def build(input_reader_config,
model_config,
training,
voxel_generator,
target_assigner=None):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
generate_bev = model_config.use_bev
without_reflectivity = model_config.without_reflectivity
num_point_features = model_config.num_point_features
out_size_factor = model_config.rpn.layer_strides[0] // model_config.rpn.upsample_strides[0]
cfg = input_reader_config
db_sampler_cfg = input_reader_config.database_sampler
db_sampler = None
if len(db_sampler_cfg.sample_groups) > 0: # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg)
u_db_sampler_cfg = input_reader_config.unlabeled_database_sampler
u_db_sampler = None
if len(u_db_sampler_cfg.sample_groups) > 0: # enable sample
u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg)
grid_size = voxel_generator.grid_size
# [352, 400]
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
prep_func = partial(
prep_pointcloud,
root_path=cfg.kitti_root_path,
class_names=list(cfg.class_names),
voxel_generator=voxel_generator,
target_assigner=target_assigner,
training=training,
max_voxels=cfg.max_number_of_voxels,
remove_outside_points=False,
remove_unknown=cfg.remove_unknown_examples,
create_targets=training,
shuffle_points=cfg.shuffle_points,
gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(cfg.global_rotation_uniform_noise),
global_scaling_noise=list(cfg.global_scaling_uniform_noise),
global_loc_noise_std=(0.2, 0.2, 0.2),
global_random_rot_range=list(
cfg.global_random_rotation_range_per_object),
db_sampler=db_sampler,
unlabeled_db_sampler=u_db_sampler,
generate_bev=generate_bev,
without_reflectivity=without_reflectivity,
num_point_features=num_point_features,
anchor_area_threshold=cfg.anchor_area_threshold,
gt_points_drop=cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=cfg.remove_points_after_sample,
remove_environment=cfg.remove_environment,
use_group_id=cfg.use_group_id,
out_size_factor=out_size_factor)
dataset = KittiDataset(
info_path=cfg.kitti_info_path,
root_path=cfg.kitti_root_path,
num_point_features=num_point_features,
target_assigner=target_assigner,
feature_map_size=feature_map_size,
prep_func=prep_func)
return dataset
def build(input_reader_config,
model_config,
training,
voxel_generator,
target_assigner,
multi_gpu=False):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
prep_cfg = input_reader_config.preprocess
dataset_cfg = input_reader_config.dataset
num_point_features = model_config.num_point_features
out_size_factor = get_downsample_factor(model_config)
assert out_size_factor > 0
cfg = input_reader_config
db_sampler_cfg = prep_cfg.database_sampler
db_sampler = None
if len(db_sampler_cfg.sample_groups) > 0: # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg)
grid_size = voxel_generator.grid_size
# [352, 400]
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
print("feature_map_size", feature_map_size)
assert all([n != '' for n in target_assigner.classes
]), "you must specify class_name in anchor_generators."
dataset_cls = get_dataset_class(dataset_cfg.dataset_class_name)
assert dataset_cls.NumPointFeatures >= 3, "you must set this to correct value"
assert dataset_cls.NumPointFeatures == num_point_features, "currently you need keep them same"
prep_func = partial(
prep_pointcloud,
root_path=dataset_cfg.kitti_root_path,
voxel_generator=voxel_generator,
target_assigner=target_assigner,
training=training,
max_voxels=prep_cfg.max_number_of_voxels,
remove_outside_points=False,
remove_unknown=prep_cfg.remove_unknown_examples,
create_targets=training,
shuffle_points=prep_cfg.shuffle_points,
gt_rotation_noise=list(prep_cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(prep_cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(prep_cfg.global_rotation_uniform_noise),
global_scaling_noise=list(prep_cfg.global_scaling_uniform_noise),
global_random_rot_range=list(
prep_cfg.global_random_rotation_range_per_object),
global_translate_noise_std=list(prep_cfg.global_translate_noise_std),
db_sampler=db_sampler,
num_point_features=dataset_cls.NumPointFeatures,
anchor_area_threshold=prep_cfg.anchor_area_threshold,
gt_points_drop=prep_cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=prep_cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=prep_cfg.remove_points_after_sample,
remove_environment=prep_cfg.remove_environment,
use_group_id=prep_cfg.use_group_id,
out_size_factor=out_size_factor,
multi_gpu=multi_gpu)
ret = target_assigner.generate_anchors(feature_map_size)
class_names = target_assigner.classes
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, 7])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, 6]])
anchor_cache = {
"anchors": anchors,
"anchors_bv": anchors_bv,
"matched_thresholds": matched_thresholds,
"unmatched_thresholds": unmatched_thresholds,
"anchors_dict": anchors_dict,
}
prep_func = partial(prep_func, anchor_cache=anchor_cache)
dataset = dataset_cls(info_path=dataset_cfg.kitti_info_path,
root_path=dataset_cfg.kitti_root_path,
class_names=class_names,
prep_func=prep_func)
return dataset
def build(input_reader_config,
model_config,
training,
voxel_generator,
target_assigner,
multi_gpu=False):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
prep_cfg = input_reader_config.preprocess
dataset_cfg = input_reader_config.dataset #kitti_info_path: "/home/lichao/v1.0-mini/infos_train.pkl"
# kitti_root_path: "/home/lichao/v1.0-mini"
# dataset_class_name: "NuScenesDataset"
num_point_features = model_config.num_point_features #4
out_size_factor = get_downsample_factor(model_config) #8
assert out_size_factor > 0
cfg = input_reader_config
db_sampler_cfg = prep_cfg.database_sampler #database_info_path: "/home/lichao/v1.0-mini/kitti_dbinfos_train.pkl"
# sample_groups { name_to_max_num key: "car" value: 30 } } global_random_rotation_range_per_object: 0.0# global_random_rotation_range_per_object: 0.0# rate: 1.0
db_sampler = None
if len(db_sampler_cfg.sample_groups
) > 0 or db_sampler_cfg.database_info_path != "": # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg) #加载了gt_base的一些东西
grid_size = voxel_generator.grid_size #[400,400]
feature_map_size = grid_size[:2] // out_size_factor #[50,50]
feature_map_size = [*feature_map_size, 1][::-1] #[50,50]
print("feature_map_size", feature_map_size)
assert all([n != '' for n in target_assigner.classes
]), "you must specify class_name in anchor_generators."
dataset_cls = get_dataset_class(
dataset_cfg.dataset_class_name) # NuScenesDataset
assert dataset_cls.NumPointFeatures >= 3, "you must set this to correct value"
assert dataset_cls.NumPointFeatures == num_point_features, "currently you need keep them same"
prep_func = partial( #pre_func partial 的功能：固定函数参数，返回一个新的函数。
prep_pointcloud, #data\preprocess.py
root_path=dataset_cfg.kitti_root_path,
voxel_generator=voxel_generator, #VoxelGeneratorV2
target_assigner=target_assigner,
training=training,
max_voxels=prep_cfg.max_number_of_voxels, #25000 eval 30000
remove_outside_points=False,
remove_unknown=prep_cfg.remove_unknown_examples,
create_targets=training,
shuffle_points=prep_cfg.shuffle_points,
gt_rotation_noise=list(prep_cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(prep_cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(prep_cfg.global_rotation_uniform_noise),
global_scaling_noise=list(prep_cfg.global_scaling_uniform_noise),
global_random_rot_range=list(
prep_cfg.global_random_rotation_range_per_object),
global_translate_noise_std=list(prep_cfg.global_translate_noise_std),
db_sampler=db_sampler,
num_point_features=dataset_cls.NumPointFeatures,
anchor_area_threshold=prep_cfg.anchor_area_threshold,
gt_points_drop=prep_cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=prep_cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=prep_cfg.remove_points_after_sample,
remove_environment=prep_cfg.remove_environment,
use_group_id=prep_cfg.use_group_id,
out_size_factor=out_size_factor, #8
multi_gpu=multi_gpu,
min_points_in_gt=prep_cfg.min_num_of_points_in_gt,
random_flip_x=prep_cfg.random_flip_x,
random_flip_y=prep_cfg.random_flip_y,
sample_importance=prep_cfg.sample_importance)
ret = target_assigner.generate_anchors(feature_map_size)
class_names = target_assigner.classes # ['car', 'bicycle', 'bus', 'construction_vehicle', 'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck', 'barrier']
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors_list = []
for k, v in anchors_dict.items():
anchors_list.append(v["anchors"]) #50000个
# anchors = ret["anchors"]
anchors = np.concatenate(anchors_list, axis=0)
anchors = anchors.reshape([-1, target_assigner.box_ndim])
assert np.allclose(anchors,
ret["anchors"].reshape(-1, target_assigner.box_ndim))
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(
anchors[:, [0, 1, 3, 4, 6]]) #bev_anchor 4维
anchor_cache = {
"anchors": anchors,
"anchors_bv": anchors_bv,
"matched_thresholds": matched_thresholds,
"unmatched_thresholds": unmatched_thresholds,
"anchors_dict": anchors_dict,
}
prep_func = partial(prep_func, anchor_cache=anchor_cache)
dataset = dataset_cls(
info_path=dataset_cfg.kitti_info_path, #数据的路径
root_path=dataset_cfg.kitti_root_path,
class_names=class_names, #10个类
prep_func=prep_func)
return dataset # _nusc_infos <class 'dict'>: {'lidar_path': '/home/lichao/v1.0-mini/samples/LIDAR_TOP/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin', 'cam_front_path': '/home/lichao/v1.0-mini/samples/CAM_FRONT/n015-2018-07-24-11-22-45+0800__CAM_FRONT__1532402927612460.jpg', 'token': 'ca9a282c9e77460f8360f564131a8af5', 'sweeps': [], 'lidar2ego_translation': [0.943713, 0.0, 1.84023], 'lidar2ego_rotation': [0.7077955119163518, -0.006492242056004365, 0.010646214713995808, -0.7063073142877817], 'ego2global_translation': [411.3039349319818, 1180.8903791765097, 0.0], 'ego2global_rotation': [0.5720320396729045, -0.0016977771610471074, 0.011798001930183783, -0.8201446642457809], 'timestamp': 1532402927647951, 'gt_boxes': array([[ 1.84143850e+01, 5.95160251e+01, 7.69634574e-01,
def build(
input_reader_config,
model_config,
training,
voxel_generator,
target_assigner,
multi_gpu=False,
generate_anchors_cachae=True, #True for pillar and second
segmentation=False,
bcl_keep_voxels=None,
seg_keep_points=None,
points_per_voxel=None):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
prep_cfg = input_reader_config.preprocess
dataset_cfg = input_reader_config.dataset
num_point_features = model_config.num_point_features
out_size_factor = get_downsample_factor(model_config)
assert out_size_factor > 0
cfg = input_reader_config
db_sampler_cfg = prep_cfg.database_sampler
db_sampler = None
if len(db_sampler_cfg.sample_groups
) > 0 or db_sampler_cfg.database_info_path != "": # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg)
grid_size = voxel_generator.grid_size
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
print("feature_map_size", feature_map_size)
assert all([n != '' for n in target_assigner.classes
]), "you must specify class_name in anchor_generators."
dataset_cls = get_dataset_class(dataset_cfg.dataset_class_name)
assert dataset_cls.NumPointFeatures >= 3, "you must set this to correct value"
assert dataset_cls.NumPointFeatures == num_point_features, "currently you need keep them same"
prep_func = partial(
prep_pointcloud,
root_path=dataset_cfg.kitti_root_path,
voxel_generator=voxel_generator,
target_assigner=target_assigner,
training=training,
max_voxels=prep_cfg.max_number_of_voxels,
remove_outside_points=False,
remove_unknown=prep_cfg.remove_unknown_examples,
create_targets=training,
shuffle_points=prep_cfg.shuffle_points,
gt_rotation_noise=list(prep_cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(prep_cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(prep_cfg.global_rotation_uniform_noise),
global_scaling_noise=list(prep_cfg.global_scaling_uniform_noise),
global_random_rot_range=list(
prep_cfg.global_random_rotation_range_per_object),
global_translate_noise_std=list(prep_cfg.global_translate_noise_std),
db_sampler=db_sampler,
num_point_features=dataset_cls.NumPointFeatures,
anchor_area_threshold=prep_cfg.anchor_area_threshold,
gt_points_drop=prep_cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=prep_cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=prep_cfg.remove_points_after_sample,
remove_environment=prep_cfg.remove_environment,
use_group_id=prep_cfg.use_group_id,
out_size_factor=out_size_factor,
multi_gpu=multi_gpu,
min_points_in_gt=prep_cfg.min_num_of_points_in_gt,
random_flip_x=prep_cfg.random_flip_x,
random_flip_y=prep_cfg.random_flip_y,
sample_importance=prep_cfg.sample_importance)
"""#leo add condition for catch is understand plz delete the comment"""
anchor_cache = None
class_names = target_assigner.classes
if generate_anchors_cachae:
ret = target_assigner.generate_anchors(
feature_map_size) #use bcl as voxel comment this line
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors_list = []
for k, v in anchors_dict.items():
anchors_list.append(v["anchors"])
# anchor_cache = None #if wants generate anchors from voxels
anchors = np.concatenate(anchors_list, axis=0)
anchors = anchors.reshape([-1, target_assigner.box_ndim])
assert np.allclose(
anchors, ret["anchors"].reshape(-1, target_assigner.box_ndim))
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:,
[0, 1, 3, 4, 6]])
anchor_cache = {
"anchors": anchors,
"anchors_bv": anchors_bv,
"matched_thresholds": matched_thresholds,
"unmatched_thresholds": unmatched_thresholds,
"anchors_dict": anchors_dict,
}
prep_func = partial(prep_func,
anchor_cache=anchor_cache,
segmentation=segmentation,
bcl_keep_voxels=bcl_keep_voxels,
seg_keep_points=seg_keep_points,
points_per_voxel=points_per_voxel)
dataset = dataset_cls(info_path=dataset_cfg.kitti_info_path,
root_path=dataset_cfg.kitti_root_path,
class_names=class_names,
prep_func=prep_func)
return dataset
def construct_example_for_predict(self, img, pc, rect, tr, p2):
"""
using params and input to construct an example
:param img:
:param pc:
:param rect:
:param tr:
:param p2:
:return:
"""
generate_bev = self.model_cfg.use_bev
without_reflectivity = self.model_cfg.without_reflectivity
num_point_features = self.model_cfg.num_point_features
out_size_factor = self.model_cfg.rpn.layer_strides[
0] // self.model_cfg.rpn.upsample_strides[0]
cfg = self.input_cfg
db_sampler_cfg = self.input_cfg.database_sampler
db_sampler = None
if len(db_sampler_cfg.sample_groups) > 0: # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg)
u_db_sampler_cfg = self.input_cfg.unlabeled_database_sampler
u_db_sampler = None
if len(u_db_sampler_cfg.sample_groups) > 0: # enable sample
u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg)
voxel_generator = voxel_builder.build(self.model_cfg.voxel_generator)
grid_size = voxel_generator.grid_size
# [352, 400]
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
ret = self.target_assigner.generate_anchors(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, 7])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:,
[0, 1, 3, 4, 6]])
anchor_cache = {
"anchors": anchors,
"anchors_bv": anchors_bv,
"matched_thresholds": matched_thresholds,
"unmatched_thresholds": unmatched_thresholds,
}
# preparing point cloud
prep_func = partial(
prep_pointcloud,
root_path=cfg.kitti_root_path,
class_names=list(cfg.class_names),
voxel_generator=voxel_generator,
target_assigner=self.target_assigner,
training=False,
max_voxels=cfg.max_number_of_voxels,
remove_outside_points=False,
remove_unknown=cfg.remove_unknown_examples,
create_targets=False,
shuffle_points=cfg.shuffle_points,
gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(cfg.global_rotation_uniform_noise),
global_scaling_noise=list(cfg.global_scaling_uniform_noise),
global_random_rot_range=list(
cfg.global_random_rotation_range_per_object),
db_sampler=db_sampler,
unlabeled_db_sampler=u_db_sampler,
generate_bev=generate_bev,
without_reflectivity=without_reflectivity,
num_point_features=num_point_features,
anchor_area_threshold=cfg.anchor_area_threshold,
gt_points_drop=cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=cfg.remove_points_after_sample,
remove_environment=cfg.remove_environment,
use_group_id=cfg.use_group_id,
out_size_factor=out_size_factor,
anchor_cache=anchor_cache,
)
example = prepare_v9_for_predict(img,
pc,
num_point_features,
r0_rect=rect,
tr_velo_2_cam=tr,
p2=p2,
prep_func=prep_func)
return example
