output_types=output_types,
train_split=train_split,
#concatenate_inputs=True
)
if args.dataset == 'AachenDayNight':
kwargs['night_augmentation'] = args.use_augmentation
if (args.model.find('mapnet') >= 0) or (args.model.find('semantic') >= 0) or (args.model.find('multitask') >= 0):
vo_func = calc_vos_safe_fc if fc_vos else calc_vos_safe
data_set = MF(dataset=args.dataset, steps=steps, skip=skip, real=real,
variable_skip=variable_skip, include_vos=False,
vo_func=vo_func, no_duplicates=False, **kwargs)
L = len(data_set.dset)
elif args.dataset == '7Scenes':
from dataset_loaders.seven_scenes import SevenScenes
data_set = SevenScenes(**kwargs)
L = len(data_set)
elif args.dataset == 'DeepLoc':
from dataset_loaders.deeploc import DeepLoc
data_set = DeepLoc(**kwargs)
L = len(data_set)
elif args.dataset == 'RobotCar':
from dataset_loaders.robotcar import RobotCar
data_set = RobotCar(**kwargs)
L = len(data_set)
elif args.dataset == 'AachenDayNight':
from dataset_loaders.aachen import AachenDayNight
data_set = AachenDayNight(**kwargs)
L = len(data_set)
elif args.dataset == 'CambridgeLandmarks':
from dataset_loaders.cambridge import Cambridge
# training split
kwargs = dict(scene=args.scene,
data_path=data_dir,
train=True,
real=False,
seed=7,
train_split=20)
if args.dataset in ['DeepLoc', 'AachenDayNight', 'CambridgeLandmarks']:
kwargs['input_types'] = []
else:
kwargs['skip_images'] = True
if args.dataset == '7Scenes':
from dataset_loaders.seven_scenes import SevenScenes
dset = SevenScenes(**kwargs)
elif args.dataset == 'DeepLoc':
from dataset_loaders.deeploc import DeepLoc
dset = DeepLoc(**kwargs)
elif args.dataset == 'RobotCar':
from dataset_loaders.robotcar import RobotCar
dset = RobotCar(**kwargs)
elif args.dataset == 'AachenDayNight':
from dataset_loaders.aachen import AachenDayNight
dset = AachenDayNight(**kwargs)
elif args.dataset == 'CambridgeLandmarks':
from dataset_loaders.cambridge import Cambridge
dset = Cambridge(**kwargs)
elif args.dataset == 'stylized_localization':
from dataset_loaders.stylized_loader import StylizedCambridge
dset = StylizedCambridge(**kwargs)
tforms.append(transforms.ToTensor())
tforms.append(transforms.Normalize(mean=stats[0], std=np.sqrt(stats[1])))
data_transform = transforms.Compose(tforms)
target_transform = transforms.Lambda(lambda x: torch.from_numpy(x).float())
# datasets
data_dir = osp.join('..', 'data', 'deepslam_data', args.dataset)
kwargs = dict(scene=args.scene,
data_path=data_dir,
transform=data_transform,
target_transform=target_transform,
seed=seed)
if args.model == 'posenet':
if args.dataset == '7Scenes':
from dataset_loaders.seven_scenes import SevenScenes
train_set = SevenScenes(train=True, **kwargs)
val_set = SevenScenes(train=False, **kwargs)
elif args.dataset == 'RobotCar':
from dataset_loaders.robotcar import RobotCar
train_set = RobotCar(train=True, **kwargs)
val_set = RobotCar(train=False, **kwargs)
else:
raise NotImplementedError
elif args.model.find('mapnet') >= 0:
kwargs = dict(kwargs,
dataset=args.dataset,
skip=skip,
steps=steps,
variable_skip=variable_skip)
if args.model.find('++') >= 0:
train_set = MFOnline(vo_lib=vo_lib,
def __init__(self,
dataset,
include_vos=False,
no_duplicates=False,
*args,
**kwargs):
"""
:param steps: Number of frames to return on every call
:param skip: Number of frames to skip
:param variable_skip: If True, skip = [1, ..., skip]
:param include_vos: True if the VOs have to be appended to poses. If real
and include_vos are both on, it gives absolute poses from GT and VOs from
the SLAM / DSO
:param no_duplicates: if True, does not duplicate frames when len(self) is
not a multiple of skip*steps
"""
self.steps = kwargs.pop('steps', 2)
self.skip = kwargs.pop('skip', 1)
self.variable_skip = kwargs.pop('variable_skip', False)
self.real = kwargs.pop('real', False)
self.include_vos = include_vos
self.train = kwargs['train']
self.vo_func = kwargs.pop('vo_func', calc_vos_simple)
self.no_duplicates = no_duplicates
if dataset == '7Scenes':
from dataset_loaders.seven_scenes import SevenScenes
self.dset = SevenScenes(*args, real=self.real, **kwargs)
if self.include_vos and self.real:
self.gt_dset = SevenScenes(*args,
skip_images=True,
real=False,
**kwargs)
elif dataset == 'InLoc':
from dataset_loaders.inloc import InLoc
self.dset = InLoc(*args, real=self.real, **kwargs)
if self.include_vos and self.real:
# This might not work
self.gt_dset = InLoc(*args,
skip_images=True,
real=False,
**kwargs)
elif dataset == 'InLocRes':
from dataset_loaders.inloc import InLocQuery
self.dset = InLocQuery(*args, real=self.real, **kwargs)
if self.include_vos and self.real:
# This might not work
self.gt_dset = InLoc(*args,
skip_images=True,
real=False,
**kwargs)
elif dataset == 'RobotCar':
from dataset_loaders.robotcar import RobotCar
self.dset = RobotCar(*args, real=self.real, **kwargs)
if self.include_vos and self.real:
self.gt_dset = RobotCar(*args,
skip_images=True,
real=False,
**kwargs)
else:
raise NotImplementedError
self.L = self.steps * self.skip
