text stringlengths 1 93.6k |
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else:
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print("=> creating model '{}'".format(args.arch))
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model = models.__dict__[args.arch]()
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if not torch.cuda.is_available():
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print('using CPU, this will be slow')
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elif args.distributed:
|
# For multiprocessing distributed, DistributedDataParallel constructor
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# should always set the single device scope, otherwise,
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# DistributedDataParallel will use all available devices.
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if args.gpu is not None:
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torch.cuda.set_device(args.gpu)
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model.cuda(args.gpu)
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# When using a single GPU per process and per
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# DistributedDataParallel, we need to divide the batch size
|
# ourselves based on the total number of GPUs we have
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args.batch_size = int(args.batch_size / ngpus_per_node)
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args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
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else:
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model.cuda()
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# DistributedDataParallel will divide and allocate batch_size to all
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# available GPUs if device_ids are not set
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model = torch.nn.parallel.DistributedDataParallel(model)
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elif args.gpu is not None:
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torch.cuda.set_device(args.gpu)
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model = model.cuda(args.gpu)
|
else:
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# DataParallel will divide and allocate batch_size to all available GPUs
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if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
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model.features = torch.nn.DataParallel(model.features)
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model.cuda()
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else:
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model = torch.nn.DataParallel(model).cuda()
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# define loss function (criterion) and optimizer
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criterion = nn.CrossEntropyLoss().cuda(args.gpu)
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optimizer = torch.optim.SGD(model.parameters(), args.lr,
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momentum=args.momentum,
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weight_decay=args.weight_decay)
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# optionally resume from a checkpoint
|
if args.resume:
|
if os.path.isfile(args.resume):
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print("=> loading checkpoint '{}'".format(args.resume))
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if args.gpu is None:
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checkpoint = torch.load(args.resume)
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else:
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# Map model to be loaded to specified single gpu.
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loc = 'cuda:{}'.format(args.gpu)
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checkpoint = torch.load(args.resume, map_location=loc)
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args.start_epoch = checkpoint['epoch']
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best_acc1 = checkpoint['best_acc1']
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if args.gpu is not None:
|
# best_acc1 may be from a checkpoint from a different GPU
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best_acc1 = best_acc1.to(args.gpu)
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model.load_state_dict(checkpoint['state_dict'])
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optimizer.load_state_dict(checkpoint['optimizer'])
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print("=> loaded checkpoint '{}' (epoch {})"
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.format(args.resume, checkpoint['epoch']))
|
else:
|
print("=> no checkpoint found at '{}'".format(args.resume))
|
cudnn.benchmark = True
|
# Data loading code
|
traindir = os.path.join(args.data, 'train')
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valdir = os.path.join(args.data, 'val')
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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train_dataset = datasets.ImageFolder(
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traindir,
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transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
|
transforms.ToTensor(),
|
normalize,
|
]))
|
if args.distributed:
|
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
|
else:
|
train_sampler = None
|
train_loader = torch.utils.data.DataLoader(
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train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
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num_workers=args.workers, pin_memory=True, sampler=train_sampler)
|
val_loader = torch.utils.data.DataLoader(
|
datasets.ImageFolder(valdir, transforms.Compose([
|
transforms.Resize(256),
|
transforms.CenterCrop(224),
|
transforms.ToTensor(),
|
normalize,
|
])),
|
batch_size=args.batch_size, shuffle=False,
|
num_workers=args.workers, pin_memory=True)
|
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