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| all_images = torchvision.datasets.CIFAR10(train=True, root="../data", download=True)
train_augs = torchvision.transforms.Compose([
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor()])
test_augs = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()])
def load_cifar10(is_train, augs, batch_size):
dataset = torchvision.datasets.CIFAR10(root="../data", train=is_train,
transform=augs, download=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=is_train, num_workers=d2l.get_dataloader_workers())
return dataloader
def train_batch_ch13(net, X, y, loss, trainer, devices):
"""用多GPU进行小批量训练"""
if isinstance(X, list):
X = [x.to(devices[0]) for x in X]
else:
X = X.to(devices[0])
y = y.to(devices[0])
net.train()
trainer.zero_grad()
pred = net(X)
l = loss(pred, y)
l.sum().backward()
trainer.step()
train_loss_sum = l.sum()
train_acc_sum = d2l.accuracy(pred, y)
return train_loss_sum, train_acc_sum
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
devices=d2l.try_all_gpus()):
"""用多GPU进行模型训练"""
timer, num_batches = d2l.Timer(), len(train_iter)
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
legend=['train loss', 'train acc', 'test acc'])
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
for epoch in range(num_epochs):
metric = d2l.Accumulator(4)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = train_batch_ch13(
net, features, labels, loss, trainer, devices)
metric.add(l, acc, labels.shape[0], labels.numel())
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[2], metric[1] / metric[3],
None))
test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {metric[0] / metric[2]:.3f}, train acc '
f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
f'{str(devices)}')
batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)
def init_weights(m):
if type(m) in [nn.Linear, nn.Conv2d]:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
def train_with_data_aug(train_augs, test_augs, net, lr=0.001):
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
loss = nn.CrossEntropyLoss(reduction="none")
trainer = torch.optim.Adam(net.parameters(), lr=lr)
train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)
train_with_data_aug(train_augs, test_augs, net)
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