解决Pytorch 训练与测试时爆显存(out of memory)的问题

当使用PyTorch进行训练和测试时，可能会遇到显存不足的问题。这种情况通常会导致程序崩溃或无法正常运行。以下是解决PyTorch训练和测试时显存不足问题的完整攻略，包括两个示例说明。

1. 示例1：使用PyTorch的DataLoader进行批量加载数据

当训练和测试数据集非常大时，可能会导致显存不足的问题。为了解决这个问题，可以使用PyTorch的DataLoader进行批量加载数据。以下是使用PyTorch的DataLoader进行批量加载数据的步骤：

导入必要的库

python import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms

定义数据预处理

python transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

加载数据集

```python
trainset = torchvision.datasets.ImageFolder(root='./data/train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

testset = torchvision.datasets.ImageFolder(root='./data/test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
```

定义AlexNet模型

```python
class AlexNet(nn.Module):
def init(self, num_classes=1000):
super(AlexNet, self).init()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)

   def forward(self, x):
       x = self.features(x)
       x = self.avgpool(x)
       x = torch.flatten(x, 1)
       x = self.classifier(x)
       return x

```

定义损失函数和优化器

python net = AlexNet() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

训练模型

```python
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

print('Finished Training')
```

测试模型

```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```

2. 示例2：使用PyTorch的半精度浮点数（FP16）进行训练和测试

另一种解决显存不足问题的方法是使用PyTorch的半精度浮点数（FP16）进行训练和测试。这种方法可以减少显存的使用量，从而避免显存不足的问题。以下是使用PyTorch的半精度浮点数（FP16）进行训练和测试的步骤：

导入必要的库

python import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms

定义数据预处理

python transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

加载数据集

```python
trainset = torchvision.datasets.ImageFolder(root='./data/train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

testset = torchvision.datasets.ImageFolder(root='./data/test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
```

定义AlexNet模型

   def forward(self, x):
       x = self.features(x)
       x = self.avgpool(x)
       x = torch.flatten(x, 1)
       x = self.classifier(x)
       return x

```

定义损失函数和优化器

python net = AlexNet() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

将模型转换为FP16

python net = net.half() criterion = criterion.half()

训练模型

```python
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs = inputs.half()
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

print('Finished Training')
```

测试模型

```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images = images.half()
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```

以上就是解决PyTorch训练和测试时显存不足问题的完整攻略，包括两个示例说明。

本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：解决Pytorch 训练与测试时爆显存(out of memory)的问题 - Python技术站