anchor_target_layer层其他部分解读

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。