我是新手，学习PyTorch并在CIFAR10上进行了一些教程。由于目前没有GPU可以使用，我选择使用Google Colab进行实验。
我已经成功训练了神经网络，但不确定我的代码是否使用了来自Colab的GPU，因为与我的2014年款 MacBook Pro（没有GPU）相比，Colab所需的训练时间并没有显著加快。
我检查过笔记本电脑，确实运行的是Tesla K80，但一些训练速度非常慢。因此，我认为可能是我的代码没有使用GPU语法，但我无法弄清楚哪部分是出了问题的。
我已经成功训练了神经网络，但不确定我的代码是否使用了来自Colab的GPU，因为与我的2014年款 MacBook Pro（没有GPU）相比，Colab所需的训练时间并没有显著加快。
我检查过笔记本电脑，确实运行的是Tesla K80，但一些训练速度非常慢。因此，我认为可能是我的代码没有使用GPU语法，但我无法弄清楚哪部分是出了问题的。
# install PyTorch
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())
accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'
!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.0-{platform}-linux_x86_64.whl torchvision
import torch
import torch.nn as nn
from torch.optim import Adam
from torchvision import transforms
from torch.autograd import Variable
import torchvision.datasets as datasets
from torch.utils.data import DataLoader, TensorDataset
import matplotlib.pyplot as plt
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
# hyperparameters
n_epochs = 50
n_batch_size = 200
n_display_step = 200
n_learning_rate = 1e-3
n_download_cifar = True
# import cifar
# more about cifar https://www.cs.toronto.edu/~kriz/cifar.html
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.CIFAR10(
root="../datasets/cifar",
train=True,
transform=transform,
download=n_download_cifar)
test_dataset = datasets.CIFAR10(
root="../datasets/cifar",
train=False,
transform=transform)
# create data loader
train_loader = DataLoader(train_dataset, batch_size=n_batch_size, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=n_batch_size, shuffle=False)
# build CNN
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
# (3, 32, 32)
self.conv1 = nn.Sequential(
nn.Conv2d(3, 32, 5, 1, 2),
nn.ReLU(),
nn.MaxPool2d(2, 2))
# (32, 16, 16)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 16, 5, 1, 2),
nn.ReLU(),
nn.MaxPool2d(2, 2))
# (16, 8, 8)
self.out = nn.Linear(16 * 8 * 8, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
out = self.out(x)
return out
net = CNN()
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = Adam(net.parameters(), lr=n_learning_rate)
def get_accuracy(model, loader):
model.eval()
n_samples = 0
n_correct = 0
with torch.no_grad():
for step, (x, y) in enumerate(loader):
x, y = Variable(x).to(device), Variable(y).to(device)
out = model(x)
_, pred = torch.max(out, 1)
n_samples += y.size(0)
n_correct += (pred == y).sum().item()
return n_correct / n_samples
def train(model, criterion, optimizer, epochs, train_loader, test_loader):
for epoch in range(epochs):
for step, (x, y) in enumerate(train_loader):
model.train()
x, y = Variable(x).to(device), Variable(y).to(device)
out = model(x)
loss = criterion(out, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % n_display_step == 0:
print("Epoch {:2d} Loss {:.4f} Accuracy (Train | Test) {:.4f} {:.4f}".format(epoch, loss.item(), get_accuracy(model, train_loader), get_accuracy(model, test_loader)))
train(net, criterion, optimizer, n_epochs, train_loader, test_loader)