def __init__(self, dim_in, spatial_scale):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_in if cfg.RPN.OUT_DIM_AS_IN_DIM else cfg.RPN.OUT_DIM
anchors = generate_anchors(stride=1. / spatial_scale,
sizes=cfg.RPN.SIZES,
aspect_ratios=cfg.RPN.ASPECT_RATIOS)
num_anchors = anchors.shape[0]
# RPN hidden representation
self.RPN_conv = nn.Conv2d(self.dim_in, self.dim_out, 3, 1, 1)
# Proposal classification scores
self.n_score_out = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.RPN_cls_score = nn.Conv2d(self.dim_out, self.n_score_out, 1, 1, 0)
# Proposal bbox regression deltas
self.RPN_bbox_pred = nn.Conv2d(self.dim_out, num_anchors * 4, 1, 1, 0)
self.RPN_GenerateProposals = GenerateProposalsOp(
anchors, spatial_scale)
self.RPN_GenerateProposalLabels = GenerateProposalLabelsOp()
self._init_weights()
def __init__(self, dim_in, spatial_scale):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_in if cfg.RPN.OUT_DIM_AS_IN_DIM else cfg.RPN.OUT_DIM
self.RPN_GenerateProposalLabels = GenerateProposalLabelsOp()