先决条件
使用官方镜像,已经安装了 nvidia 驱动 和 cuda。
- 查看cuda版本:
cat /usr/local/cuda/version.txt
- 查看nvidia 驱动信息:
nvidia-smi
1. 实例
python 实例如下:
# -*- coding: utf-8 -*-
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
net.cuda()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # 循环遍历数据集多次
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# 得到输入数据
inputs, labels = data
# 包装数据
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
# 梯度清零
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 打印信息
# print(len(loss.data))
running_loss += loss
if i % 2000 == 1999: # 每2000个小批量打印一次
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
使用cpu计算:
去除 net.cuda() ;
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda()) 修改为: inputs, labels = Variable(inputs), Variable(labels)
2. 安装和测试
-
安装pytorch
pip3 install torch==1.0.0 torchvision==0.2.1 Pillow==6.2.2
-
测试运行
python3 test.py