# ready to go
for epoch in range(args.epochs):
model.train()
if epoch in decreasing_lr:
grad_scale = grad_scale / 8.0
logger("training phase")
for batch_idx, (data, target) in enumerate(train_loader):
indx_target = target.clone()
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = wage_util.SSE(output, target)
loss.backward()
for name, param in list(model.named_parameters())[::-1]:
param.grad.data = wage_quantizer.QG(param.grad.data,
args.wl_grad, grad_scale)
optimizer.step()
for name, param in list(model.named_parameters())[::-1]:
param.data = wage_quantizer.C(param.data, args.wl_weight)
if batch_idx % args.log_interval == 0 and batch_idx > 0:
pred = output.data.max(1)[
1] # get the index of the max log-probability
train_loader, test_loader = dataset.get_cifar10(batch_size=args.batch_size,
num_workers=1)
elif args.dataset == 'cifar100':
train_loader, test_loader = dataset.get_cifar100(
batch_size=args.batch_size, num_workers=1)
elif args.dataset == 'imagenet':
train_loader, test_loader = dataset.get_imagenet(
batch_size=args.batch_size, num_workers=1)
else:
raise ValueError("Unknown dataset type")
assert args.model in ['VGG8', 'DenseNet40', 'ResNet18'], args.model
if args.model == 'VGG8':
from models import VGG
model = VGG.vgg8(args=args, logger=logger)
criterion = wage_util.SSE()
elif args.model == 'DenseNet40':
from models import DenseNet
model = DenseNet.densenet40(args=args, logger=logger)
criterion = wage_util.SSE()
elif args.model == 'ResNet18':
from models import ResNet
model = ResNet.resnet18(args=args, logger=logger)
criterion = torch.nn.CrossEntropyLoss()
else:
raise ValueError("Unknown model type")
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(),