文章目录[隐藏]
2021SC@SDUSC
Mask Rcnn整体结构
对应PaddleDetection的流程图
modeling/architecture/mask_rcnn.py源码解析
在yaml的配置：/configs/_base_/models/mask_rcnn_r50_fpn.yml
'''
# Model Achitecture
# MaskRCNNYOLOV3结构，包括了以下主要组
MaskRCNN:
# model anchor info flow
anchor: Anchor
proposal: Proposal
mask: Mask
# model feat info flow
backbone: ResNet #主干网络类名
neck: FPN #neck FPN类名
rpn_head: RPNHead #Head成员类名
bbox_head: BBoxHead #Head成员类名
mask_head: MaskHead #Head成员类名
# post process
bbox_post_process: BBoxPostProcess #BBox后处理
mask_post_process: MaskPostProcess #Mask后处理
'''相关库引用与定义
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from ppdet.core.workspace import register
from .meta_arch import BaseArch
__all__ = ['MaskRCNN']接下来是具体代码分析
@register
class MaskRCNN(BaseArch):
__category__ = 'architecture'
__inject__ = [
'anchor', #anchor成员类
'proposal', #proposal成员类
'mask', #mask成员类
'backbone', #backbone成员类
'neck', #neck成员类
'rpn_head', #rpn_head成员类
'bbox_head', #bbox_head成员类
'mask_head', #mask_head成员类
'bbox_post_process', #bbox_post_process成员类
'mask_post_process', #mask_post_process成员类
]
def __init__(self,
anchor,
proposal,
mask,
backbone,
rpn_head,
bbox_head,
mask_head,
bbox_post_process,
mask_post_process,
neck=None):
super(MaskRCNN, self).__init__()
self.anchor = anchor
self.proposal = proposal
self.mask = mask
self.backbone = backbone
self.neck = neck
self.rpn_head = rpn_head
self.bbox_head = bbox_head
self.mask_head = mask_head
self.bbox_post_process = bbox_post_process
self.mask_post_process = mask_post_process
}MaskRCNN类的定义，以及初始化函数。相关注释在代码中。
def model_arch(self):
# Backbone 主干网络
body_feats = self.backbone(self.inputs)
spatial_scale = 1. / 16
# Neck 颈
if self.neck is not None:
body_feats, spatial_scale = self.neck(body_feats)
# RPN 区域选取网络
# rpn_head returns two list: rpn_feat, rpn_head_out
# each element in rpn_feats contains rpn feature on each level,
# and the length is 1 when the neck is not applied.
# each element in rpn_head_out contains (rpn_rois_score, rpn_rois_delta)
rpn_feat, self.rpn_head_out = self.rpn_head(self.inputs, body_feats)
# Anchor 锚框
# anchor_out returns a list,
# each element contains (anchor, anchor_var)
self.anchor_out = self.anchor(rpn_feat)
# Proposal RoI 提供感兴趣区域
# compute targets here when training
rois = self.proposal(self.inputs, self.rpn_head_out, self.anchor_out)
# BBox Head BBox头
bbox_feat, self.bbox_head_out, self.bbox_head_feat_func = self.bbox_head(
body_feats, rois, spatial_scale)
rois_has_mask_int32 = None
if self.inputs['mode'] == 'infer':#推理过程中BBox特征又bbox头获取
bbox_pred, bboxes = self.bbox_head.get_prediction(
self.bbox_head_out, rois)
# Refine bbox by the output from bbox_head at test stage
self.bboxes = self.bbox_post_process(bbox_pred, bboxes,
self.inputs['im_shape'],
self.inputs['scale_factor'])
else:
# Proposal RoI for Mask branch 感兴趣区域从掩膜特征获取
# bboxes update at training stage only
bbox_targets = self.proposal.get_targets()[0]
self.bboxes, rois_has_mask_int32 = self.mask(self.inputs, rois,
bbox_targets)
# Mask Head 掩膜头
self.mask_head_out = self.mask_head(
self.inputs, body_feats, self.bboxes, bbox_feat,
rois_has_mask_int32, spatial_scale, self.bbox_head_feat_func)def get_loss(self, ):
loss = {}
# RPN loss 计算候选区域损失
rpn_loss_inputs = self.anchor.generate_loss_inputs(
self.inputs, self.rpn_head_out, self.anchor_out)
loss_rpn = self.rpn_head.get_loss(rpn_loss_inputs)
loss.update(loss_rpn)
# BBox loss 计算bbox损失
bbox_targets = self.proposal.get_targets()
loss_bbox = self.bbox_head.get_loss([self.bbox_head_out], bbox_targets)
loss.update(loss_bbox)
# Mask loss 计算掩膜损失
mask_targets = self.mask.get_targets()
loss_mask = self.mask_head.get_loss(self.mask_head_out, mask_targets)
loss.update(loss_mask)
#损失求和，更新loss
total_loss = paddle.add_n(list(loss.values()))
loss.update({'loss': total_loss})
return loss
def get_pred(self):#预测推理结果（框、数量、掩膜）
bbox, bbox_num = self.bboxes
output = {
'bbox': bbox,
'bbox_num': bbox_num,
'mask': self.mask_head_out
}
return output
版权声明：本文为CSDN博主「无情铁铲」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_45684033/article/details/122148350
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