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Getting high accuracy on CIFAR-10 is not straightforward. This self-contained script gets to 94% accuracy with a minimal setup. You can download a model trained with this script from: https://files.joo.st/cifar_model.pt
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import argparse | |
from tqdm import tqdm | |
import torch | |
import torch.nn.functional as F | |
from torchvision import models, datasets, transforms | |
def get_CIFAR10(root="./"): | |
input_size = 32 | |
num_classes = 10 | |
train_transform = transforms.Compose( | |
[ | |
transforms.RandomCrop(32, padding=4), | |
transforms.RandomHorizontalFlip(), | |
transforms.ToTensor(), | |
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), | |
] | |
) | |
train_dataset = datasets.CIFAR10( | |
root + "data/CIFAR10", train=True, transform=train_transform, download=True | |
) | |
test_transform = transforms.Compose( | |
[ | |
transforms.ToTensor(), | |
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), | |
] | |
) | |
test_dataset = datasets.CIFAR10( | |
root + "data/CIFAR10", train=False, transform=test_transform, download=True | |
) | |
return input_size, num_classes, train_dataset, test_dataset | |
class Model(torch.nn.Module): | |
def __init__(self): | |
super().__init__() | |
self.resnet = models.resnet18(pretrained=False, num_classes=10) | |
self.resnet.conv1 = torch.nn.Conv2d( | |
3, 64, kernel_size=3, stride=1, padding=1, bias=False | |
) | |
self.resnet.maxpool = torch.nn.Identity() | |
def forward(self, x): | |
x = self.resnet(x) | |
x = F.log_softmax(x, dim=1) | |
return x | |
def train(model, train_loader, optimizer, epoch): | |
model.train() | |
total_loss = [] | |
for data, target in tqdm(train_loader): | |
data = data.cuda() | |
target = target.cuda() | |
optimizer.zero_grad() | |
prediction = model(data) | |
loss = F.nll_loss(prediction, target) | |
loss.backward() | |
optimizer.step() | |
total_loss.append(loss.item()) | |
avg_loss = sum(total_loss) / len(total_loss) | |
print(f"Epoch: {epoch}:") | |
print(f"Train Set: Average Loss: {avg_loss:.2f}") | |
def test(model, test_loader): | |
model.eval() | |
loss = 0 | |
correct = 0 | |
for data, target in test_loader: | |
with torch.no_grad(): | |
data = data.cuda() | |
target = target.cuda() | |
prediction = model(data) | |
loss += F.nll_loss(prediction, target, reduction="sum") | |
prediction = prediction.max(1)[1] | |
correct += prediction.eq(target.view_as(prediction)).sum().item() | |
loss /= len(test_loader.dataset) | |
percentage_correct = 100.0 * correct / len(test_loader.dataset) | |
print( | |
"Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)".format( | |
loss, correct, len(test_loader.dataset), percentage_correct | |
) | |
) | |
return loss, percentage_correct | |
def main(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--epochs", type=int, default=50, help="number of epochs to train (default: 50)" | |
) | |
parser.add_argument( | |
"--lr", type=float, default=0.05, help="learning rate (default: 0.05)" | |
) | |
parser.add_argument("--seed", type=int, default=1, help="random seed (default: 1)") | |
args = parser.parse_args() | |
print(args) | |
torch.manual_seed(args.seed) | |
input_size, num_classes, train_dataset, test_dataset = get_CIFAR10() | |
kwargs = {"num_workers": 2, "pin_memory": True} | |
train_loader = torch.utils.data.DataLoader( | |
train_dataset, batch_size=128, shuffle=True, **kwargs | |
) | |
test_loader = torch.utils.data.DataLoader( | |
test_dataset, batch_size=5000, shuffle=False, **kwargs | |
) | |
model = Model() | |
model = model.cuda() | |
milestones = [25, 40] | |
optimizer = torch.optim.SGD( | |
model.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4 | |
) | |
scheduler = torch.optim.lr_scheduler.MultiStepLR( | |
optimizer, milestones=milestones, gamma=0.1 | |
) | |
for epoch in range(1, args.epochs + 1): | |
train(model, train_loader, optimizer, epoch) | |
test(model, test_loader) | |
scheduler.step() | |
torch.save(model.state_dict(), "cifar_model.pt") | |
if __name__ == "__main__": | |
main() |
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