diff --git a/utils/data_utils.py b/utils/data_utils.py index 9ecfe0c..ef5a615 100755 --- a/utils/data_utils.py +++ b/utils/data_utils.py @@ -12,6 +12,37 @@ from .autoaugment import AutoAugImageNetPolicy logger = logging.getLogger(__name__) +def get_loader_new(): + + train_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR), + transforms.RandomCrop((600, 600)), #448 + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) + test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) + trainset = emptyJudge(root='./emptyJudge5', is_train=True, transform=train_transform) + testset = emptyJudge(root='./emptyJudge5', is_train=False, transform=test_transform) + + + train_sampler = RandomSampler(trainset) + test_sampler = SequentialSampler(testset) + train_loader = DataLoader(trainset, + sampler=train_sampler, + batch_size=8, + num_workers=4, + drop_last=True, + pin_memory=True) + test_loader = DataLoader(testset, + sampler=test_sampler, + batch_size=8, + num_workers=4, + pin_memory=True) if testset is not None else None + + print('emptyJudge5 getdataloader ok!') + return train_loader, test_loader + def get_loader(args): if args.local_rank not in [-1, 0]: @@ -101,7 +132,7 @@ def get_loader(args): testset = INat2017(args.data_root, 'val', test_transform) elif args.dataset == 'emptyJudge5' or args.dataset == 'emptyJudge4': train_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR), - transforms.RandomCrop((320, 320)), + transforms.RandomCrop((600, 600)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) @@ -109,7 +140,7 @@ def get_loader(args): # transforms.CenterCrop((448, 448)), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) - test_transform = transforms.Compose([transforms.Resize((320, 320), Image.BILINEAR), + test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) trainset = emptyJudge(root=args.data_root, is_train=True, transform=train_transform)