Pytorch-GPU我忘记移动到GPU吗?

Question

我得到这个错误。我遗漏了什么，我觉得我已经厌倦了一切。也没有一个简单的方法来使用GPU和CPU我觉得我没有试过所有这些选项。如不在任何地方使用。cuda()

这是我的第一个中立网络，所以请对我宽容。(大部分来自Class)

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking arugment for argument target in method wrapper_nll_loss_forward)

import torch.cuda
import numpy as np
import time
from torchvision import datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt 
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
torch.cuda.set_device(0)
def load_data():
num_workers = 0
load_data.batch_size = 20
transform = transforms.ToTensor()

train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)
test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)

load_data.train_loader = torch.utils.data.DataLoader(train_data, 
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data, 
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True)

def visualize():
dataiter = iter(load_data.train_loader)
visualize.images, labels = dataiter.next()
visualize.images = visualize.images.numpy()
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(load_data.batch_size):
ax = fig.add_subplot(2, load_data.batch_size/2, idx+1, xticks=[], yticks=[])
ax.imshow(np.squeeze(visualize.images[idx]), cmap='gray')
ax.set_title(str(labels[idx].item()))
#plt.show()
def fig_values():
img = np.squeeze(visualize.images[1])
fig = plt.figure(figsize = (12,12))
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
width, height = img.shape
thresh = img.max()/2.5
for x in range(width):
for y in range(height):
val = round(img[x][y],2) if img[x][y] !=0 else 0
ax.annotate(str(val), xy=(y,x),
horizontalalignment='center',
verticalalignment='center',
color='white' if img[x][y]<thresh else 'black')
#plt.show()

load_data()
visualize()
fig_values()
class NeuralNet(nn.Module):
def __init__(self, gpu = True):
super(NeuralNet, self ).__init__()
self.fc1 = nn.Linear(28 * 28, 16).cuda()
self.fc2 = nn.Linear(16, 10).cuda()
def forward(self, x):
x = x.view(-1, 28 * 28).cuda()
x = F.relu(self.fc1(x)).cuda()
x = self.fc2(x).cuda()
return x

def training():
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
n_epochs = 100 
model.train().cuda()
for epoch in range(n_epochs):
train_loss = 0.0
for data, target in load_data.train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()*data.size(0)
train_loss = train_loss/len(load_data.train_loader.dataset)
print('Epoch: {} tTraining Loss: {:.6f}'.format(
epoch+1, 
train_loss
))

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet().to(device)
summary(model, input_size=(1, 28, 28))
training()

Answer 1

您的data和target不在GPU中(考虑移除重复的cuda调用)。

你也在做不必要的cuda()，这是不需要的。简单地说，看看你的数据和模型在哪里。将模型送到GPU，将数据和标签送到GPU，最后将数据馈送给模型。

不要使用cuda()，使用to.device()，从长远来看更安全，在多gpu设置中更容易定制。

import torch.cuda
import numpy as np
import time
from torchvision import datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt 
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
torch.cuda.set_device(0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def load_data():
num_workers = 0
load_data.batch_size = 20
transform = transforms.ToTensor()

train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)
test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)

load_data.train_loader = torch.utils.data.DataLoader(train_data, 
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data, 
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True)

def visualize():
dataiter = iter(load_data.train_loader)
visualize.images, labels = dataiter.next()
visualize.images = visualize.images.numpy()
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(load_data.batch_size):
ax = fig.add_subplot(2, load_data.batch_size/2, idx+1, xticks=[], yticks=[])
ax.imshow(np.squeeze(visualize.images[idx]), cmap='gray')
ax.set_title(str(labels[idx].item()))
#plt.show()
def fig_values():
img = np.squeeze(visualize.images[1])
fig = plt.figure(figsize = (12,12))
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
width, height = img.shape
thresh = img.max()/2.5
for x in range(width):
for y in range(height):
val = round(img[x][y],2) if img[x][y] !=0 else 0
ax.annotate(str(val), xy=(y,x),
horizontalalignment='center',
verticalalignment='center',
color='white' if img[x][y]<thresh else 'black')
#plt.show()

load_data()
visualize()
fig_values()
class NeuralNet(nn.Module):
def __init__(self, gpu = True):
super(NeuralNet, self ).__init__()
self.fc1 = nn.Linear(28 * 28, 16)
self.fc2 = nn.Linear(16, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x

def training():
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
n_epochs = 100 
model.train()
for epoch in range(n_epochs):
train_loss = 0.0
for data, target in load_data.train_loader:
optimizer.zero_grad()
###################################
data = data.to(device)
target = target.to(device)
###################################
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()*data.size(0)
train_loss = train_loss/len(load_data.train_loader.dataset)
print('Epoch: {} tTraining Loss: {:.6f}'.format(
epoch+1, 
train_loss
))


model = NeuralNet().to(device)
summary(model, input_size=(1, 28, 28))
training()

Answer 2

显然你的target变量不在GPU上。

同时，在forward()

内部调用.cuda()也是一个坏主意

def forward(self, x):
x = x.view(-1, 28 * 28).cuda() # BAD
x = F.relu(self.fc1(x)).cuda() # BAD
x = self.fc2(x).cuda() #BAD
return x

更确切地说，删除所有.cuda()内部的forward并执行主循环

for data, target in load_data.train_loader:
data = data.cuda()
target = target.cuda()