def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
# fmt: off
self.min_box_side_len = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
self.in_features = cfg.MODEL.RPN.IN_FEATURES
self.nms_thresh = cfg.MODEL.RPN.NMS_THRESH
self.batch_size_per_image = cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE
self.positive_fraction = cfg.MODEL.RPN.POSITIVE_FRACTION
self.smooth_l1_beta = cfg.MODEL.RPN.SMOOTH_L1_BETA
self.loss_weight = cfg.MODEL.RPN.LOSS_WEIGHT
# fmt: on
# Map from self.training state to train/test settings
self.pre_nms_topk = {
True: cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN,
False: cfg.MODEL.RPN.PRE_NMS_TOPK_TEST,
}
self.post_nms_topk = {
True: cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN,
False: cfg.MODEL.RPN.POST_NMS_TOPK_TEST,
}
self.boundary_threshold = cfg.MODEL.RPN.BOUNDARY_THRESH
self.anchor_generator = build_anchor_generator(
cfg, [input_shape[f] for f in self.in_features]
)
self.box2box_transform = BUABox2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
self.anchor_matcher = Matcher(
cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True
)
self.rpn_head = build_rpn_head(cfg, [input_shape[f] for f in self.in_features])
def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
# fmt: off
self.min_box_side_len = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE
self.in_features = cfg.MODEL.RPN.IN_FEATURES
self.nms_thresh = cfg.MODEL.RPN.NMS_THRESH
self.batch_size_per_image = cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE
self.positive_fraction = cfg.MODEL.RPN.POSITIVE_FRACTION
self.smooth_l1_beta = cfg.MODEL.RPN.SMOOTH_L1_BETA
self.loss_weight = cfg.MODEL.RPN.LOSS_WEIGHT
self.num_frames = cfg.MODEL.SPATIOTEMPORAL.NUM_FRAMES
self.temporal_dropout = cfg.MODEL.SPATIOTEMPORAL.TEMPORAL_DROPOUT
# fmt: on
if self.temporal_dropout:
assert cfg.MODEL.SPATIOTEMPORAL.FORWARD_AGGREGATION, "Temporal dropout without forward aggregation."
if cfg.MODEL.SPATIOTEMPORAL.FORWARD_AGGREGATION:
# (f_{t-NUM_FRAMES}, ..., f_{t-1}, f_t, f_{t+1}, ..., f_{t+NUM_FRAMES})
self.num_frames = (2 * self.num_frames) + 1
# Map from self.training state to train/test settings
self.pre_nms_topk = {
True: cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN,
False: cfg.MODEL.RPN.PRE_NMS_TOPK_TEST,
}
self.post_nms_topk = {
True: cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN,
False: cfg.MODEL.RPN.POST_NMS_TOPK_TEST,
}
self.boundary_threshold = cfg.MODEL.RPN.BOUNDARY_THRESH
self.anchor_generator = build_anchor_generator(
cfg, [input_shape[f] for f in self.in_features])
self.box2box_transform = Box2BoxTransform(
weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
self.anchor_matcher = Matcher(cfg.MODEL.RPN.IOU_THRESHOLDS,
cfg.MODEL.RPN.IOU_LABELS,
allow_low_quality_matches=True)
self.rpn_head = build_rpn_head(
cfg, [input_shape[f] for f in self.in_features])
def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
in_features = cfg.MODEL.RPN.IN_FEATURES
ret = {
"in_features": in_features,
"min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE,
"nms_thresh": cfg.MODEL.RPN.NMS_THRESH,
"batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE,
"positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION,
"smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA,
"loss_weight": cfg.MODEL.RPN.LOSS_WEIGHT,
"anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH,
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS),
}
ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST)
ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST)
ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features])
ret["anchor_matcher"] = build_matcher(cfg)
ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features])
return ret