标签：ast sample order over span cnn class ide osi
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] # keep only inside anchors anchors = all_anchors[inds_inside, :]
这部分代码是把所有anchor中超过了图片边界部分的anchor去掉，即论文中说的cross-boundary anchors
# 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
这部分代码是把和gt-roi有最大iou的anchor和与任何gt-roi iou大于0.7的anchor的label置为1，即前景。这和论文中所说的是一样的。
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
把和所有gt-roi iou都小于0.3的achor的label置为0
# label: 1 is positive, 0 is negative, -1 is dont care labels = np.empty((len(inds_inside), ), dtype=np.float32) labels.fill(-1)
这是label的初始化的代码，所有的label都置为-1
所以总的来看，label分为3类，一类是0,即背景label；一类是1，即前景label；另一类既不是前景也不是背景，置为-1。论文中说只有前景和背景对训练目标有用，这种-1的label对训练没用。
# subsample positive labels if we have too many 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))
论文中说从所有anchor中随机选取256个anchor，前景128个，背景128个。注意：那种label为-1的不会当前景也不会当背景。
这两段代码是前一部分是在所有前景的anchor中选128个，后一部分是在所有的背景achor中选128个。如果前景的个数少于了128个，就把所有的anchor选出来，差的由背景部分补。这和fast rcnn选取roi一样。
标签：ast sample order over span cnn class ide osi
原文地址：http://www.cnblogs.com/ymjyqsx/p/7601841.html
