def validate(val_loader, model, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (data, target) in enumerate(val_loader):
target = target.cuda(non_blocking=True)
data = data.cuda()
# compute output
output = model(data)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), data.size(0))
top1.update(acc1.item(), data.size(0))
top5.update(acc5.item(), data.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logger.info(
'Test: [{0}/{1}]\t'
'Test Loss {loss.val:.3f} (avg={loss.avg:.3f})\t'
'Prec@1 {top1.val:.3f} (avg={top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} (avg={top5.avg:.3f})'.format(
i, len(val_loader), loss=losses, top1=top1, top5=top5))
logger.info(' * Prec@1 {top1.avg:.5f} Prec@5 {top5.avg:.5f}'.format(
top1=top1, top5=top5))
return top1.avg, top5.avg
def train(train_loader, model, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (data, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(non_blocking=True)
data = data.cuda()
output = model(data)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), data.size(0))
top1.update(acc1.item(), data.size(0))
top5.update(acc5.item(), data.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
lr = optimizer.param_groups[0]["lr"]
if i % 20 == 0:
logger.info(
'Epoch[{0}/{1}] Iter[{2}/{3}]\t'
'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Train Loss {loss.val:.3f} ({loss.avg:.3f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\t'
'LR: {lr:.4f}'.format(epoch,
args.epochs,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=lr))
return losses.avg
def train(train_loader, model, optimizer, lr_scheduler, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
if args.use_dali:
train_loader_len = int(np.ceil(train_loader._size/args.batch_size))
else:
train_loader_len = len(train_loader)
# switch to train mode
model.train()
end = time.time()
for i, data in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.use_dali:
target = torch.cat([i["label"].to(torch.device('cuda:0')) for i in data], dim=0)
data = torch.cat([i["data"].to(torch.device('cuda:0')) for i in data], dim=0)
target = target.cuda().squeeze().long()
else:
data, target = data
data = data.cuda()
target = target.cuda()
output = model(data)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), data.size(0))
top1.update(acc1.item(), data.size(0))
top5.update(acc5.item(), data.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
lr = lr_scheduler.step()
for pg in optimizer.param_groups:
pg["lr"] = lr
# impose L1 penalty to BN factors
if args.sparsity != 0:
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.grad.data.add_(args.sparsity*torch.sign(m.weight.data)) # L1
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
lr = optimizer.param_groups[0]["lr"]
if i % args.print_freq == 0:
logger.info('Epoch[{0}/{1}] Iter[{2}/{3}]\t'
'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Train Loss {loss.val:.3f} ({loss.avg:.3f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\t'
'LR: {lr:.4f}'.format(
epoch, args.epochs, i, train_loader_len,
batch_time=batch_time, data_time=data_time, loss=losses, top1=top1, top5=top5,
lr=lr))
if args.use_dali:
train_loader.reset()
return losses.avg
def validate(val_loader, model, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
if args.use_dali:
val_loader_len = int(val_loader._size / 100)
else:
val_loader_len = len(val_loader)
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, data in enumerate(val_loader):
if args.use_dali:
target = torch.cat([i["label"].to(torch.device('cuda:0')) for i in data], dim=0)
data = torch.cat([i["data"].to(torch.device('cuda:0')) for i in data], dim=0)
target = target.cuda(non_blocking=True).squeeze().long()
else:
data, target = data
data = data.cuda()
target = target.cuda(non_blocking=True)
data = data.cuda()
# compute output
output = model(data)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), data.size(0))
top1.update(acc1.item(), data.size(0))
top5.update(acc5.item(), data.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logger.info('Test: [{0}/{1}]\t'
'Test Loss {loss.val:.3f} (avg={loss.avg:.3f})\t'
'Prec@1 {top1.val:.3f} (avg={top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} (avg={top5.avg:.3f})'.format(
i, val_loader_len, loss=losses, top1=top1, top5=top5))
logger.info(' * Prec@1 {top1.avg:.5f} Prec@5 {top5.avg:.5f}'
.format(top1=top1, top5=top5))
if args.use_dali:
val_loader.reset()
return top1.avg, top5.avg
def train(train_loader, model, optimizer, lr_scheduler, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (data, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(non_blocking=True)
data = data.cuda()
output = model(data)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), data.size(0))
top1.update(acc1.item(), data.size(0))
top5.update(acc5.item(), data.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
# adjust lr
lr = lr_scheduler.step()
for pg in optimizer.param_groups:
pg["lr"] = lr
# impose L1 penalty to BN factors
if args.sparsity != 0:
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.grad.data.add_(args.sparsity*torch.sign(m.weight.data)) # L1
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
lr = optimizer.param_groups[0]["lr"]
if i % args.print_freq == 0:
logger.info('Epoch[{0}/{1}] Iter[{2}/{3}]\t'
'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Train Loss {loss.val:.3f} ({loss.avg:.3f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})\t'
'LR: {lr:.4f}'.format(
epoch, args.epochs, i, len(train_loader),
batch_time=batch_time, data_time=data_time, loss=losses, top1=top1, top5=top5,
lr=lr))
return losses.avg