def __init__(self, **kwargs):
# get target assigner & box_coder configs.
assigner_cfg = kwargs["cfg"]
target_assigner_config = assigner_cfg.target_assigner
self.tasks = target_assigner_config.tasks # num_class=1, class_names=["Car"]
box_coder_cfg = assigner_cfg.box_coder # "ground_box3d_coder"
# get anchor_generator
anchor_cfg = target_assigner_config.anchor_generators # anchor_generator_range
anchor_generators = []
for a_cfg in anchor_cfg:
anchor_generator = build_anchor_generator(a_cfg)
anchor_generators.append(anchor_generator)
# my addition
self.target_class_names = [
anchor_generator.class_name
for anchor_generator in anchor_generators
]
self.target_class_ids = [1] # for car id
self.enable_similar_type = assigner_cfg.get("enable_similar_type",
False)
if self.enable_similar_type:
self.target_class_ids = [
1, 2
] # for car id # todo: addition of similar type
# get target_assigner
target_assigners = []
similarity_calc = build_similarity_metric(
target_assigner_config.region_similarity_calculator) # nearest iou
positive_fraction = target_assigner_config.sample_positive_fraction # -1
if positive_fraction < 0:
positive_fraction = None
flag = 0
for task in self.tasks: # { num_class=1, class_names=["Car"] }
target_assigner = TargetAssigner(
box_coder=build_box_coder(
box_coder_cfg), # "ground_box3d_coder"
anchor_generators=anchor_generators[flag:flag +
task.num_class],
region_similarity_calculator=similarity_calc, # nearest iou
positive_fraction=positive_fraction, # None
sample_size=target_assigner_config.sample_size, # 512
)
flag += task.num_class # 1
target_assigners.append(target_assigner)
# results
self.target_assigners = target_assigners
self.out_size_factor = assigner_cfg.out_size_factor # 8
feature_map_size = [1, 200, 176]
self.anchor_dicts_by_task = [
assigner.generate_anchors_dict(feature_map_size)
for assigner in self.target_assigners
]
def __init__(self, **kwargs):
# get configs
assigner_cfg = kwargs["cfg"]
target_assigner_config = assigner_cfg.target_assigner
tasks = target_assigner_config.tasks # num_class=1, class_names=["Car"]
box_coder_cfg = assigner_cfg.box_coder # type="ground_box3d_coder", n_dim=7, linear_dim=False, encode_angle_vector=False,
# get anchor_generator
anchor_cfg = target_assigner_config.anchor_generators
anchor_generators = []
for a_cfg in anchor_cfg:
anchor_generator = build_anchor_generator(a_cfg)
anchor_generators.append(anchor_generator)
############# prepare configs and get target_assigner #############
# non-rotated iou
similarity_calc = build_similarity_metric(
target_assigner_config.region_similarity_calculator)
positive_fraction = target_assigner_config.sample_positive_fraction # -1
if positive_fraction < 0:
positive_fraction = None
target_assigners = []
flag = 0
box_coder = build_box_coder(box_coder_cfg)
for task in tasks: # { num_class=1, class_names=["Car"] }
target_assigner = TargetAssigner(
box_coder=box_coder,
anchor_generators=anchor_generators[flag:flag +
task.num_class],
region_similarity_calculator=similarity_calc,
positive_fraction=positive_fraction, # None
sample_size=target_assigner_config.sample_size, # 512
)
flag += task.num_class # 1
target_assigners.append(target_assigner)
# results
self.target_assigners = target_assigners
self.out_size_factor = assigner_cfg.out_size_factor
self.anchor_area_threshold = target_assigner_config.pos_area_threshold # -1
def __init__(self, **kwargs):
assigner_cfg = kwargs["cfg"]
target_assigner_config = assigner_cfg.target_assigner
tasks = target_assigner_config.tasks
box_coder_cfg = assigner_cfg.box_coder
anchor_cfg = target_assigner_config.anchor_generators
anchor_generators = []
for a_cfg in anchor_cfg:
anchor_generator = build_anchor_generator(a_cfg)
anchor_generators.append(anchor_generator)
similarity_calc = build_similarity_metric(
target_assigner_config.region_similarity_calculator)
positive_fraction = target_assigner_config.sample_positive_fraction
if positive_fraction < 0:
positive_fraction = None
target_assigners = []
flag = 0
box_coder = build_box_coder(box_coder_cfg)
for task in tasks:
target_assigner = TargetAssigner(
box_coder=box_coder,
anchor_generators=anchor_generators[flag:flag +
task.num_class],
region_similarity_calculator=similarity_calc,
positive_fraction=positive_fraction,
sample_size=target_assigner_config.sample_size,
)
flag += task.num_class
target_assigners.append(target_assigner)
self.target_assigners = target_assigners
self.out_size_factor = assigner_cfg.out_size_factor
self.anchor_area_threshold = target_assigner_config.pos_area_threshold
norm_cfg=norm_cfg,
logger=logging.getLogger("RPN"),
),
bbox_head=dict(
# type='RPNHead',
type="MultiGroupHead",
mode="3d",
in_channels=sum([
128,
]),
norm_cfg=norm_cfg,
tasks=tasks,
weights=[
1,
],
box_coder=build_box_coder(box_coder),
encode_background_as_zeros=True,
loss_norm=dict( # 这边用的是默认，什么含义暂时不知道
type="NormByNumPositives",
pos_cls_weight=1.0,
neg_cls_weight=1.0,
),
loss_cls=dict(
type="SigmoidFocalLoss",
alpha=0.25,
gamma=2.0,
loss_weight=1.0,
),
# Focal loss down-weights well classified examples and focusses on the hard examples.
# See https://arxiv.org/pdf/1708.02002.pdf for the loss definition.
use_sigmoid_score=True,
