))

    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i+1)
        plt.imshow(predictions[i, :, :, 0], cmap='gray')
        plt.axis('off')

    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()

最后，运行训练循环：

EPOCHS = 100
noise_dim = 100
num_examples_to_generate = 16
BATCH_SIZE = 256

# Set up input for image generation
seed = tf.random.normal([num_examples_to_generate, noise_dim])

# Set up GAN model
generator = make_generator_model()
discriminator = make_discriminator_model()

# Train GAN model
train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(60000).batch(BATCH_SIZE)
train(train_dataset, EPOCHS)