RuntimeError：输入类型（torch.FloatTensor）和权重类型（torch.cuda.FloatTensor）应该相同

我正在尝试按照以下方法训练以下CNN，但关于.cuda（），我一直遇到相同的错误，并且不确定如何解决它。到目前为止，这是我的大部分代码。

import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms, models
from torch.utils.data.sampler import SubsetRandomSampler


data_dir = "/home/ubuntu/ML2/ExamII/train2/"
valid_size = .2

# Normalize the test and train sets with torchvision
train_transforms = transforms.Compose([transforms.Resize(224),
                                           transforms.ToTensor(),
                                           ])

test_transforms = transforms.Compose([transforms.Resize(224),
                                          transforms.ToTensor(),
                                          ])

# ImageFolder class to load the train and test images
train_data = datasets.ImageFolder(data_dir, transform=train_transforms)
test_data = datasets.ImageFolder(data_dir, transform=test_transforms)


# Number of train images
num_train = len(train_data)
indices = list(range(num_train))
# Split = 20% of train images
split = int(np.floor(valid_size * num_train))
# Shuffle indices of train images
np.random.shuffle(indices)
# Subset indices for test and train
train_idx, test_idx = indices[split:], indices[:split]
# Samples elements randomly from a given list of indices
train_sampler = SubsetRandomSampler(train_idx)
test_sampler = SubsetRandomSampler(test_idx)
# Batch and load the images
trainloader = torch.utils.data.DataLoader(train_data, sampler=train_sampler, batch_size=1)
testloader = torch.utils.data.DataLoader(test_data, sampler=test_sampler, batch_size=1)


#print(trainloader.dataset.classes)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.resnet50(pretrained=True)

model.fc = nn.Sequential(nn.Linear(2048, 512),
                                 nn.ReLU(),
                                 nn.Dropout(0.2),
                                 nn.Linear(512, 10),
                                 nn.LogSigmoid())
                                 # nn.LogSoftmax(dim=1))
# criterion = nn.NLLLoss()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.003)
model.to(device)

#Train the network
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

但是，我一直在控制台中收到此错误：

RuntimeError：输入类型（torch.FloatTensor）和重量类型（torch.cuda.FloatTensor）应该相同。

关于如何解决它的任何想法？我读到该模型可能尚未推送到我的GPU中，但不确定如何修复。谢谢！

之所以会出现此错误，是因为您的模型位于GPU上，而数据位于CPU上。因此，您需要将输入张量发送到CUDA。

inputs, labels = data
inputs, labels = inputs.cuda(), labels.cuda() # add this line

或者像这样，与其他代码保持一致：

inputs, labels = inputs.to(device), labels.to(device)

在同样的错误，如果你的数据在CUDA，但你的模型是不是消息将弹出。在这种情况下，您需要将模型发送到CUDA。

model = MyModel()

if torch.cuda.is_available():
    model.cuda()