PyTorch期望CPU得到CUDA张量

我一直在努力查找代码中的错误。我正在尝试实现DCGAN论文，在过去的一个小时里，我经历了这些错误。有人能帮我修一下吗？

我在谷歌colab上用GPU运行时训练这个，但我得到了这个错误。昨天，我实现了Ian Goodfellow的第一篇GAN论文，但我没有得到这个错误。我不知道发生了什么事，如果有任何帮助，我将不胜感激。此外，请检查gen_input是否正确。

这是代码：

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torchvision.utils import save_image
import torch.optim as optim
#---------------configuration part------------------#
lr = 0.00002 #learning rate
nc = 3 #color channels
nz = 100 #size of latent vector or size of generator input
ngf = 64 #size of feature maps in generator
ndf = 64 #size of feature maps in discriminator
height = 128 #height of the image
width = 128 #width of the image
num_epochs = 100 #the variable name tells everything
workers = 2 #number of workers to load the data in batches
batch_size = 64 #batch size
image_size = 128 #resizing parameter
root = '/content/gdrive/My Drive/sharingans/' #path to the training directory
beta1 = 0.4
#---------------------------------------------------#
#define the shape of the image
img_shape = (nc, height, width)
#---------------------------------------------------#
#define the weights initialization function
#in the DCGAN paper they state that all weights should be
#randomly initialize weights from normal distribution
#the following function does that
def weights_init(m):
classname = m.__class__.__name__ #returns the class name(eg: Conv2d or ConvTranspose2d)
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02) #0.0 is mean and 0.02 is standard deviation
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1, 0.02) #1 is mean and 0.02 is standard deviation
nn.init.constant_(m.bias.data, 0.0)
#---------------------------------------------------#

#implement the data loader function to load images
def load_data(image_size, root):
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.486, 0.486, 0.486), (0.486, 0.486, 0.486))
])
train_set = torchvision.datasets.ImageFolder(root = root, transform = transform)
return train_set
train_set = load_data(128, root)
#getting the batches of data
train_data = torch.utils.data.DataLoader(train_set, batch_size = batch_size, shuffle = True, num_workers = workers)
#---------------------------------------------------#
#implement the generator network
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.convt1 = nn.ConvTranspose2d(in_channels = nz, out_channels = ngf*8, kernel_size = 4, stride = 1, padding = 0, bias = False)
self.convt2 = nn.ConvTranspose2d(in_channels = ngf*8, out_channels = ngf*4, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.convt3 = nn.ConvTranspose2d(in_channels = ngf*4, out_channels = ngf*2, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.convt4 = nn.ConvTranspose2d(in_channels = ngf*2, out_channels = ngf, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.convt5 = nn.ConvTranspose2d(in_channels = ngf, out_channels = 3, kernel_size=4, stride = 2, padding = 1, bias = False)
def forward(self, t):
t = self.convt1(t)
t = nn.BatchNorm2d(t)
t = F.relu(t)
t = self.convt2(t)
t = nn.BatchNorm2d(t)
t = F.relu(t)
t = self.convt3(t)
t = nn.BatchNorm2d(t)
t = F.relu(t)
t = self.convt4(t)
t = nn.BatchNorm2d(t)
t = F.relu(t)
t = self.convt5(t)
t = F.tanh(t)
return t
#---------------------------------------------------#
#implement the discriminator network
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels = 3, out_channels = ndf, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.conv2 = nn.Conv2d(in_channels = ndf, out_channels = ndf*2, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.conv3 = nn.Conv2d(in_channels = ndf*2, out_channels = ndf*4, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.conv4 = nn.Conv2d(in_channels = ndf*4, out_channels = ndf*8, kernel_size = 4, stride = 2, padding = 1, bias = False)
self.conv5 = nn.Conv2d(in_channels = ndf*8, out_channels = 1, kernel_size = 4, stride = 1, padding = 0, bias = False)
def forward(self, t):
t = self.conv1(t)
t = F.leaky_relu(t, 0.2)
t = self.conv2(t)
t = nn.BatchNorm2d(t)
t = F.leaky_relu(t, 0.2)
t = self.conv3(t)
t = nn.BatchNorm2d(t)
t = F.leaky_relu(t, 0.2)
t = self.conv4(t)
t = nn.BatchNorm2d(t)
t = F.leaky_relu(t, 0.2)
t = self.conv5(t)
t = F.sigmoid(t)
return t
#---------------------------------------------------#
#create the instances of networks
generator = Generator()
discriminator = Discriminator()
#apply the weights_init function to randomly initialize weights to mean = 0 and std = 0.02
generator.apply(weights_init)
discriminator.apply(weights_init)
print(generator)
print(discriminator)
#---------------------------------------------------#
#define the loss function
criterion = nn.BCELoss()
#fixed noise
noise = torch.randn(64, nz, 1, 1).cuda()
#conventions for fake and real labels
real_label = 1
fake_label = 0
#create the optimizer instances
optimizer_d = optim.Adam(discriminator.parameters(), lr = lr, betas = (beta1, 0.999))
optimizer_g = optim.Adam(generator.parameters(), lr = lr, betas = (beta1, 0.999))
#---------------------------------------------------#
if torch.cuda.is_available():
generator = generator.cuda()
discriminator = discriminator.cuda()
criterion = criterion.cuda()
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
#---------------------------------------------------#
#Training loop
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_data):

#ones is passed when the data is coming from original dataset
#zeros is passed when the data is coming from generator
ones = Tensor(images.size(0), 1).fill_(1.0)
zeros = Tensor(images.size(0),1).fill_(0.0)

real_images = images.cuda()

optimizer_g.zero_grad()

#following is the input to the generator
#we create tensor with random noise of size 100
gen_input = np.random.normal(0,3,(512,100,4,4))
gen_input = torch.tensor(gen_input, dtype = torch.float32)
gen_input = gen_input.cuda()
#we then pass it to generator()
gen = generator(gen_input) #this returns a image

#now calculate the loss wrt to discriminator output
g_loss = criterion(discriminator(gen), ones)

#backpropagation
g_loss.backward()
#update weights
optimizer_g.step()

#above was for generator network

#now for the discriminator network
optimizer_d.zero_grad()

#calculate the real loss
real_loss = criterion(discriminator(real_images), ones)
#calculate the fake loss from the generated image
fake_loss = criterion(discriminator(gen.detach()),zeros)
#average out the losses
d_loss = (real_loss + fake_loss)/2

#backpropagation
d_loss.backward()
#update weights
optimizer_d.step()

if i%100 == 0:
print("[EPOCH %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"%(epoch, epochs, i, len(dataset), d_loss.item(), g_loss.item()))
total_batch = epoch * len(dataset) + i
if total_batch%20 == 0:
save_image(gen.data[:5], '/content/gdrive/My Drive/tttt/%d.png' % total_batch, nrow=5)

这是一个错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-36-0af32f223344> in <module>()
18         gen_input = gen_input.cuda()
19         #we then pass it to generator()
---> 20         gen = generator(gen_input) #this returns a image
21 
22         #now calculate the loss wrt to discriminator output
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/batchnorm.py in __init__(self, num_features, eps, momentum, affine, track_running_stats)
40         self.track_running_stats = track_running_stats
41         if self.affine:
---> 42             self.weight = Parameter(torch.Tensor(num_features))
43             self.bias = Parameter(torch.Tensor(num_features))
44         else:
TypeError: expected CPU (got CUDA)

如有任何帮助，我们将不胜感激。非常感谢。