Faster R-CNN源代码中faster_rcnn文件夹中包含三个文件 faster_rcnn.py，resnet.py，vgg16.py。
1.faster_rcnn.py注释
1 class _fasterRCNN(nn.Module): 2 """ faster RCNN """ 3 def __init__(self, classes, class_agnostic):#class-agnostic 方式只回归2类bounding box,即前景和背景 4 super(_fasterRCNN, self).__init__() 5 self.classes = classes #类别 6 self.n_classes = len(classes)#类别数 7 self.class_agnostic = class_agnostic #前景背景类 8 # loss 两种loss 9 self.RCNN_loss_cls = 0 10 self.RCNN_loss_bbox = 0 11 12 # define rpn 定义RPN网络 13 self.RCNN_rpn = _RPN(self.dout_base_model) 14 self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)#候选区域对应gt 15 self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)#POOLING 16 self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) 17 18 self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE 19 self.RCNN_roi_crop = _RoICrop() 20 21 def forward(self, im_data, im_info, gt_boxes, num_boxes):#图像 图像信息 标注信息 框数目 22 batch_size = im_data.size(0) 23 24 im_info = im_info.data 25 gt_boxes = gt_boxes.data 26 num_boxes = num_boxes.data 27 28 # feed image data to base model to obtain base feature map 29 #将图像数据馈送到基础模型以获得基础特征图 30 base_feat = self.RCNN_base(im_data) 31 32 # feed base feature map tp RPN to obtain rois 33 # 特征图反馈到RPN得到ROIS 34 rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) 35 36 # if it is training phrase, then use ground trubut bboxes for refining 37 #如果是在训练 用ground truth回归 38 if self.training: 39 roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes) 40 rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws = roi_data 41 42 rois_label = Variable(rois_label.view(-1).long()) 43 rois_target = Variable(rois_target.view(-1, rois_target.size(2))) 44 rois_inside_ws = Variable(rois_inside_ws.view(-1, rois_inside_ws.size(2))) 45 rois_outside_ws = Variable(rois_outside_ws.view(-1, rois_outside_ws.size(2))) 46 else: 47 rois_label = None 48 rois_target = None 49 rois_inside_ws = None 50 rois_outside_ws = None 51 rpn_loss_cls = 0 52 rpn_loss_bbox = 0 53 54 rois = Variable(rois) 55 # do roi pooling based on predicted rois 56 #进行ROI POOLING，下面pooling方式 57 58 if cfg.POOLING_MODE == 'crop': 59 # pdb.set_trace() 60 # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5)) 61 grid_xy = _affine_grid_gen(rois.view(-1, 5), base_feat.size()[2:], self.grid_size) 62 grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous() 63 pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach()) 64 if cfg.CROP_RESIZE_WITH_MAX_POOL: 65 pooled_feat = F.max_pool2d(pooled_feat, 2, 2) 66 elif cfg.POOLING_MODE == 'align': 67 pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5)) 68 elif cfg.POOLING_MODE == 'pool': 69 pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1,5)) 70 71 # feed pooled features to top model 72 #pooling后的特征反馈到上次模型 73 pooled_feat = self._head_to_tail(pooled_feat) 74 75 # compute bbox offset 76 #计算bounding box的偏移 77 bbox_pred = self.RCNN_bbox_pred(pooled_feat) 78 if self.training and not self.class_agnostic: 79 # select the corresponding columns according to roi labels 80 # 根据roi标签选择相应的列 81 bbox_pred_view = bbox_pred.view(bbox_pred.size(0), int(bbox_pred.size(1) / 4), 4) 82 bbox_pred_select = torch.gather(bbox_pred_view, 1, rois_label.view(rois_label.size(0), 1, 1).expand(rois_label.size(0), 1, 4)) 83 bbox_pred = bbox_pred_select.squeeze(1) 84 85 # compute object classification probability 86 # 计算对象分类概率 87 cls_score = self.RCNN_cls_score(pooled_feat) 88 cls_prob = F.softmax(cls_score, 1) 89 90 RCNN_loss_cls = 0 91 RCNN_loss_bbox = 0 92 93 if self.training: 94 # classification loss 95 #分类损失 96 RCNN_loss_cls = F.cross_entropy(cls_score, rois_label) 97 98 # bounding box regression L1 loss 99 #回归损失 100 RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws) 101 102 103 cls_prob = cls_prob.view(batch_size, rois.size(1), -1) 104 bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1) 105 106 return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label 107 108 #初始化权重 109 def _init_weights(self): 110 def normal_init(m, mean, stddev, truncated=False):#均值 标准差 111 #截断正态 随机正态 112 """ 113 weight initalizer: truncated normal and random normal. 114 """ 115 # x is a parameter 116 if truncated: 117 m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation 118 else: 119 m.weight.data.normal_(mean, stddev) 120 m.bias.data.zero_() 121 122 normal_init(self.RCNN_rpn.RPN_Conv, 0, 0.01, cfg.TRAIN.TRUNCATED) 123 normal_init(self.RCNN_rpn.RPN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 124 normal_init(self.RCNN_rpn.RPN_bbox_pred, 0, 0.01, cfg.TRAIN.TRUNCATED) 125 normal_init(self.RCNN_cls_score, 0, 0.01, cfg.TRAIN.TRUNCATED) 126 normal_init(self.RCNN_bbox_pred, 0, 0.001, cfg.TRAIN.TRUNCATED) 127 128 def create_architecture(self): 129 self._init_modules() 130 self._init_weights()
ref:https://blog.csdn.net/weixin_43872578/article/details/87930953