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jblitzar
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github-python

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assert max(args.lr_steps) < args.epochs
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, args.lr_steps, args.gamma)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch,
args.print_freq, args.rank, logger, tb_logger, args.sampled)
lr_scheduler.step()
# evaluate on validation set
prec1, loss = validate(val_loader, model, criterion, args.print_freq,
args.rank, logger, args.sampled)
# remember best prec@1 and save checkpoint
if args.rank == 0:
if tb_logger is not None:
tb_logger.add_scalar('test_acc', prec1, epoch)
tb_logger.add_scalar('test_loss', loss, epoch)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_ckpt(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, args.save_path, epoch + 1, is_best)
if args.sampled:
with ParameterClient(args.tmp_client_id) as client:
client.snapshot('{}_epoch_{}_cls.h5'.format(
args.save_path, epoch + 1))
def train(train_loader,
model,
criterion,
optimizer,
epoch,
print_freq,
rank,
logger,
tb_logger=None,
sampled=None):
batch_time = AverageMeter(10)
data_time = AverageMeter(10)
losses = AverageMeter(10)
top1 = AverageMeter(10)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# target = target.cuda(non_blocking=True)
target = target.cuda()
# compute output
if not sampled:
output = model(input, target)
else:
output, target = model(input, target)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, = accuracy(output, target, topk=(1, ))
losses.update(loss.item())
top1.update(prec1[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()
if i % print_freq == 0 and rank == 0 and logger is not None:
logger.info('Epoch: [{0}][{1}/{2}]\t'
'LR: {3}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch,
i,
len(train_loader),
optimizer.param_groups[0]['lr'],
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))