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Fix conditions about using single GPU #841

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Fix conditions about using single GPU #841

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1413314
fixed conditions about using single GPU
sousquared Nov 11, 2020
4ab1db6
fix imagenet/main.py for single GPU machine
sousquared Nov 13, 2020
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6 changes: 4 additions & 2 deletions imagenet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -159,6 +159,8 @@ def main_worker(gpu, ngpus_per_node, args):
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
elif torch.cuda.device_count() == 1:
model = model.cuda()
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
Expand Down Expand Up @@ -281,7 +283,7 @@ def train(train_loader, model, criterion, optimizer, epoch, args):
# measure data loading time
data_time.update(time.time() - end)

if args.gpu is not None:
if args.gpu is not None or torch.cuda.device_count() == 1:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
Expand Down Expand Up @@ -325,7 +327,7 @@ def validate(val_loader, model, criterion, args):
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
if args.gpu is not None:
if args.gpu is not None or torch.cuda.device_count() == 1:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
Expand Down