def validate(val_loader, model, criterion, epoch, log):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
for i, (input, target) in enumerate(val_loader):
if args.use_cuda:
target = target.cuda(async=True)
input = input.cuda()
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy2(output.data, target, topk=(1, 1))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
print_log(
' **VAL** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Error@1 {error1:.3f}'
.format(top1=top1, top5=top5, error1=100 - top1.avg), log)
if args.tensorboard:
log_value('val_loss', losses.avg, epoch)
log_value('val_acc', top1.avg, epoch)
return top1.avg, losses.avg
def train(train_loader, model, criterion, optimizer, epoch, log):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# 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)
if args.use_cuda:
target = target.cuda(async=True)
input = input.cuda()
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy2(output.data, target, topk=(1, 1))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.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()
if i % args.print_freq == 0:
print_log(
' Epoch: [{:03d}][{:03d}/{:03d}] '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Loss {loss.val:.4f} ({loss.avg:.4f}) '
'Prec@1 {top1.val:.3f} ({top1.avg:.3f}) '
'Prec@5 {top5.val:.3f} ({top5.avg:.3f}) '.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5) + time_string(), log)
print_log(
' **Train** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Error@1 {error1:.3f}'
.format(top1=top1, top5=top5, error1=100 - top1.avg), log)
# log to TensorBoard
if args.tensorboard:
log_value('train_loss', losses.avg, epoch)
log_value('train_error', top1.avg, epoch)
return top1.avg, losses.avg
