RuntimeError:输入类型(torch.FloatTensor)和权重类型(torch.cuda.FloatTensor)应该相同 [英] RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

问题描述

我正在尝试如下训练以下CNN，但对于.cuda()，我一直收到相同的错误，并且不确定如何解决.到目前为止，这是我的大部分代码.

I am trying to train the following CNN as follows, but I keep getting the same error regarding .cuda() and I am not sure how to fix it. Here is a chunk of my code so far.

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:

However, I keep getting this error in the console:

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

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same`

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，但不确定如何修复它.谢谢！

Any thoughts on how to fix it? I read that maybe the model hasn't been pushed into my GPU, but not sure how to fix it. Thanks!

推荐答案

您会收到此错误，因为您的模型在GPU上，但数据在CPU上.因此，您需要将输入张量发送到CUDA.

You get this error because your model is on the GPU but your data is on the CPU. So, you need to send your input tensors to CUDA.

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

或者像这样，与其余代码保持一致:

Or like this, to stay consistent with the rest of your code:

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

如果您的数据在CUDA上，但会弹出相同的错误消息，但您的模型不在.在这种情况下，您需要将模型发送到CUDA.

The same error message will pop up if your data is on CUDA, but your model isn't. In this case, you need to send your model to CUDA.

model = MyModel()

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

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