wehub-resource-sync
158 lines
4.5 KiB
Python
158 lines
4.5 KiB
Python
import argparse
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import torch
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import torch.optim as optim
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from model import Net
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from torchvision import datasets, transforms
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def train(args, model, device, train_loader, optimizer, epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = model(data)
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loss = model.margin_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print(
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"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
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epoch,
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batch_idx * len(data),
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len(train_loader.dataset),
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100.0 * batch_idx / len(train_loader),
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loss.item(),
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)
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)
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def test(args, model, device, test_loader):
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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test_loss += model.margin_loss(
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output, target
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).item() # sum up batch loss
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pred = (
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output.norm(dim=2).squeeze().max(1, keepdim=True)[1]
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) # get the index of the max log-probability
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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print(
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"\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
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test_loss,
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correct,
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len(test_loader.dataset),
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100.0 * correct / len(test_loader.dataset),
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)
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)
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def main():
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# Training settings
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parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
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parser.add_argument(
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"--batch-size",
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type=int,
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default=512,
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metavar="N",
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help="input batch size for training (default: 64)",
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)
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parser.add_argument(
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"--test-batch-size",
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type=int,
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default=512,
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metavar="N",
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help="input batch size for testing (default: 1000)",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=10,
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metavar="N",
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help="number of epochs to train (default: 10)",
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)
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parser.add_argument(
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"--lr",
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type=float,
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default=0.01,
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metavar="LR",
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help="learning rate (default: 0.01)",
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)
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parser.add_argument(
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"--no-cuda",
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action="store_true",
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default=False,
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help="disables CUDA training",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=1,
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metavar="S",
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help="random seed (default: 1)",
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)
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parser.add_argument(
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"--log-interval",
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type=int,
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default=10,
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metavar="N",
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help="how many batches to wait before logging training status",
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)
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args = parser.parse_args()
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use_cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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device = torch.device("cuda" if use_cuda else "cpu")
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kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST(
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"../data",
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train=True,
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download=True,
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transform=transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,)),
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]
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),
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),
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batch_size=args.batch_size,
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shuffle=True,
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**kwargs
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)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST(
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"../data",
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train=False,
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transform=transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,)),
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]
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),
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),
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batch_size=args.test_batch_size,
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shuffle=True,
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**kwargs
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)
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model = Net(device=device).to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch)
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test(args, model, device, test_loader)
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if __name__ == "__main__":
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main()
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