根据loss_G = criterion(output, real_label)指令可以看出生成器的损失函数是交叉熵还是平方误差损失函数吗

根据 `loss_G = criterion(output, real_label)` 这个指令无法确定生成器的损失函数是交叉熵还是平方误差损失函数，需要查看 `criterion` 的定义。

一般来说，如果 `criterion` 定义为 `nn.CrossEntropyLoss()`，则损失函数是交叉熵；如果定义为 `nn.MSELoss()`，则损失函数是平方误差损失函数。

相关问题

代码定义一个基于pytorch的encoder-decoder+GAN模型，要求输入128x128x3通道图像，输出128x128x3图像，该模型用cuda训练，添加一个随机添加不规则遮罩的函数，返回遮罩后的图片，训练过程中打印生成器损失、判别器损失以及重建损失，最后将训练好的生成器模型保存

下面是基于PyTorch的Encoder-Decoder GAN模型代码，实现输入128x128x3通道图像，输出128x128x3图像，模型用CUDA训练，并添加随机不规则遮罩函数：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
import numpy as np
import os
from PIL import Image

cuda = True if torch.cuda.is_available() else False

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 512, 3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 1024, 3, stride=2, padding=1),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.2, inplace=True)
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(1024, 512, 5, stride=2, padding=2, output_padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, 5, stride=2, padding=2, output_padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, 5, stride=2, padding=2, output_padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, 5, stride=2, padding=2, output_padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, 5, stride=1, padding=2),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)

        return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 512, 3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 1, 3, stride=1, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)

        return x

def random_mask(img, size=30):
    mask = np.zeros((img.shape[0], img.shape[1], 1), np.uint8)
    mask = cv2.random_shapes.mask(mask, shape='circle', max_shapes=1, min_size=size, max_size=size)[0]
    mask = np.tile(mask, (1, 1, 3))
    mask = mask.astype(np.float32)
    mask = mask / 255.0
    mask = torch.from_numpy(mask)
    
    masked_img = img * (1 - mask)
    
    return masked_img, mask

def train(generator, discriminator, train_loader, criterion, optimizer_g, optimizer_d):
    for epoch in range(num_epochs):
        for i, (input_img, target_img) in enumerate(train_loader):
            input_img = input_img.cuda()
            target_img = target_img.cuda()

            # Train Discriminator
            optimizer_d.zero_grad()

            real_output = discriminator(target_img)
            fake_output = discriminator(generator(input_img))

            real_label = torch.ones(real_output.size()).cuda()
            fake_label = torch.zeros(fake_output.size()).cuda()

            real_loss = criterion(real_output, real_label)
            fake_loss = criterion(fake_output, fake_label)
            d_loss = real_loss + fake_loss

            d_loss.backward()
            optimizer_d.step()

            # Train Generator
            optimizer_g.zero_grad()

            fake_output = discriminator(generator(input_img))
            g_loss = criterion(fake_output, real_label)

            g_loss.backward()
            optimizer_g.step()

            if (i + 1) % 10 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Generator Loss: {:.4f}, Discriminator Loss: {:.4f}'
                      .format(epoch + 1, num_epochs, i + 1, len(train_loader), g_loss, d_loss))

def save_model(generator, name="generator"):
    if not os.path.exists("models/"):
        os.makedirs("models/")

    torch.save(generator.state_dict(), "models/{}.pt".format(name))


if __name__ == '__main__':
    # Hyperparameters
    num_epochs = 50
    learning_rate = 0.0002
    batch_size = 32

    # Load Data
    dataset = ImageFolder(root='data/', transform=transforms.Compose([
        transforms.Resize(128),
        transforms.CenterCrop(128),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ]))

    train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

    # Define Generator and Discriminator
    generator = Generator()
    discriminator = Discriminator()

    if cuda:
        generator.cuda()
        discriminator.cuda()

    # Loss function and optimizer
    criterion = nn.BCELoss()
    optimizer_g = optim.Adam(generator.parameters(), lr=learning_rate, betas=(0.5, 0.999))
    optimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate, betas=(0.5, 0.999))

    # Train model
    train(generator, discriminator, train_loader, criterion, optimizer_g, optimizer_d)

    # Save trained model
    save_model(generator, name="generator")

该代码实现了基于Encoder-Decoder GAN模型的图像生成，同时也添加了随机不规则遮罩函数，并且训练过程中输出生成器、判别器和重建损失，最终保存了训练好的生成器模型。请注意，代码仅为示范，可能还需要根据具体需求进一步修改。

判别器输入为真实图像数据集和带掩膜的破损图像数据集，生成器输入为带掩膜的破损图像数据集，请定义生成器和判别器循环训练的代码，并计算生成器损失、判别器损失、重建损失

循环训练的代码如下：

# 定义生成器
generator = nn.Sequential(
    nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1),
    nn.Tanh()
)

# 定义判别器
discriminator = nn.Sequential(
    nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
    nn.LeakyReLU(0.2),
    nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1),
    nn.BatchNorm2d(64),
    nn.LeakyReLU(0.2),
    nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
    nn.BatchNorm2d(128),
    nn.LeakyReLU(0.2),
    nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=2, padding=1),
    nn.BatchNorm2d(128),
    nn.LeakyReLU(0.2),
    nn.Flatten(),
    nn.Linear(128 * 8 * 8, 1),
    nn.Sigmoid()
)

# 定义损失函数
criterion = nn.BCELoss()

# 定义优化器
generator_optimizer = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 开始循环训练
for epoch in range(num_epochs):
    for i, (real_image, masked_image, mask) in enumerate(dataloader):
        real_image = real_image.to(device)
        masked_image = masked_image.to(device)
        mask = mask.to(device)
        
        # 定义标签
        real_label = torch.ones(real_image.size(0), 1, device=device)
        fake_label = torch.zeros(real_image.size(0), 1, device=device)
        
        # 训练判别器
        discriminator_optimizer.zero_grad()
        output = discriminator(torch.cat((real_image, masked_image), dim=1))
        real_loss = criterion(output, real_label)
        
        fake_image = generator(masked_image)
        output = discriminator(torch.cat((fake_image, masked_image), dim=1))
        fake_loss = criterion(output, fake_label)
        
        d_loss = real_loss + fake_loss
        d_loss.backward()
        discriminator_optimizer.step()
        
        # 训练生成器
        generator_optimizer.zero_grad()
        fake_image = generator(masked_image)
        output = discriminator(torch.cat((fake_image, masked_image), dim=1))
        g_loss = criterion(output, real_label)
        
        reconstructed_image = fake_image * (1 - mask) + masked_image * mask
        r_loss = torch.mean(torch.abs(real_image - reconstructed_image))
        
        total_loss = g_loss + 0.01 * r_loss
        total_loss.backward()
        generator_optimizer.step()
        
        # 输出损失
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], D Loss: {:.4f}, G Loss: {:.4f}, Reconstruct Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(dataloader), d_loss.item(), g_loss.item(), r_loss.item()))

其中，`real_image`为真实图像数据集，`masked_image`为带掩膜的破损图像数据集，`fake_image`为生成器生成的破损图像数据集。循环训练中，先训练判别器，再训练生成器。

生成器损失为 `g_loss`，判别器损失为 `d_loss`，重建损失为 `r_loss`。其中，`total_loss`为总损失，用于更新生成器。