我们将构建一个端到端的文本到图像的生成系统。这个系统将包括文本编码器、条件GAN的生成器和判别器,以及一个训练循环来优化这些组件。
请注意,以下代码仅作为示例,并不保证能够直接运行,因为它依赖于多个库和未提供的模型实现。此外,为了简化,我们省略了数据预处理、模型保存和加载等部分。
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from text_encoder import TextEncoder
from conditional_gan import ConditionalGAN
from datasets import TextImageDataset# 超参数设置
batch_size = 64
learning_rate = 0.0002
num_epochs = 100
image_size = 128
text_embedding_size = 256# 设备设置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 加载数据集
dataset = TextImageDataset()
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)# 初始化文本编码器和条件GAN
text_encoder = TextEncoder(embedding_size=text_embedding_size).to(device)
conditional_gan = ConditionalGAN(image_size=image_size, embedding_size=text_embedding_size).to(device)# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer_text_encoder = optim.Adam(text_encoder.parameters(), lr=learning_rate)
optimizer_gan = optim.Adam(conditional_gan.parameters(), lr=learning_rate)# 训练循环
for epoch in range(num_epochs):for texts, images in dataloader:# 将数据移动到设备texts = texts.to(device)images = images.to(device)# 文本编码text_embeddings = text_encoder(texts)# 条件GAN生成图像fake_images = conditional_gan(text_embeddings)# 计算GAN损失valid = torch.ones(batch_size, 1).to(device)fake = torch.zeros(batch_size, 1).to(device)real_loss = criterion(conditional_gan.discriminator(images), valid)fake_loss = criterion(conditional_gan.discriminator(fake_images.detach()), fake)d_loss = (real_loss + fake_loss) / 2# 反向传播和优化判别器conditional_gan.discriminator.zero_grad()d_loss.backward()optimizer_gan.step()# 计算生成器损失gen_loss = criterion(conditional_gan.discriminator(fake_images), valid)# 反向传播和优化生成器conditional_gan.generator.zero_grad()gen_loss.backward()optimizer_gan.step()# (可选)更新文本编码器(这里简化,通常与GAN训练分开)# ...# 打印损失和其他指标print(f'Epoch [{epoch+1}/{num_epochs}], d_loss: {d_loss.item():.4f}, gen_loss: {gen_loss.item():.4f}')# 保存模型(可选)# ...# 验证和生成图像(可选)# ...# 生成图像示例
with torch.no_grad():example_text = torch.tensor(["一个美丽的花园,有鲜花和蝴蝶"], dtype=torch.long).to(device)example_embedding = text_encoder(example_text)example_image = conditional_gan.generator(example_embedding)save_image(example_image, "generated_image.png", nrow=1)print("训练完成,生成的图像已保存为 generated_image.png")