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Update all prec occurrences to acc in imagenet tutorial #420

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Oct 11, 2018
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Update all prec occurrences to acc in imagenet tutorial #420

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38 changes: 19 additions & 19 deletions imagenet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,11 +62,11 @@
parser.add_argument('--gpu', default=None, type=int,
help='GPU id to use.')

best_prec1 = 0
best_acc1 = 0


def main():
global args, best_prec1
global args, best_acc1
args = parser.parse_args()

if args.seed is not None:
Expand Down Expand Up @@ -122,7 +122,7 @@ def main():
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
Expand Down Expand Up @@ -179,16 +179,16 @@ def main():
train(train_loader, model, criterion, optimizer, epoch)

# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
acc1 = validate(val_loader, model, criterion)

# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best)

Expand Down Expand Up @@ -217,10 +217,10 @@ def train(train_loader, model, criterion, optimizer, epoch):
loss = criterion(output, target)

# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))

# compute gradient and do SGD step
optimizer.zero_grad()
Expand All @@ -236,8 +236,8 @@ def train(train_loader, model, criterion, optimizer, epoch):
'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})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@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))

Expand All @@ -263,10 +263,10 @@ def validate(val_loader, model, criterion):
loss = criterion(output, target)

# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))

# measure elapsed time
batch_time.update(time.time() - end)
Expand All @@ -276,12 +276,12 @@ def validate(val_loader, model, criterion):
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))

print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))

return top1.avg
Expand Down