def init_param(self, model_config):
self.in_channels = model_config['din']
self.post_nms_topN = model_config['post_nms_topN']
self.pre_nms_topN = model_config['pre_nms_topN']
self.nms_thresh = model_config['nms_thresh']
self.use_score = model_config['use_score']
self.rpn_batch_size = model_config['rpn_batch_size']
self.use_focal_loss = model_config['use_focal_loss']
# sampler
# self.sampler = HardNegativeSampler(model_config['sampler_config'])
# self.sampler = BalancedSampler(model_config['sampler_config'])
self.sampler = DetectionSampler(model_config['sampler_config'])
# anchor generator
self.anchor_generator = AnchorGenerator(
model_config['anchor_generator_config'])
self.num_anchors = self.anchor_generator.num_anchors
self.nc_bbox_out = 4 * self.num_anchors
self.nc_score_out = self.num_anchors * 2
# target assigner
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# bbox coder
self.bbox_coder = self.target_assigner.bbox_coder
self.use_iou = model_config.get('use_iou')
def init_param(self, model_config):
classes = model_config['classes']
self.classes = classes
self.n_classes = len(classes)
self.class_agnostic = model_config['class_agnostic']
self.pooling_size = model_config['pooling_size']
self.pooling_mode = model_config['pooling_mode']
self.crop_resize_with_max_pool = model_config[
'crop_resize_with_max_pool']
self.truncated = model_config['truncated']
self.use_focal_loss = model_config['use_focal_loss']
self.subsample_twice = model_config['subsample_twice']
self.rcnn_batch_size = model_config['rcnn_batch_size']
# some submodule config
self.feature_extractor_config = model_config[
'feature_extractor_config']
self.rpn_config = model_config['rpn_config']
# assigner
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# sampler
self.sampler = BalancedSampler(model_config['sampler_config'])
def build_target_assigner(target_assigner_config, bv_range, box_coder):
"""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.
"""
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_calculator(
target_assigner_config.REGION_SIMILARITY_CALCULATOR)
positive_fraction = target_assigner_config.SAMPLE_POSITIVE_FRACTION
if positive_fraction < 0:
positive_fraction = None
target_assigner = TargetAssigner(
box_coder=box_coder,
anchor_generators=anchor_generators,
region_similarity_calculator=similarity_calc,
positive_fraction=positive_fraction,
sample_size=target_assigner_config.SAMPLE_SIZE)
return target_assigner
def init_params(self, model_config):
self.feature_extractor_config = model_config[
'feature_extractor_config']
self.multibox_cfg = model_config['multibox_cfg']
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
def __init__(self, layer_config):
super().__init__()
# some parameters
self.rpn_positive_weight = layer_config['rpn_positive_weight']
self.rpn_negative_overlaps = layer_config['rpn_negative_overlaps']
self.rpn_positive_overlaps = layer_config['rpn_positive_overlaps']
self.rpn_batch_size = layer_config['rpn_batch_size']
# subsample score and iou or subsample score only
self.subsample_twice = layer_config['subsample_twice']
self.subsample_type = layer_config['subsample_type']
self.target_assigner = TargetAssigner()
self.sampler = Sampler(self.subsample_type)
def init_param(self, model_config):
self.feat_size = model_config['common_feat_size']
self.batch_size = model_config['batch_size']
self.sample_size = model_config['sample_size']
self.pooling_size = model_config['pooling_size']
self.n_classes = model_config['num_classes']
self.use_focal_loss = model_config['use_focal_loss']
self.feature_extractor_config = model_config[
'feature_extractor_config']
self.voxel_generator = VoxelGenerator(
model_config['voxel_generator_config'])
self.voxel_generator.init_voxels()
self.integral_map_generator = IntegralMapGenerator()
self.oft_target_assigner = OFTargetAssigner(
model_config['target_assigner_config'])
self.target_assigner = TargetAssigner(
model_config['eval_target_assigner_config'])
self.target_assigner.analyzer.append_gt = False
self.sampler = DetectionSampler(model_config['sampler_config'])
self.bbox_coder = self.oft_target_assigner.bbox_coder
# find the most expensive operators
self.profiler = Profiler()
# self.multibin = model_config['multibin']
self.num_bins = model_config['num_bins']
self.reg_channels = 3 + 3 + self.num_bins * 4
# score, pos, dim, ang
self.rcnn_output_channels = self.n_classes + self.reg_channels
self.rpn_output_channels = 2 + 3 + 3
nms_deltas = model_config.get('nms_deltas')
if nms_deltas is None:
nms_deltas = 1
self.nms_deltas = nms_deltas
def init_param(self, model_config):
self.feature_extractor_config = model_config['feature_extractor_config']
self.multibox_cfg = [3, 3, 3, 3, 3, 3]
self.n_classes = len(model_config['classes'])
self.sampler = DetectionSampler(model_config['sampler_config'])
self.batch_size = model_config['batch_size']
self.use_focal_loss = model_config['use_focal_loss']
# self.multibox_cfg = model_config['multibox_config']
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# import ipdb
# ipdb.set_trace()
self.anchor_generator = AnchorGenerator(
model_config['anchor_generator_config'])
self.bbox_coder = self.target_assigner.bbox_coder