python - 在 PyTorch 的 GPU 上使用 DataLoader 训练 CNN 模型
问题描述
我试图将胸部 X 光片分为两类:“正常”和“肺炎”。我的训练和测试集是 DataLoader 对象,带有num_workers=0, pin_memory=True
. Cuda 在我的设备上可用(GTX 1060 6GB)。创建 CNN 后,我调用
model = CNN().cuda()
. 当我尝试训练我得到的模型时RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
。为了在我的 GPU 上训练模型,我必须进行哪些更改?
编码:
root = 'chest_xray/'
train_data = datasets.ImageFolder(os.path.join(root, 'train'), transform=train_transform)
test_data = datasets.ImageFolder(os.path.join(root, 'test'), transform=test_transform)
train_loader = DataLoader(train_data,batch_size=10,shuffle=True,num_workers=0,pin_memory=True)
test_loader = DataLoader(test_data,batch_size=10,shuffle=False,num_workers=0,pin_memory=True)
class_names = train_data.classes
class CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 12, 5, 1)
self.conv2 = nn.Conv2d(12, 24, 5, 1)
self.conv3 = nn.Conv2d(24, 30, 5, 1)
self.conv4 = nn.Conv2d(30, 36, 5, 1)
self.fc1 = nn.Linear(58*58*36, 256)
self.fc2 = nn.Linear(256, 144)
self.fc3 = nn.Linear(144, 84)
self.fc4 = nn.Linear(84, 16)
self.fc5 = nn.Linear(16, 2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv3(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv4(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 58*58*36)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = self.fc5(x)
return F.log_softmax(x, dim=1)
model = CNN().cuda()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
epochs = 2
train_losses = []
train_correct = []
for i in range(epochs):
trn_correct = 0
tst_correct = 0
for b, (X_train, y_train) in enumerate(train_loader):
y_pred = model(X_train)
loss = criterion(y_pred, y_train)
predicted = torch.max(y_pred.data, 1)[1]
batch_correct = (predicted == y_train).sum()
trn_correct += batch_correct
optimizer.zero_grad()
loss.backward()
optimizer.step()
if b % 200 == 0:
print(f'epoch: {i+1} batch: {b} progress: {10*b/len(train_data)} loss: {loss.item()} accuracy: {10*trn_correct/b}%')
train_losses.append(loss)
train_correct.append(trn_correct)
解决方案
在这一行之后:
for b, (X_train, y_train) in enumerate(train_loader):
添加以下内容:
X_train, y_train = X_train.cuda(), y_train.cuda()
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