四个层的forward函数分析：
RoIDataLayer：读数据，随机打乱等
AnchorTargetLayer：输出所有anchors（这里分析这个）
ProposalLayer：用产生的anchors平移整图，裁剪出界、移除低于阈值的的anchors，排序后使用nms，返回顶部排名的anchors
ProposalTargetLayer：将proposals分配给gt物体。得出proposal的分类标签和box的回归目标。
紧接着之前的博客，我们继续来看faster rcnn中的AnchorTargetLayer层：
class AnchorTargetLayer(caffe.Layer): """ Assign anchors to ground-truth targets. Produces anchor classification labels and bounding-box regression targets. """ def setup(self, bottom, top): layer_params = yaml.load(self.param_str_) anchor_scales = layer_params.get('scales', (8, 16, 32)) self._anchors = generate_anchors(scales=np.array(anchor_scales)) self._num_anchors = self._anchors.shape[0] self._feat_stride = layer_params['feat_stride'] if DEBUG: print 'anchors:' print self._anchors print 'anchor shapes:' print np.hstack(( self._anchors[:, 2::4] - self._anchors[:, 0::4], self._anchors[:, 3::4] - self._anchors[:, 1::4], )) self._counts = cfg.EPS self._sums = np.zeros((1, 4)) self._squared_sums = np.zeros((1, 4)) self._fg_sum = 0 self._bg_sum = 0 self._count = 0 # allow boxes to sit over the edge by a small amount self._allowed_border = layer_params.get('allowed_border', 0) height, width = bottom[0].data.shape[-2:] if DEBUG: print 'AnchorTargetLayer: height', height, 'width', width A = self._num_anchors # labels top[0].reshape(1, 1, A * height, width) # bbox_targets top[1].reshape(1, A * 4, height, width) # bbox_inside_weights top[2].reshape(1, A * 4, height, width) # bbox_outside_weights top[3].reshape(1, A * 4, height, width)
首先说一下这一层的目的是输出在特征图上所有点的anchors（经过二分类和回归）；
（1）输入blob：bottom[0]储存特征图信息，bottom[1]储存gt框坐标,bottom[2]储存im_info信息；
（2）输出blob：top[0]存储anchors的label值（fg是1,bg是0,-1类不关心）,top[1]存储的是生成的anchors的回归偏移量，即论文中的tx,ty,tw,th四个量（所以说整个faster rcnn总共两次bbox回归，第一次在RPN中，第二次在fast rcnn中），top[2]和top[3]分别存储的是bbox_inside_weights和bbox_outside_weights。
好的，先进入层的setup函数：该函数通过解析父类对自己的一些参数进行初始化，同时定义该层的输入输出blob;
该函数中要注意的是generate_anchors()函数，它的作用是产生对应与特征图上最左上角那个点的九种anchor（尺寸对应与输入图像），这9个anchor在后面被用来产生所有图像上的anchors，进入generate_anchors（）函数。前面博客做过分析了，不再累述。
接着向下看该层的前向传播函数forward函数：
def forward(self, bottom, top): # Algorithm: # # for each (H, W) location i # generate 9 anchor boxes centered on cell i # apply predicted bbox deltas at cell i to each of the 9 anchors # filter out-of-image anchors # measure GT overlap assert bottom[0].data.shape[0] == 1, \ 'Only single item batches are supported' # map of shape (..., H, W) height, width = bottom[0].data.shape[-2:] ##bottom[0]特征图信息，bottom[1]gt坐标，bottom[3]为im_info # GT boxes (x1, y1, x2, y2, label) gt_boxes = bottom[1].data # im_info im_info = bottom[2].data[0, :] if DEBUG: print '' print 'im_size: ({}, {})'.format(im_info[0], im_info[1]) print 'scale: {}'.format(im_info[2]) print 'height, width: ({}, {})'.format(height, width) print 'rpn: gt_boxes.shape', gt_boxes.shape print 'rpn: gt_boxes', gt_boxes # 1. Generate proposals from bbox deltas and shifted anchors shift_x = np.arange(0, width) * self._feat_stride ##映射原图的偏移量 shift_y = np.arange(0, height) * self._feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # add A anchors (1, A, 4) to # cell K shifts (K, 1, 4) to get # shift anchors (K, A, 4) # reshape to (K*A, 4) shifted anchors A = self._num_anchors K = shifts.shape[0] all_anchors = (self._anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))) ##左上角anchor进行偏移覆盖全图 all_anchors = all_anchors.reshape((K * A, 4)) total_anchors = int(K * A) # only keep anchors inside the image ，保留位置在图像内的anchors inds_inside = np.where( (all_anchors[:, 0] >= -self._allowed_border) & (all_anchors[:, 1] >= -self._allowed_border) & (all_anchors[:, 2] < im_info[1] + self._allowed_border) & # width (all_anchors[:, 3] < im_info[0] + self._allowed_border) # height )[0] if DEBUG: print 'total_anchors', total_anchors print 'inds_inside', len(inds_inside) # keep only inside anchors anchors = all_anchors[inds_inside, :] if DEBUG: print 'anchors.shape', anchors.shape
########################################################################################################################
##这里的shift_x和shift_y分别对应x和y轴上的偏移量，用在之前说过的用generate_anchors（）函数生成的最左上角的anchors上，
##对其进行偏移，从而获得所有图像上的anchors；all_anchors用来存储所有这些anchors，total_anchors用来存储这些anchors的数量K×A,其中，
##K是输入图像的num,A是一幅图像上anchor的num；之后作者还对这些anchors进行了筛选，超出图像边界的anchors都将其丢弃～继续：
##########################################################################################################################
# label: 1 is positive, 0 is negative, -1 is dont care labels = np.empty((len(inds_inside), ), dtype=np.float32) labels.fill(-1) # overlaps between the anchors and the gt boxes # overlaps (ex, gt) overlaps = bbox_overlaps( np.ascontiguousarray(anchors, dtype=np.float), np.ascontiguousarray(gt_boxes, dtype=np.float)) ##n*k,重叠率 argmax_overlaps = overlaps.argmax(axis=1) max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] gt_argmax_overlaps = overlaps.argmax(axis=0) gt_max_overlaps = overlaps[gt_argmax_overlaps, np.arange(overlaps.shape[1])] gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] if not cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels first so that positive labels can clobber them labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # fg label: for each gt, anchor with highest overlap labels[gt_argmax_overlaps] = 1 # fg label: above threshold IOU labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 if cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels last so that negative labels can clobber positives labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 #################################################################################################################
##这一部分主要就是获得这些anchors和对应gt的最大重叠率的情况，以及正样本的划分标准：a.对于每一个gt，重叠率最大的那个anchor为fg；
##b,对于每一个gt，最大重叠率大于0.7的为fg；
#################################################################################################################
# subsample positive labels if we have too many 正样本太多就采样 num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE) fg_inds = np.where(labels == 1)[0] if len(fg_inds) > num_fg: disable_inds = npr.choice( fg_inds, size=(len(fg_inds) - num_fg), replace=False) labels[disable_inds] = -1 # subsample negative labels if we have too many num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1) bg_inds = np.where(labels == 0)[0] if len(bg_inds) > num_bg: disable_inds = npr.choice( bg_inds, size=(len(bg_inds) - num_bg), replace=False) labels[disable_inds] = -1 #print "was %s inds, disabling %s, now %s inds" % ( #len(bg_inds), len(disable_inds), np.sum(labels == 0)) bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32) bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS) bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32) if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: # uniform weighting of examples (given non-uniform sampling) num_examples = np.sum(labels >= 0) positive_weights = np.ones((1, 4)) * 1.0 / num_examples negative_weights = np.ones((1, 4)) * 1.0 / num_examples else: assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) & (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1)) positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT / np.sum(labels == 1)) negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) / np.sum(labels == 0)) bbox_outside_weights[labels == 1, :] = positive_weights bbox_outside_weights[labels == 0, :] = negative_weights if DEBUG: self._sums += bbox_targets[labels == 1, :].sum(axis=0) self._squared_sums += (bbox_targets[labels == 1, :] ** 2).sum(axis=0) self._counts += np.sum(labels == 1) means = self._sums / self._counts stds = np.sqrt(self._squared_sums / self._counts - means ** 2) print 'means:' print means print 'stdevs:' print stds # map up to original set of anchors
##这里则是通过_unmap()函数实现将之前在所有图像上产生的anchors都赋上label、bbox_targets、bbox_inside_weights、bbox_outside_weights属性
labels = _unmap(labels, total_anchors, inds_inside, fill=-1) bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0) bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0) bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0) if DEBUG: print 'rpn: max max_overlap', np.max(max_overlaps) print 'rpn: num_positive', np.sum(labels == 1) print 'rpn: num_negative', np.sum(labels == 0) self._fg_sum += np.sum(labels == 1) self._bg_sum += np.sum(labels == 0) self._count += 1 print 'rpn: num_positive avg', self._fg_sum / self._count print 'rpn: num_negative avg', self._bg_sum / self._count # labels labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2) labels = labels.reshape((1, 1, A * height, width)) top[0].reshape(*labels.shape) top[0].data[...] = labels # bbox_targets bbox_targets = bbox_targets \ .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2) top[1].reshape(*bbox_targets.shape) top[1].data[...] = bbox_targets # bbox_inside_weights bbox_inside_weights = bbox_inside_weights \ .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2) assert bbox_inside_weights.shape[2] == height assert bbox_inside_weights.shape[3] == width top[2].reshape(*bbox_inside_weights.shape) top[2].data[...] = bbox_inside_weights # bbox_outside_weights bbox_outside_weights = bbox_outside_weights \ .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2) assert bbox_outside_weights.shape[2] == height assert bbox_outside_weights.shape[3] == width top[3].reshape(*bbox_outside_weights.shape) top[3].data[...] = bbox_outside_weights
这一部分是生成bbox_targets、bbox_inside_weights、bbox_inside_weights；其中对于bbox_targets，它这里是调用了_compute_targets()函数，见：
def _compute_targets(ex_rois, gt_rois): """Compute bounding-box regression targets for an image.""" assert ex_rois.shape[0] == gt_rois.shape[0] assert ex_rois.shape[1] == 4 assert gt_rois.shape[1] == 5 return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
在该函数又接着调用了bbox_transform函数，见：
def bbox_transform(ex_rois, gt_rois): ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights targets_dw = np.log(gt_widths / ex_widths) targets_dh = np.log(gt_heights / ex_heights) targets = np.vstack( (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() return targets
从而得到了论文中所需要的四个偏移量tx,ty,tw,th四个量；
而对于后两个bbox_inside_weights和bbox_outside_weights，函数中定义的是bbox_inside_weights初始化为n×4的0数组，然后其中正样本的坐标的权值均为1；而bbox_outside_weights同样的初始化，其中正样本和负样本都被赋值1/num(anchors的数量)。