def __init__(
self,
db_infos, # object/dbinfos_train.pkl
groups, # [dict(Car=15,),],
db_prepor=None, # filter_by_min_num_points, filter_by_difficulty
rate=1.0, # rate=1.0
global_rot_range=None, # [0, 0]
logger=None, # logging.getLogger("build_dbsampler")
gt_random_drop=-1.0,
gt_aug_with_context=-1.0,
gt_aug_similar_type=False,
):
# load all gt database here.
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
# preprocess: filter_by_min_num_points/difficulty.
if db_prepor is not None:
db_infos = db_prepor(db_infos)
logger.info("After filter database:")
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
self.db_infos = db_infos
self._rate = rate
self._groups = groups
self._group_db_infos = {}
self._group_name_to_names = []
self._sample_classes = []
self._sample_max_nums = []
self.gt_point_random_drop = gt_random_drop
self.gt_aug_with_context = gt_aug_with_context
# get group_name: Car and group_max_num: 15
self._group_db_infos = self.db_infos # just use db_infos
for group_info in groups:
self._sample_classes += list(group_info.keys()) # ['Car']
self._sample_max_nums += list(group_info.values()) # [15]
# get sampler dict for each class like Car, Cyclist, Pedestrian...
# this sampler can ensure batch samples selected randomly.
self._sampler_dict = {}
for k, v in self._group_db_infos.items():
self._sampler_dict[k] = prep.BatchSampler(v, k)
if gt_aug_similar_type:
self._sampler_dict["Car"] = prep.BatchSampler(
self._group_db_infos["Car"] + self._group_db_infos["Van"],
"Car")
def __init__(
self,
db_infos,
groups,
db_prepor=None,
rate=1.0,
global_rot_range=None,
logger=None,
):
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
if db_prepor is not None:
db_infos = db_prepor(db_infos)
logger.info("After filter database:")
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
self.db_infos = db_infos
self._rate = rate
self._groups = groups
self._group_db_infos = {}
self._group_name_to_names = []
self._sample_classes = []
self._sample_max_nums = []
self._use_group_sampling = False # slower
if any([len(g) > 1 for g in groups]):
self._use_group_sampling = True
if not self._use_group_sampling:
self._group_db_infos = self.db_infos # just use db_infos
for group_info in groups:
group_names = list(group_info.keys())
self._sample_classes += group_names
self._sample_max_nums += list(group_info.values())
else:
for group_info in groups:
group_dict = {}
group_names = list(group_info.keys())
group_name = ", ".join(group_names)
self._sample_classes += group_names
self._sample_max_nums += list(group_info.values())
self._group_name_to_names.append((group_name, group_names))
# self._group_name_to_names[group_name] = group_names
for name in group_names:
for item in db_infos[name]:
gid = item["group_id"]
if gid not in group_dict:
group_dict[gid] = [item]
else:
group_dict[gid] += [item]
if group_name in self._group_db_infos:
raise ValueError("group must be unique")
group_data = list(group_dict.values())
self._group_db_infos[group_name] = group_data
info_dict = {}
if len(group_info) > 1:
for group in group_data:
names = [item["name"] for item in group]
names = sorted(names)
group_name = ", ".join(names)
if group_name in info_dict:
info_dict[group_name] += 1
else:
info_dict[group_name] = 1
print(info_dict)
self._sampler_dict = {}
for k, v in self._group_db_infos.items():
self._sampler_dict[k] = prep.BatchSampler(v, k)
self._enable_global_rot = False
if global_rot_range is not None:
if not isinstance(global_rot_range, (list, tuple, np.ndarray)):
global_rot_range = [-global_rot_range, global_rot_range]
else:
assert shape_mergeable(global_rot_range, [2])
if np.abs(global_rot_range[0] - global_rot_range[1]) >= 1e-3:
self._enable_global_rot = True
self._global_rot_range = global_rot_range
def __init__(
self,
db_infos, # object/dbinfos_train.pkl
groups, # [dict(Car=15,),],
db_prepor=None, # filter_by_min_num_points, filter_by_difficulty
rate=1.0, # rate=1.0
global_rot_range=None, # [0, 0]
logger=None, # logging.getLogger("build_dbsampler")
):
# load all gt database here.
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
# preprocess: filter_by_min_num_points/difficulty.
if db_prepor is not None:
db_infos = db_prepor(db_infos)
logger.info("After filter database:")
for k, v in db_infos.items():
logger.info(f"load {len(v)} {k} database infos")
self.db_infos = db_infos
self._rate = rate
self._groups = groups
self._group_db_infos = {}
self._group_name_to_names = []
self._sample_classes = []
self._sample_max_nums = []
self._use_group_sampling = False # slower
if any([len(g) > 1 for g in groups]): # False
self._use_group_sampling = True
# get group_name: Car and group_max_num: 15
if not self._use_group_sampling: # True
self._group_db_infos = self.db_infos # just use db_infos
for group_info in groups:
group_names = list(group_info.keys())
self._sample_classes += group_names # ['Car']
self._sample_max_nums += list(group_info.values()) # [15]
else: # False
for group_info in groups:
group_dict = {}
group_names = list(group_info.keys())
group_name = ", ".join(group_names)
self._sample_classes += group_names
self._sample_max_nums += list(group_info.values())
self._group_name_to_names.append((group_name, group_names))
# self._group_name_to_names[group_name] = group_names
for name in group_names:
for item in db_infos[name]:
gid = item["group_id"]
if gid not in group_dict:
group_dict[gid] = [item]
else:
group_dict[gid] += [item]
if group_name in self._group_db_infos:
raise ValueError("group must be unique")
group_data = list(group_dict.values())
self._group_db_infos[group_name] = group_data
info_dict = {}
if len(group_info) > 1:
for group in group_data:
names = [item["name"] for item in group]
names = sorted(names)
group_name = ", ".join(names)
if group_name in info_dict:
info_dict[group_name] += 1
else:
info_dict[group_name] = 1
print(info_dict)
# get sampler dict for each class like Car, Cyclist, Pedestrian...
# this sampler can ensure batch samples selected randomly.
self._sampler_dict = {}
for k, v in self._group_db_infos.items():
self._sampler_dict[k] = prep.BatchSampler(v, k)
# get rotation range
self._enable_global_rot = False
if global_rot_range is not None:
if not isinstance(global_rot_range, (list, tuple, np.ndarray)):
global_rot_range = [-global_rot_range, global_rot_range]
else: # True
assert shape_mergeable(global_rot_range, [2]) # True
if np.abs(global_rot_range[0] -
global_rot_range[1]) >= 1e-3: # False
self._enable_global_rot = True
self._global_rot_range = global_rot_range # [0, 0]