def build(similarity_calculator_cfg):
class_name = similarity_calculator_cfg["type"]
params = {
k: v
for k, v in similarity_calculator_cfg.items() if k != "type"
}
builder = load_module("methods/second/core/similarity_calculator.py",
name=class_name)
return builder(**params)
def build(target_assigner_cfg,
box_coder,
anchor_generators,
region_similarity_calculators):
class_name = target_assigner_cfg["type"]
params = {k:v for k, v in target_assigner_cfg.items() if k != "type"}
params["box_coder"] = box_coder
params["anchor_generators"] = anchor_generators
params["region_similarity_calculators"] = region_similarity_calculators
builder = load_module("methods/second/core/target_assigner.py", name=class_name)
return builder(**params)
def build(cfg, target_assigner, voxelizer):
class_name = cfg["name"]
cfg["MiddleLayer"]["output_shape"] = [
1
] + voxelizer.grid_size[::-1].tolist() + [16] # Q: Why 16 here?
cfg["RPN"]["num_anchor_per_loc"] = target_assigner.num_anchors_per_location
cfg["RPN"]["box_code_size"] = target_assigner.box_coder.code_size
cfg["num_input_features"] = 4
builder = load_module("methods/second/core/second.py", name=class_name)
second = builder(vfe_cfg=cfg["VoxelEncoder"],
middle_cfg=cfg["MiddleLayer"],
rpn_cfg=cfg["RPN"],
cls_loss_cfg=cfg["ClassificationLoss"],
loc_loss_cfg=cfg["LocalizationLoss"],
cfg=cfg,
target_assigner=target_assigner,
voxelizer=voxelizer)
return second
def build_multiclass(target_assigner_cfg,
box_coder):
class_name = target_assigner_cfg["type"]
params = {k:v for k, v in target_assigner_cfg.items() if k != "type" and "class_settings" not in k}
params["box_coder"] = box_coder
# build anchors
# build region similarity calculators
classsettings_cfgs = [v for k, v in target_assigner_cfg.items() if "class_settings" in k]
anchor_generators = []
similarity_calculators = []
classes = params["classes"]
for cls in classes:
classsetting_cfg = [itm for itm in classsettings_cfgs
if itm["AnchorGenerator"]["class_name"] == cls][0]
anchor_generator_cfg = classsetting_cfg["AnchorGenerator"]
anchor_generator = build_anchor_generator(anchor_generator_cfg)
anchor_generators.append(anchor_generator)
similarity_calculator_cfg = classsetting_cfg["SimilarityCalculator"]
similarity_calculator = build_similarity_calculator(similarity_calculator_cfg)
similarity_calculators.append(similarity_calculator)
params["anchor_generators"] = anchor_generators
params["region_similarity_calculators"] = similarity_calculators
builder = load_module("methods/second/core/target_assigner.py", name=class_name)
return builder(**params)
def build(box_coder_cfg):
class_name = box_coder_cfg["type"]
params = {k: v for k, v in box_coder_cfg.items() if k != "type"}
builder = load_module("methods/second/core/box_coder.py", name=class_name)
return builder(**params)
def build(anchor_generator_cfg):
class_name = anchor_generator_cfg["type"]
params = {k:v for k, v in anchor_generator_cfg.items() if k != "type"}
builder = load_module("methods/second/core/anchor_generator.py", name=class_name)
return builder(**params)
def build(model_cfg, dataloader_cfg, voxelizer, target_assigner, training):
from det3.methods.second.data.preprocess import DataBasePreprocessor
if "DBSampler" in dataloader_cfg.keys():
# build DBProcer
dbprocers_cfg = dataloader_cfg["DBSampler"]["DBProcer"]
tmps = [(load_module("methods/second/data/preprocess.py",
cfg["name"]), cfg) for cfg in dbprocers_cfg]
dbproces = []
for builder, params in tmps:
del params["name"]
dbproces.append(builder(**params))
db_prepor = DataBasePreprocessor(dbproces)
# build DBSampler
info_path = dataloader_cfg["DBSampler"]["db_info_path"]
with open(info_path, "rb") as f:
db_infos = pickle.load(f)
dbsampler_builder = load_module("methods/second/core/db_sampler.py",
dataloader_cfg["DBSampler"]["name"])
if dataloader_cfg["DBSampler"]["name"] == "DataBaseSamplerV3":
sample_dict = dataloader_cfg["DBSampler"]["sample_dict"]
sample_param = dataloader_cfg["DBSampler"]["sample_param"]
dbsampler = dbsampler_builder(db_infos=db_infos,
db_prepor=db_prepor,
sample_dict=sample_dict,
sample_param=sample_param)
Logger.log_txt(
"Warning: dataloader_builder.py:" +
"DBSampler build function should be changed to configurable.")
elif dataloader_cfg["DBSampler"]["name"] == "DataBaseSamplerV2":
groups = dataloader_cfg["DBSampler"]["sample_groups"]
rate = dataloader_cfg["DBSampler"]["rate"]
global_rot_range = dataloader_cfg["DBSampler"][
"global_random_rotation_range_per_object"]
dbsampler = dbsampler_builder(db_infos=db_infos,
groups=groups,
db_prepor=db_prepor,
rate=rate,
global_rot_range=global_rot_range)
Logger.log_txt(
"Warning: dataloader_builder.py:" +
"DBSampler build function should be changed to configurable.")
else:
raise NotImplementedError
else:
dbsampler = None
Logger.log_txt(
"Warning: dataloader_builder.py:" +
"DBSampler build function should be changed to configurable.")
grid_size = voxelizer.grid_size
out_size_factor = get_downsample_factor(model_cfg)
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
assert (all([n != '' for n in target_assigner.classes]),
"you must specify class_name in anchor_generators.")
dataset_cls = get_dataset_class(dataloader_cfg["Dataset"]["name"])
num_point_features = model_cfg["VoxelEncoder"]["num_input_features"]
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_cfg = dataloader_cfg["PreProcess"]
if dataloader_cfg["Dataset"]["name"] == "KittiDataset":
from det3.methods.second.data.preprocess import prep_pointcloud
prep_func = partial(
prep_pointcloud,
root_path=dataloader_cfg["Dataset"]["kitti_root_path"],
voxel_generator=voxelizer,
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=dbsampler,
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=False,
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"])
elif dataloader_cfg["Dataset"]["name"] == "MyKittiDataset":
from det3.methods.second.data.mypreprocess import prep_pointcloud
augment_dict = prep_cfg["augment_dict"]
prep_func = partial(prep_pointcloud,
training=training,
db_sampler=dbsampler,
augment_dict=augment_dict,
voxelizer=voxelizer,
max_voxels=prep_cfg["max_number_of_voxels"],
target_assigner=target_assigner)
else:
raise NotImplementedError
# generate anchors
ret = target_assigner.generate_anchors(feature_map_size)
class_names = target_assigner.classes
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors_list = []
for k, v in anchors_dict.items():
anchors_list.append(v["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 = 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=dataloader_cfg["Dataset"]["kitti_info_path"],
root_path=dataloader_cfg["Dataset"]["kitti_root_path"],
class_names=class_names,
prep_func=prep_func,
num_point_features=num_point_features)
dataset = DatasetWrapper(dataset)
return dataset
info_path=dataloader_cfg["Dataset"]["kitti_info_path"],
root_path=dataloader_cfg["Dataset"]["kitti_root_path"],
class_names=class_names,
prep_func=prep_func,
num_point_features=num_point_features)
dataset = DatasetWrapper(dataset)
return dataset
if __name__ == "__main__":
from det3.methods.second.builder import (voxelizer_builder,
box_coder_builder,
similarity_calculator_builder,
anchor_generator_builder,
target_assigner_builder)
cfg = load_module("methods/second/configs/config.py", name="cfg")
voxelizer = voxelizer_builder.build(voxelizer_cfg=cfg.Voxelizer)
anchor_generator = anchor_generator_builder.build(
anchor_generator_cfg=cfg.AnchorGenerator)
box_coder = box_coder_builder.build(box_coder_cfg=cfg.BoxCoder)
similarity_calculator = similarity_calculator_builder.build(
similarity_calculator_cfg=cfg.SimilarityCalculator)
target_assigner = target_assigner_builder.build(
target_assigner_cfg=cfg.TargetAssigner,
box_coder=box_coder,
anchor_generators=[anchor_generator],
region_similarity_calculators=[similarity_calculator])
build(cfg.Net,
cfg.TrainDataLoader,
voxelizer,
target_assigner,
target_assigner_builder)
from det3.dataloader.augmentor import KittiAugmentor
import numpy as np
voxelizer = voxelizer_builder.build(voxelizer_cfg=cfg.Voxelizer)
anchor_generator = anchor_generator_builder.build(
anchor_generator_cfg=cfg.AnchorGenerator)
box_coder = box_coder_builder.build(box_coder_cfg=cfg.BoxCoder)
similarity_calculator = similarity_calculator_builder.build(
similarity_calculator_cfg=cfg.SimilarityCalculator)
target_assigner = target_assigner_builder.build(
target_assigner_cfg=cfg.TargetAssigner,
box_coder=box_coder,
anchor_generators=[anchor_generator],
region_similarity_calculators=[similarity_calculator])
dbprocers_cfg = cfg.TrainDataLoader["DBSampler"]["DBProcer"]
tmps = [(load_module("methods/second/data/mypreprocess.py",
cfg["name"]), cfg) for cfg in dbprocers_cfg]
dbproces = []
for builder, params in tmps:
del params["name"]
dbproces.append(builder(**params))
db_prepor = DataBasePreprocessor(dbproces)
db_infos = load_pickle("/usr/app/data/MyKITTI/KITTI_dbinfos_train.pkl")
train_infos = load_pickle("/usr/app/data/MyKITTI/KITTI_infos_train.pkl")
sample_dict = cfg.TrainDataLoader["DBSampler"]["sample_dict"]
dbsampler = DataBaseSamplerV3(db_infos,
db_prepor=db_prepor,
sample_dict=sample_dict)
# augmentor = KittiAugmentor(p_rot=0.2, p_flip=0.2, p_keep=0.4, p_tr=0.2,
# dx_range=[-0.5, 0.5], dy_range=[-0.5, 0.5], dz_range=[-0.1, 0.1],
# dry_range=[-5 * 180 / np.pi, 5 * 180 / np.pi])
augment_dict = {
def build(voxelizer_cfg):
class_name = voxelizer_cfg["type"]
params = {k: v for k, v in voxelizer_cfg.items() if k != "type"}
builder = load_module("methods/second/core/voxelizer.py", name=class_name)
voxelizer = builder(**params)
return voxelizer