def create_test_loader(
args,
use_batched_inference: bool = False
) -> Tuple[torch.utils.data.dataloader.DataLoader, List[Tuple[str, str]]]:
"""Create a Pytorch dataloader from a dataroot and list of relative paths.
Args:
args: CfgNode object
use_batched_inference: whether to process images in batch mode
Returns:
test_loader
data_list: list of 2-tuples (relative rgb path, relative label path)
"""
preprocess_imgs_in_loader = True if use_batched_inference else False
if preprocess_imgs_in_loader:
# resize and normalize images in advance
mean, std = get_imagenet_mean_std()
test_transform = transform.Compose([
transform.ResizeShort(args.base_size),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)
])
else:
# no resizing on the fly using OpenCV and also normalize images on the fly
test_transform = transform.Compose([transform.ToTensor()])
test_data = dataset.SemData(split=args.split,
data_root=args.data_root,
data_list=args.test_list,
transform=test_transform)
index_start = args.index_start
if args.index_step == 0:
index_end = len(test_data.data_list)
else:
index_end = min(index_start + args.index_step,
len(test_data.data_list))
test_data.data_list = test_data.data_list[index_start:index_end]
data_list = test_data.data_list
# limit batch size to 1 if not performing batched inference
batch_size = args.batch_size_val if use_batched_inference else 1
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
return test_loader, data_list
def create_test_loader(self):
"""
Create a Pytorch dataloader from a dataroot and list of
relative paths.
"""
test_transform = transform.Compose([transform.ToTensor()])
test_data = dataset.SemData(split=self.args.split,
data_root=self.args.data_root,
data_list=self.args.test_list,
transform=test_transform)
index_start = self.args.index_start
if self.args.index_step == 0:
index_end = len(test_data.data_list)
else:
index_end = min(index_start + args.index_step,
len(test_data.data_list))
test_data.data_list = test_data.data_list[index_start:index_end]
self.data_list = test_data.data_list
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=1,
shuffle=False,
num_workers=self.args.workers,
pin_memory=True)
return test_loader
def create_test_loader(
args
) -> Tuple[torch.utils.data.dataloader.DataLoader, List[Tuple[str, str]]]:
"""
Create a Pytorch dataloader from a dataroot and list of
relative paths.
Args:
Returns:
- test_loader
- data_list: list of 2-tuples (relative rgb path, relative label path)
"""
test_transform = transform.Compose([transform.ToTensor()])
test_data = dataset.SemData(split=args.split,
data_root=args.data_root,
data_list=args.test_list,
transform=test_transform)
index_start = args.index_start
if args.index_step == 0:
index_end = len(test_data.data_list)
else:
index_end = min(index_start + args.index_step,
len(test_data.data_list))
test_data.data_list = test_data.data_list[index_start:index_end]
data_list = test_data.data_list
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=1,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
return test_loader, data_list
def get_train_transform_list(args, split: str):
""" Return the input data transform for training (w/ data augmentations)
Args:
- args:
- split
Return:
- List of transforms
"""
from mseg_semantic.utils.normalization_utils import get_imagenet_mean_std
from mseg_semantic.utils import transform
mean, std = get_imagenet_mean_std()
if split == 'train':
transform_list = [
transform.ResizeShort(args.short_size),
transform.RandScale([args.scale_min, args.scale_max]),
transform.RandRotate([args.rotate_min, args.rotate_max], padding=mean, ignore_label=args.ignore_label),
transform.RandomGaussianBlur(),
transform.RandomHorizontalFlip(),
transform.Crop([args.train_h, args.train_w], crop_type='rand', padding=mean, ignore_label=args.ignore_label),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)
]
elif split == 'val':
transform_list = [
transform.Crop([args.train_h, args.train_w], crop_type='center', padding=mean, ignore_label=args.ignore_label),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)
]
else:
print('Unknown split. Quitting ...')
quit()
if len(args.dataset) > 1 and args.universal:
transform_list += [ToFlatLabel(args.tc, args.dataset_name)]
elif args.universal:
transform_list += [ToFlatLabel(args.tc, args.dataset[0])]
return transform.Compose(transform_list)