def get_data_fields(mode, cfg):
''' Returns the data fields.
Args:
mode (str): the mode which is used
cfg (dict): imported yaml config
'''
points_transform = data.SubsamplePoints(cfg['data']['points_subsample'])
with_transforms = cfg['model']['use_camera']
fields = {}
fields['points'] = data.PointsField(
cfg['data']['points_file'],
points_transform,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
)
if mode in ('val', 'test'):
points_iou_file = cfg['data']['points_iou_file']
voxels_file = cfg['data']['voxels_file']
if points_iou_file is not None:
fields['points_iou'] = data.PointsField(
points_iou_file,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
)
if voxels_file is not None:
fields['voxels'] = data.VoxelsField(voxels_file)
return fields
def get_data_fields(split, cfg, **kwargs):
''' Returns the data fields.
Args:
split (str): the split which should be used
cfg (dict): loaded yaml config
'''
with_transforms = cfg['data']['with_transforms']
fields = {}
if split == 'train':
fields['voxels'] = data.VoxelsField(cfg['data']['voxels_file'])
elif split in ('val', 'test'):
fields['points_iou'] = data.PointsField(
cfg['data']['points_iou_file'],
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
)
return fields
if args.generation_dir == "":
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
else:
generation_dir = os.path.join(out_dir, args.generation_dir)
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.csv')
else:
out_file = os.path.join(generation_dir, 'eval_input_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_input.csv')
# Dataset
points_field = data.PointsField(
cfg['data']['points_iou_file'],
unpackbits=cfg['data']['points_unpackbits'],
)
pointcloud_field = data.PointCloudField(cfg['data']['pointcloud_chamfer_file'])
fields = {
'points_iou': points_field,
'pointcloud_chamfer': pointcloud_field,
'idx': data.IndexField(),
}
print('Test split: ', cfg['data']['test_split'])
if args.da:
dataset_folder = cfg['data']['uda_path']
categories_to_use = cfg['data']['uda_classes']
else:
dataset_folder = cfg['data']['path']
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.csv')
else:
out_file = os.path.join(generation_dir, 'eval_input_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_input.csv')
# Dataset
if 'test_range' in cfg['data']:
input_range = cfg['data']['test_range']
print('Test range:', input_range)
else:
input_range = None
points_field = data.PointsField(cfg['data']['points_iou_file'],
unpackbits=cfg['data']['points_unpackbits'],
input_range=input_range)
pointcloud_field = data.PointCloudField(cfg['data']['pointcloud_chamfer_file'])
fields = {
'points_iou': points_field,
'pointcloud_chamfer': pointcloud_field,
'idx': data.IndexField(),
}
print('Test split: ', cfg['data']['test_split'])
dataset_folder = cfg['data']['path']
dataset = data.Shapes3dDataset(dataset_folder,
fields,
cfg['data']['test_split'],
categories=cfg['data']['classes'])
def get_data_fields(mode, cfg):
''' Returns the data fields.
Args:
mode (str): the mode which is used
cfg (dict): imported yaml config
'''
points_transform = data.SubsamplePoints(cfg['data']['points_subsample'])
if cfg.get('sdf_generation', False):
points_transform = None
with_transforms = cfg['model']['use_camera']
fields = {}
fields['points'] = data.PointsField(
cfg['data']['points_file'],
points_transform,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
)
if not cfg.get('sdf_generation', False) and cfg['trainer'].get(
'is_sdf', False):
sdf_points_transform = data.SubsampleSDFPoints(
cfg['data']['points_subsample'])
fields['sdf_points'] = data.SDFPointsField(
cfg['data']['sdf_points_file'],
sdf_points_transform,
with_transforms=with_transforms)
pointcloud_transform = data.SubsamplePointcloud(
cfg['data']['pointcloud_target_n'])
if cfg.get('sdf_generation', False):
pointcloud_transform = None
fields['pointcloud'] = data.PointCloudField(cfg['data']['pointcloud_file'],
pointcloud_transform,
with_transforms=True)
fields['angles'] = nsd_data.SphericalCoordinateField(
cfg['data']['primitive_points_sample_n'],
mode,
is_normal_icosahedron=cfg['data'].get('is_normal_icosahedron', False),
is_normal_uv_sphere=cfg['data'].get('is_normal_uv_sphere', False),
icosahedron_subdiv=cfg['data'].get('icosahedron_subdiv', 2),
icosahedron_uv_margin=cfg['data'].get('icosahedron_uv_margin', 1e-5),
icosahedron_uv_margin_phi=cfg['data'].get('icosahedron_uv_margin_phi',
1e-5),
uv_sphere_length=cfg['data'].get('uv_sphere_length', 20),
normal_mesh_no_invert=cfg['data'].get('normal_mesh_no_invert', False))
if mode in ('val', 'test'):
points_iou_file = cfg['data']['points_iou_file']
voxels_file = cfg['data']['voxels_file']
if points_iou_file is not None:
fields['points_iou'] = data.PointsField(
points_iou_file,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
)
if voxels_file is not None:
fields['voxels'] = data.VoxelsField(voxels_file)
return fields
def get_occ_data_fields(mode, cfg):
''' Returns the data fields.
Args:
mode (str): the mode which is used
cfg (dict): imported yaml config
'''
N = cfg['data']['points_subsample']
points_transform = data.SubsamplePoints(cfg['data']['points_subsample'])
with_transforms = cfg['model']['use_camera']
if mode == 'train':
if 'input_range' in cfg['data']:
input_range = cfg['data']['input_range']
print('Input range:', input_range)
else:
input_range = None
else:
if 'test_range' in cfg['data']:
input_range = cfg['data']['test_range']
print('Test range:', input_range)
else:
input_range = None
fields = {}
points_file = cfg['data']['points_file']
if points_file.endswith('.npz'):
fields['points'] = data.PointsField(
cfg['data']['points_file'], points_transform,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
input_range=input_range
)
elif points_file.endswith('.h5'):
fields['points'] = data.PointsH5Field(
cfg['data']['points_file'], subsample_n=N,
with_transforms=with_transforms,
input_range=input_range
)
else:
raise NotImplementedError
if mode in ('val', 'test'):
points_iou_file = cfg['data']['points_iou_file']
voxels_file = cfg['data']['voxels_file']
if points_iou_file is not None:
if points_iou_file.endswith('.npz'):
fields['points_iou'] = data.PointsField(
points_iou_file,
with_transforms=with_transforms,
unpackbits=cfg['data']['points_unpackbits'],
input_range=input_range
)
elif points_iou_file.endswith('.h5'):
fields['points_iou'] = data.PointsH5Field(
points_iou_file,
with_transforms=with_transforms,
input_range=input_range
)
else:
raise NotImplementedError
if voxels_file is not None:
fields['voxels'] = data.VoxelsField(voxels_file)
return fields