def make_data_loader(args, **kwargs):
if args.dataset == 'cityscapes':
if args.autodeeplab == 'search':
train_set1, train_set2 = cityscapes.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
train_loader1 = DataLoader(train_set1, batch_size=args.batch_size, shuffle=True, **kwargs)
train_loader2 = DataLoader(train_set2, batch_size=args.batch_size, shuffle=True, **kwargs)
elif args.autodeeplab == 'train':
train_set = cityscapes.CityscapesSegmentation(args, split='retrain')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
else:
raise Exception('autodeeplab param not set properly')
val_set = cityscapes.CityscapesSegmentation(args, split='val')
test_set = cityscapes.CityscapesSegmentation(args, split='test')
val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, **kwargs)
if args.autodeeplab == 'search':
return train_loader1, train_loader2, val_loader, test_loader, num_class
elif args.autodeeplab == 'train':
return train_loader, num_class
else:
raise NotImplementedError
def make_data_loader(args, **kwargs):
if args.dataset == 'pascal':
train_set = pascal.VOCSegmentation(args, split='train')
val_set = pascal.VOCSegmentation(args, split='val')
if args.use_sbd:
sbd_train = sbd.SBDSegmentation(args, split=['train', 'val'])
train_set = combine_dbs.CombineDBs([train_set, sbd_train],
excluded=[val_set])
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, train_loader, val_loader, test_loader, num_class
elif args.dataset == 'cityscapes':
if args.autodeeplab == 'search':
train_set1, train_set2 = cityscapes.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
train_loader1 = DataLoader(train_set1,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set2,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
elif args.autodeeplab == 'train':
train_set = cityscapes.CityscapesSegmentation(args, split='train')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
else:
raise Exception('autodeeplab param not set properly')
val_set = cityscapes.CityscapesSegmentation(args, split='val')
test_set = cityscapes.CityscapesSegmentation(args, split='test')
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
if args.autodeeplab == 'search':
return train_loader1, train_loader2, val_loader, test_loader, num_class
elif args.autodeeplab == 'train':
return train_loader, val_loader, test_loader, num_class
elif args.dataset == 'coco':
train_set = coco.COCOSegmentation(args, split='train')
val_set = coco.COCOSegmentation(args, split='val')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, train_loader, val_loader, test_loader, num_class
elif args.dataset == 'kd':
train_set = kd.CityscapesSegmentation(args, split='train')
val_set = kd.CityscapesSegmentation(args, split='val')
test_set = kd.CityscapesSegmentation(args, split='test')
num_class = train_set.NUM_CLASSES
train_loader1 = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
return train_loader1, train_loader2, val_loader, test_loader, num_class
else:
raise NotImplementedError
def make_data_loader(args, **kwargs):
"""
if args.dist:
print("=> Using Distribued Sampler")
if args.dataset == 'cityscapes':
if args.autodeeplab == 'search':
train_set1, train_set2 = cityscapes.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
sampler1 = torch.utils.data.distributed.DistributedSampler(train_set1)
sampler2 = torch.utils.data.distributed.DistributedSampler(train_set2)
train_loader1 = DataLoader(train_set1, batch_size=args.batch_size, shuffle=False, sampler=sampler1, **kwargs)
train_loader2 = DataLoader(train_set2, batch_size=args.batch_size, shuffle=False, sampler=sampler2, **kwargs)
elif args.autodeeplab == 'train':
train_set = cityscapes.CityscapesSegmentation(args, split='retrain')
num_class = train_set.NUM_CLASSES
sampler1 = torch.utils.data.distributed.DistributedSampler(train_set)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=False, sampler=sampler1, **kwargs)
else:
raise Exception('autodeeplab param not set properly')
val_set = cityscapes.CityscapesSegmentation(args, split='val')
test_set = cityscapes.CityscapesSegmentation(args, split='test')
sampler3 = torch.utils.data.distributed.DistributedSampler(val_set)
sampler4 = torch.utils.data.distributed.DistributedSampler(test_set)
val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, sampler=sampler3, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, sampler=sampler4, **kwargs)
if args.autodeeplab == 'search':
return train_loader1, train_loader2, val_loader, test_loader, num_class
elif args.autodeeplab == 'train':
return train_loader, num_class, sampler1
else:
raise NotImplementedError
else:
"""
if args.dataset == 'pascal':
train_set = pascal.VOCSegmentation(args, split='train')
val_set = pascal.VOCSegmentation(args, split='val')
if args.use_sbd:
sbd_train = sbd.SBDSegmentation(args, split=['train', 'val'])
train_set = combine_dbs.CombineDBs([train_set, sbd_train],
excluded=[val_set])
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, train_loader, val_loader, test_loader, num_class
elif args.dataset == 'cityscapes':
if args.autodeeplab == 'search':
train_set1, train_set2 = cityscapes.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
train_loader1 = DataLoader(train_set1,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set2,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
elif args.autodeeplab == 'train':
train_set = cityscapes.CityscapesSegmentation(args,
split='retrain')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
else:
raise Exception('autodeeplab param not set properly')
val_set = cityscapes.CityscapesSegmentation(args, split='val')
test_set = cityscapes.CityscapesSegmentation(args, split='test')
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
if args.autodeeplab == 'search':
return train_loader1, train_loader2, val_loader, test_loader, num_class
elif args.autodeeplab == 'train':
return train_loader, num_class
elif args.dataset == 'custom':
if args.autodeeplab == 'search':
train_set1, train_set2 = custom.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
train_loader1 = DataLoader(train_set1,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set2,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
elif args.autodeeplab == 'train':
train_set = custom.CustomSegmentation(args, split='retrain')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
else:
raise Exception('autodeeplab param not set properly')
val_set = custom.CustomSegmentation(args, split='val')
test_set = custom.CustomSegmentation(args, split='test')
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
if args.autodeeplab == 'search':
return train_loader1, train_loader2, val_loader, test_loader, num_class
elif args.autodeeplab == 'train':
return train_loader, num_class
elif args.dataset == 'coco':
train_set = coco.COCOSegmentation(args, split='train')
val_set = coco.COCOSegmentation(args, split='val')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, train_loader, val_loader, test_loader, num_class
elif args.dataset == 'kd':
train_set = kd.CityscapesSegmentation(args, split='train')
val_set = kd.CityscapesSegmentation(args, split='val')
test_set = kd.CityscapesSegmentation(args, split='test')
num_class = train_set.NUM_CLASSES
train_loader1 = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
return train_loader1, train_loader2, val_loader, test_loader, num_class
else:
raise NotImplementedError
def make_data_loader(args, **kwargs):
if args.dataset == 'pascal':
train_set = pascal.VOCSegmentation('../../../Pascal/VOCdevkit',
train=True)
val_set = pascal.VOCSegmentation('../../../Pascal/VOCdevkit',
train=False)
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, val_loader, test_loader, num_class
elif 'cityscapes' in args.dataset:
if args.dataset == 'cityscapes_edm':
train_set = cityscapes.CityscapesSegmentation(args,
split='train',
full=True)
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
else:
if 'supernet' in args.network:
train_set1, train_set2 = cityscapes.twoTrainSeg(args)
num_class = train_set1.NUM_CLASSES
train_loader1 = DataLoader(train_set1,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loader2 = DataLoader(train_set2,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
else:
train_set = cityscapes.CityscapesSegmentation(args,
split='train')
num_class = train_set.NUM_CLASSES
if args.dist:
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=False,
sampler=DistributedSampler(train_set),
**kwargs)
else:
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
if args.network != None:
val_set = cityscapes.CityscapesSegmentation(args, split='val')
test_set = cityscapes.CityscapesSegmentation(args, split='test')
else:
raise Exception('autodeeplab param not set properly')
val_loader = DataLoader(val_set,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
test_loader = DataLoader(test_set,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
if 'supernet' in args.network:
return train_loader1, train_loader2, val_loader, test_loader, num_class
else:
return train_loader, val_loader, test_loader, num_class
elif args.dataset == 'coco':
train_set = coco.COCOSegmentation(args, split='train')
val_set = coco.COCOSegmentation(args, split='val')
num_class = train_set.NUM_CLASSES
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = None
return train_loader, train_loader, val_loader, test_loader, num_class
else:
raise NotImplementedError