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fixed validate fn to match train fn in imagnet main file #1296

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fixed validate fn to match train fn in imagnet main file #1296

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16 changes: 5 additions & 11 deletions imagenet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -270,7 +270,7 @@ def main_worker(gpu, ngpus_per_node, args):
num_workers=args.workers, pin_memory=True, sampler=val_sampler)

if args.evaluate:
validate(val_loader, model, criterion, args)
validate(val_loader, model, criterion, device, args)
return

for epoch in range(args.start_epoch, args.epochs):
Expand All @@ -281,7 +281,7 @@ def main_worker(gpu, ngpus_per_node, args):
train(train_loader, model, criterion, optimizer, epoch, device, args)

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

scheduler.step()

Expand Down Expand Up @@ -347,21 +347,15 @@ def train(train_loader, model, criterion, optimizer, epoch, device, args):
progress.display(i + 1)


def validate(val_loader, model, criterion, args):
def validate(val_loader, model, criterion, device, args):

def run_validate(loader, base_progress=0):
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(loader):
i = base_progress + i
if args.gpu is not None and torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
if torch.backends.mps.is_available():
images = images.to('mps')
target = target.to('mps')
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)

images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
Expand Down