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,