train_tfms = albu.Compose([ albu.RandomScale([0.75, 2], interpolation=cv2.INTER_CUBIC, always_apply=True), albu.RandomCrop(1024, 512), albu.HorizontalFlip(), # albu.HueSaturationValue(), albu.Normalize(), ToTensor(), ]) val_tfms = albu.Compose([ albu.Normalize(), ToTensor(), ]) dataset_dir = get_datasets_root('cityscapes') train_dataset = Cityscapes(dataset_dir, split='train', transforms=train_tfms) val_dataset = Cityscapes(dataset_dir, split='val', transforms=val_tfms) sampler_args = dict(world_size=world_size, local_rank=local_rank, enable=distributed) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=8, sampler=create_sampler(train_dataset, **sampler_args), shuffle=not distributed, )
crop_size = args.crop_size train_tfms = albu.Compose([ albu.RandomScale([0.5, 2.0]), albu.RandomCrop(crop_size, crop_size), albu.HorizontalFlip(), albu.HueSaturationValue(), albu.Normalize(), ToTensor(), ]) val_tfms = albu.Compose([ albu.Normalize(), ToTensor(), ]) dataset_dir = get_datasets_root('bdd100k/seg') train_dataset = BDDSegmentation(dataset_dir, split='train', transforms=train_tfms) val_dataset = BDDSegmentation(dataset_dir, split='val', transforms=val_tfms) sampler_args = dict(world_size=world_size, local_rank=local_rank, enable=distributed) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=8, sampler=create_sampler(train_dataset, **sampler_args), shuffle=not distributed,
albu.RandomScale([0.5, 1.5]), albu.PadIfNeeded(crop_size, crop_size), albu.RandomCrop(crop_size, crop_size), albu.HorizontalFlip(), albu.HueSaturationValue(), albu.Normalize(), ToTensor(), ]) val_tfms = albu.Compose([ albu.PadIfNeeded(crop_size, crop_size), albu.CenterCrop(crop_size, crop_size), albu.Normalize(), ToTensor(), ]) dataset_dir = get_datasets_root('coco') train_dataset = COCOStuff(dataset_dir, split='train', transforms=train_tfms) val_dataset = COCOStuff(dataset_dir, split='val', transforms=val_tfms) sampler_args = dict(world_size=world_size, local_rank=local_rank, enable=distributed) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=4, sampler=create_sampler(train_dataset, **sampler_args), shuffle=not distributed,
albu.Resize(256, 256), albu.RandomScale([0.2, 1]), albu.RandomCrop(224, 224), albu.HorizontalFlip(), albu.HueSaturationValue(), albu.Normalize(), ToTensor(), ]) val_tfms = albu.Compose([ albu.Resize(256, 256), albu.CenterCrop(224, 224), albu.Normalize(), ToTensor(), ]) dataset_dir = get_datasets_root('imagenet') train_dataset = Imagenet(dataset_dir, split='train', transforms=train_tfms) val_dataset = Imagenet(dataset_dir, split='val', transforms=val_tfms) sampler_args = dict(world_size=world_size, local_rank=local_rank, enable=distributed) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=8, sampler=create_sampler(train_dataset, **sampler_args), shuffle=not distributed, )
albu.RandomScale([0.5, 1.5]), albu.PadIfNeeded(crop_size, crop_size), albu.RandomCrop(crop_size, crop_size), albu.HorizontalFlip(), albu.HueSaturationValue(), albu.Normalize(), ToTensor(), ]) val_tfms = albu.Compose([ albu.PadIfNeeded(crop_size, crop_size), albu.CenterCrop(crop_size, crop_size), albu.Normalize(), ToTensor(), ]) dataset_dir = get_datasets_root('PASCAL_VOC2012') train_dataset = VOC2012Segmentation(dataset_dir, split='train', transforms=train_tfms) val_dataset = VOC2012Segmentation(dataset_dir, split='val', transforms=val_tfms) sampler_args = dict(world_size=world_size, local_rank=local_rank, enable=distributed) train_loader = DataLoader( train_dataset, batch_size=args.batch_size, drop_last=True, num_workers=4, sampler=create_sampler(train_dataset, **sampler_args), shuffle=not distributed,