def add_generic_rpn_outputs(model, blob_in, dim_in, spatial_scale_in):
"""Add RPN outputs (objectness classification and bounding box regression)
to an RPN model. Abstracts away the use of FPN.
是否为目标，对anchor进行回归
"""
loss_gradients = None
if cfg.FPN.FPN_ON:
# Delegate to the FPN module
# 添加proposals层，输出为(bacth_index, x1, y1, x2, y2)
# 1. 获得rpn的输出，MxNxAnchors, MxNx4Anchors;
# 2. 对fpn中的每一层，获取ROIs
FPN.add_fpn_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
if cfg.MODEL.FASTER_RCNN:
# CollectAndDistributeFpnRpnProposals also labels proposals when in
# training mode
# 在该函数中构造候选区域的目标值，回归量以及类别，权重
model.CollectAndDistributeFpnRpnProposals()
if model.train:
loss_gradients = FPN.add_fpn_rpn_losses(model)
else:
# Not using FPN, add RPN to a single scale
# 添加输出
add_single_scale_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
if model.train:
# 添加loss
loss_gradients = add_single_scale_rpn_losses(model)
return loss_gradients
def add_generic_rpn_outputs(model, blob_in, dim_in, spatial_scale_in):
"""Add RPN outputs (objectness classification and bounding box regression)
to an RPN model. Abstracts away the use of FPN.
"""
loss_gradients = None
if cfg.FPN.FPN_ON:
# Delegate to the FPN module
FPN.add_fpn_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
if cfg.MODEL.FASTER_RCNN:
# CollectAndDistributeFpnRpnProposals also labels proposals when in
# training mode
model.CollectAndDistributeFpnRpnProposals()
if model.train:
loss_gradients = FPN.add_fpn_rpn_losses(model)
return loss_gradients