def get_dataset(args):
# [-1,1]
Normalize = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
train_transform = MatTransform(args.size, flip=True)
train_set = MatDataset(args.trainList, args.imgDir, args.mskDir, args.alphaDir, normalize=Normalize, transform=train_transform)
train_loader = DataLoader(dataset=train_set, num_workers=args.threads, batch_size=args.batchSize, shuffle=True)
return train_loader
def get_dataset(args):
train_transform = MatTransform(flip=True)
args.crop_h = [int(i) for i in args.crop_h.split(',')]
args.crop_w = [int(i) for i in args.crop_w.split(',')]
train_set = MatDatasetOffline(args, train_transform)
train_loader = DataLoader(dataset=train_set,
num_workers=args.threads,
batch_size=args.batchSize,
shuffle=True)
return train_loader
def get_dataset(args):
train_transform = MatTransform(flip=True)
args.crop_h = [int(i) for i in args.crop_h.split(',')]
args.crop_w = [int(i) for i in args.crop_w.split(',')]
normalize = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])
])
train_set = MatDatasetOffline(args, train_transform, normalize)
train_loader = DataLoader(dataset=train_set, num_workers=args.threads, batch_size=args.batchSize, shuffle=True)
return train_loader