我尝试在一些猴子图片上训练 gan，但如果尝试训练它，它会因未知原因导致 colab 崩溃。 我使用的是 1370 张 128*128 的猴子图片。
我不知道问题出在哪里，请回复
顺便说一句，运行时是 gpu，所以问题与此无关
from torch import optim
import torchvision
from torchvision import transforms
import torch, torch.nn as nn
batch_size = 4
generic_transform = transforms.Compose([
transforms.ToTensor(),
transforms.ToPILImage(),
transforms.Resize((128,128)),
transforms.ToTensor(),
transforms.Normalize((0., 0., 0.), (6, 6, 6)),
transforms.Grayscale(),
])
trainset=torchvision.datasets.ImageFolder(root='drive/My Drive/monkeys', transform=generic_transform)
trainloader = torch.utils.data.DataLoader(trainset,batch_size=batch_size, shuffle=True)
def _init_weights(m):
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0.0, 0.02)
def gen_noise(noise_shape, n_samples, device='cuda:0'):
return torch.randn(noise_shape, n_samples).to(device)
class Discriminator(nn.Module):
#convolutional discriminator
def __init__(self) -> None:
super(Discriminator, self).__init__()
self.hidden_dim = 64
self.relu = nn.ReLU(inplace=False)
self.sigmoid = nn.Sigmoid()
self.conv_1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=32, stride = 2)
self.maxPooling_1 = nn.MaxPool2d(kernel_size=3)
self.conv_2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=8, stride = 2)
self.maxPooling_2 = nn.MaxPool2d(kernel_size=2)
self.linear_layer = nn.Linear(in_features=self.hidden_dim, out_features=1)
def forward(self, x) -> float:
self.x = x
self.x = self.relu(self.conv_1(self.x))
self.x = self.maxPooling_1(self.x)
self.x = self.relu(self.conv_2(self.x))
self.x = self.maxPooling_2(self.x)
print(self.x.shape)
self.x = self.x.view(self.x.shape[0],
self.x.shape[1]*self.x.shape[2]*self.x.shape[3])
self.x = self.sigmoid(self.linear_layer(self.x))
return self.x
class Generator(nn.Module):
#fully connected generator
def __init__(self, hidden_dim, output_dim, z_dim) -> None:
super(Generator, self).__init__()
self.relu = nn.ReLU(inplace=False)
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.z_dim = z_dim
self.linear_layer_1 = nn.Linear(in_features=self.z_dim, out_features=self.hidden_dim)
self.linear_layer_2 = nn.Linear(in_features=self.hidden_dim, out_features=self.hidden_dim*2)
self.linear_layer_3 = nn.Linear(in_features=self.hidden_dim*2, out_features=self.output_dim)
def forward(self, x) -> torch.tensor:
self.x = x
self.x = self.relu(self.linear_layer_1(self.x))
self.x = self.relu(self.linear_layer_2(self.x))
self.x = self.relu(self.linear_layer_3(self.x))
return self.x
class GAN():
def __init__(self, hidden_dim, output_dim, z_dim, criterion, device="cuda:0") -> None:
if device == "cuda:0":
assert torch.cuda.is_available(), "apply gpu"
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.device = device
self.criterion = criterion
self.z_dim = torch.tensor(z_dim).long()
self.discriminator = Discriminator().to(self.device)
self.d_opt = optim.Adam(self.discriminator.parameters(), lr=0.0001)
self.generator = Generator(hidden_dim=self.hidden_dim, output_dim=self.output_dim, z_dim=self.z_dim).to(self.device)
self.g_opt = optim.Adam(self.generator.parameters(), lr=0.0001)
self.generator = self.generator.apply(_init_weights)
self.discriminator = self.discriminator.apply(_init_weights)
class GAN_Trainer():
def __init__(self, z_dim, model, device="cuda:0") -> None:
self.device = device
self.gan = model
self.z_dim = z_dim
self._d_mean_train_loss = None
self._g_mean_train_loss = None
def train(self, batch) -> None:
print(1)
self.batch = batch.to(self.device)
self.noise = gen_noise(self.batch.shape[0],self.z_dim).to(self.device)
self.gan.g_opt.zero_grad()
self._g_output = self.gan.generator.forward(self.noise.to(self.device))
self._g_output = self._g_output.view(self.batch.shape[0],
1,
torch.sqrt(torch.tensor(self.gan.output_dim)).int(),
torch.sqrt(torch.tensor(self.gan.output_dim)).int())
print(self._g_output.shape)
self._d_for_g_pred = self.gan.discriminator.forward(self._g_output)
self._g_loss = self.gan.criterion(self._d_for_g_pred, torch.zeros_like(self._d_for_g_pred))
self._g_loss.backward()
self.gan.g_opt.step()
self.gan.d_opt.zero_grad()
self._d_fake_pred = self.gan.discriminator.forward(self._g_output)
self._d_fake_loss = self.gan.criterion(self._g_output, torch.zeros_like(self._g_output))
self._d_real_pred = self.gan.discriminator.forward(self.batch)
self._d_real_loss = self.gan.criterion(self.batch, torch.ones_like(self.batch))
self._d_mean_loss = torch.mean(torch.cat((self._d_fake_loss, self._d_real_loss),0))
self._d_mean_loss.backward(retain_graph=True)
self.gan.d_opt.step()
self._d_mean_train_loss = self._d_mean_train_loss + self._d_mean_loss.detach()
self._g_mean_train_loss = self._g_mean_train_loss + self._g_loss.detach()
torch.cuda.empty_cache()
gan = GAN(hidden_dim=1200,
output_dim=16384,
z_dim = 1000,
criterion=nn.BCEWithLogitsLoss())
trainer = GAN_Trainer(model=gan, z_dim=1000)
#here is where it crashes
from tqdm import trange
torch.cuda.empty_cache()
image = trainset[0][0].to("cuda:0").view(1,
trainset[0][0].shape[0],
trainset[0][0].shape[1],
trainset[0][0].shape[2])
trainer.train(batch=image)
请帮忙！我已经开始失去理智了，谢谢！ <3
答案 0 :(得分：0)
我对您的代码进行了一些调试，发现崩溃发生在一行：
<ZipInfo filename='fake_xml.xml' compress_type=deflate external_attr=0x20 file_size=122 compress_size=114>
我试图找出它崩溃的原因，看起来你的操作没有正确完成，一些就地操作正在改变图形并导致 pytorch 出现故障。
在 GAN 逻辑方面，您需要更改代码中的一些主要问题。这里：
self._d_mean_loss = torch.mean(torch.cat((self._d_fake_loss, self._d_real_loss),0))
真正的损失应该基于判别器 self._d_real_pred 的输出。一般来说，你应该有三个向后调用：
我强烈推荐本教程：https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html。这是一个很好的教程，可以帮助您了解 GAN 是如何训练的。
最后，我将删除所有 self.... 存储在 forward 和 train 函数中。任何操作都完成了张量通过将张量存储为成员来更新图，修改它们可能会导致问题并导致梯度擦除等。