def setup_dataset(config, mode, requirements, **kwargs):
"""
Create a dataset class
Parameters
----------
config : CfgNode
Configuration (cf. configs/default_config.py)
mode : str {'train', 'validation', 'test'}
Mode from which we want the dataset
requirements : dict (string -> bool)
Different requirements for dataset loading (gt_depth, gt_pose, etc)
kwargs : dict
Extra parameters for dataset creation
Returns
-------
dataset : Dataset
Dataset class for that mode
"""
# If no dataset is given, return None
if len(config.path) == 0:
return None
print0(pcolor('###### Setup %s datasets' % mode, 'red'))
# Global shared dataset arguments
dataset_args = {
'back_context': config.back_context,
'forward_context': config.forward_context,
'data_transform': get_transforms(mode, **kwargs)
}
# Loop over all datasets
datasets = []
for i in range(len(config.split)):
path_split = os.path.join(config.path[i], config.split[i])
# Individual shared dataset arguments
dataset_args_i = {
'depth_type': config.depth_type[i] if 'gt_depth' in requirements else None,
'input_depth_type': config.input_depth_type[i] if 'gt_depth' in requirements else None,
'with_pose': 'gt_pose' in requirements,
}
# KITTI dataset
if config.dataset[i] == 'KITTI':
from packnet_sfm.datasets.kitti_dataset import KITTIDataset
dataset = KITTIDataset(
config.path[i], path_split,
**dataset_args, **dataset_args_i,
)
# DGP dataset
elif config.dataset[i] == 'DGP':
from packnet_sfm.datasets.dgp_dataset import DGPDataset
dataset = DGPDataset(
config.path[i], config.split[i],
**dataset_args, **dataset_args_i,
cameras=config.cameras[i],
)
# Image dataset
elif config.dataset[i] == 'Image':
from packnet_sfm.datasets.image_dataset import ImageDataset
dataset = ImageDataset(
config.path[i], config.split[i],
**dataset_args, **dataset_args_i,
)
else:
ValueError('Unknown dataset %d' % config.dataset[i])
# Repeat if needed
if 'repeat' in config and config.repeat[i] > 1:
dataset = ConcatDataset([dataset for _ in range(config.repeat[i])])
datasets.append(dataset)
# Display dataset information
bar = '######### {:>7}'.format(len(dataset))
if 'repeat' in config:
bar += ' (x{})'.format(config.repeat[i])
bar += ': {:<}'.format(path_split)
print0(pcolor(bar, 'yellow'))
# If training, concatenate all datasets into a single one
if mode == 'train':
datasets = [ConcatDataset(datasets)]
return datasets
def setup_dataset(config, mode, requirements, **kwargs):
"""
Create a dataset class
Parameters
----------
config : CfgNode
Configuration (cf. configs/default_config.py)
mode : str {'train', 'validation', 'test'}
Mode from which we want the dataset
requirements : dict (string -> bool)
Different requirements for dataset loading (gt_depth, gt_pose, etc)
kwargs : dict
Extra parameters for dataset creation
Returns
-------
dataset : Dataset
Dataset class for that mode
"""
# If no dataset is given, return None
if len(config.path) == 0:
return None
print0(pcolor('###### Setup %s datasets' % mode, 'red'))
# Global shared dataset arguments
dataset_args = {
'back_context': config.back_context,
'forward_context': config.forward_context,
'with_geometric_context': config.with_geometric_context,
}
# Loop over all datasets
datasets = []
for i in range(len(config.split)):
path_split = os.path.join(config.path[i], config.split[i])
# Individual shared dataset arguments
if config.dataset[i] == 'ValeoMultifocal':
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms_multifocal(mode, **kwargs),
'with_spatiotemp_context':
config.with_spatiotemp_context,
}
elif config.dataset[i] == 'KITTIValeoFisheye':
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms_fisheye(mode, **kwargs),
'calibrations_suffix':
config.calibrations_suffix,
'depth_suffix':
config.depth_suffix,
'cam_convs':
config.cam_convs
}
elif config.dataset[i] == 'KITTIValeoDistorted':
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms_distorted(mode, **kwargs)
}
elif config.dataset[i] == 'DGPvaleo':
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms_dgp_valeo(mode, **kwargs)
}
elif config.dataset[i] == 'WoodscapeFisheye':
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms_woodscape_fisheye(mode, **kwargs)
}
else:
dataset_args_i = {
'depth_type':
config.depth_type[i] if requirements['gt_depth'] else None,
'with_pose':
requirements['gt_pose'],
'data_transform':
get_transforms(mode, **kwargs)
}
if config.dataset[i] == 'ValeoMultifocal':
from packnet_sfm.datasets.kitti_based_valeo_dataset_multifocal import KITTIBasedValeoDatasetMultifocal
dataset = KITTIBasedValeoDatasetMultifocal(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# KITTI dataset
elif config.dataset[i] == 'KITTI':
from packnet_sfm.datasets.kitti_dataset import KITTIDataset
dataset = KITTIDataset(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
)
# DGP dataset
elif config.dataset[i] == 'DGP':
from packnet_sfm.datasets.dgp_dataset import DGPDataset
dataset = DGPDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# DGP dataset
elif config.dataset[i] == 'DGPvaleo':
from packnet_sfm.datasets.dgp_valeo_dataset import DGPvaleoDataset
dataset = DGPvaleoDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# Image dataset
elif config.dataset[i] == 'Image':
from packnet_sfm.datasets.image_dataset import ImageDataset
dataset = ImageDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
)
# KITTI-based Valeo dataset
elif config.dataset[i] == 'KITTIValeo':
from packnet_sfm.datasets.kitti_based_valeo_dataset import KITTIBasedValeoDataset
dataset = KITTIBasedValeoDataset(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# KITTI-based Valeo dataset (fisheye)
elif config.dataset[i] == 'KITTIValeoFisheye':
from packnet_sfm.datasets.kitti_based_valeo_dataset_fisheye_singleView import \
KITTIBasedValeoDatasetFisheye_singleView
dataset = KITTIBasedValeoDatasetFisheye_singleView(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
elif config.dataset[i] == 'KITTIValeoDistorted':
from packnet_sfm.datasets.kitti_based_valeo_dataset_distorted_singleView import \
KITTIBasedValeoDatasetDistorted_singleView
dataset = KITTIBasedValeoDatasetDistorted_singleView(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
elif config.dataset[i] == 'WoodscapeFisheye':
from packnet_sfm.datasets.woodscape_fisheye import WoodscapeFisheye
dataset = WoodscapeFisheye(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# Image-based Valeo dataset
elif config.dataset[i] == 'ImageValeo':
from packnet_sfm.datasets.image_based_valeo_dataset import ImageBasedValeoDataset
dataset = ImageBasedValeoDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
else:
ValueError('Unknown dataset %d' % config.dataset[i])
# Repeat if needed
if 'repeat' in config and config.repeat[i] > 1:
dataset = ConcatDataset([dataset for _ in range(config.repeat[i])])
datasets.append(dataset)
# Display dataset information
bar = '######### {:>7}'.format(len(dataset))
if 'repeat' in config:
bar += ' (x{})'.format(config.repeat[i])
bar += ': {:<}'.format(path_split)
print0(pcolor(bar, 'yellow'))
# If training, concatenate all datasets into a single one
if mode == 'train':
datasets = [ConcatDataset(datasets)]
return datasets
def setup_dataset(config, mode, requirements, **kwargs):
"""
Create a dataset class
Parameters
----------
config : CfgNode
Configuration (cf. configs/default_config.py)
mode : str {'train', 'validation', 'test'}
Mode from which we want the dataset
requirements : dict (string -> bool)
Different requirements for dataset loading (gt_depth, gt_pose, etc)
kwargs : dict
Extra parameters for dataset creation
Returns
-------
dataset : Dataset
Dataset class for that mode
"""
# If no dataset is given, return None
if len(config.path) == 0:
return None
print0(pcolor("###### Setup %s datasets" % mode, "red"))
# Global shared dataset arguments
dataset_args = {
"back_context": config.back_context,
"forward_context": config.forward_context,
"data_transform": get_transforms(mode, **kwargs),
}
# Loop over all datasets
datasets = []
for i in range(len(config.split)):
path_split = os.path.join(config.path[i], config.split[i])
# Individual shared dataset arguments
dataset_args_i = {
"depth_type":
config.depth_type[i] if requirements["gt_depth"] else None,
"with_pose": requirements["gt_pose"],
}
# KITTI dataset
if config.dataset[i] == "KITTI":
from packnet_sfm.datasets.kitti_dataset import KITTIDataset
dataset = KITTIDataset(
config.path[i],
path_split,
**dataset_args,
**dataset_args_i,
)
# DGP dataset
elif config.dataset[i] == "DGP":
from packnet_sfm.datasets.dgp_dataset import DGPDataset
dataset = DGPDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
cameras=config.cameras[i],
)
# Image dataset
elif config.dataset[i] == "Image":
from packnet_sfm.datasets.image_dataset import ImageDataset
dataset = ImageDataset(
config.path[i],
config.split[i],
**dataset_args,
**dataset_args_i,
)
else:
ValueError("Unknown dataset %d" % config.dataset[i])
# Repeat if needed
if "repeat" in config and config.repeat[i] > 1:
dataset = ConcatDataset([dataset for _ in range(config.repeat[i])])
datasets.append(dataset)
# Display dataset information
bar = "######### {:>7}".format(len(dataset))
if "repeat" in config:
bar += " (x{})".format(config.repeat[i])
bar += ": {:<}".format(path_split)
print0(pcolor(bar, "yellow"))
# If training, concatenate all datasets into a single one
if mode == "train":
datasets = [ConcatDataset(datasets)]
return datasets