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jblitzar
/
github-python

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# init data loader
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.25, 0.25, 0.25])
train_dataset = FileListDataset(
args.train_filelist, args.train_prefix,
transforms.Compose([
transforms.Resize(args.input_size),
transforms.ToTensor(),
normalize,
]))
val_dataset = FileListDataset(
args.val_filelist, args.val_prefix,
transforms.Compose([
transforms.Resize(args.input_size),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
val_sampler = DistSequentialSampler(val_dataset, args.world_size,
args.rank)
else:
train_sampler = None
val_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
if args.test_batch_size is None:
args.test_batch_size = 2 * args.batch_size
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.test_batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
sampler=val_sampler)
# create model
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](feature_dim=args.feature_dim)
if args.sampled:
if args.rank > 0:
assert args.distributed
assert args.sample_num <= args.num_classes
model = models.build_classifier(args.classifier_type, model,
**args.__dict__)
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model, [args.rank])
print('create DistributedDataParallel model successfully', args.rank)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
args.start_epoch, best_prec1 = load_ckpt(args.resume,
model,
optimizer=optimizer)
if args.sampled:
with ParameterClient(args.tmp_client_id) as client:
cls_resume = args.resume.replace('.pth.tar', '_cls.h5')
if os.path.isfile(cls_resume):
client.resume(cls_resume)
print("=> loaded checkpoint '{}' (epoch {})".format(
cls_resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(
cls_resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
if args.evaluate:
validate(val_loader, model, criterion, args.print_freq, args.rank,
logger, args.sampled)
return