RPNhead类代码解释

class RPNHead(nn.Module):
    """Network head of RPN.
                                  / - rpn_cls (1x1 conv)
    input - rpn_conv (3x3 conv) -
                                  \ - rpn_reg (1x1 conv)
    Args:
        in_channels (int): Number of channels in the input feature map.
        feat_channels (int): Number of channels for the RPN feature map.
        anchor_scales (Iterable): Anchor scales.
        anchor_ratios (Iterable): Anchor aspect ratios.
        anchor_strides (Iterable): Anchor strides.
        anchor_base_sizes (Iterable): Anchor base sizes.
        target_means (Iterable): Mean values of regression targets.
        target_stds (Iterable): Std values of regression targets.
        use_sigmoid_cls (bool): Whether to use sigmoid loss for classification.
            (softmax by default)
    """  # noqa: W605

    def __init__(self,
                 in_channels,
                 feat_channels=256,
                 anchor_scales=[8, 16, 32],
                 anchor_ratios=[0.5, 1.0, 2.0],
                 anchor_strides=[4, 8, 16, 32, 64],
                 anchor_base_sizes=None,
                 target_means=(.0, .0, .0, .0),
                 target_stds=(1.0, 1.0, 1.0, 1.0),
                 use_sigmoid_cls=False):
        super(RPNHead, self).__init__()
        self.in_channels = in_channels
        self.feat_channels = feat_channels
        self.anchor_scales = anchor_scales
        self.anchor_ratios = anchor_ratios
        self.anchor_strides = anchor_strides
        self.anchor_base_sizes = list(
            anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
        self.target_means = target_means
        self.target_stds = target_stds
        self.use_sigmoid_cls = use_sigmoid_cls

        self.anchor_generators = []
        for anchor_base in self.anchor_base_sizes:
            self.anchor_generators.append(
                AnchorGenerator(anchor_base, anchor_scales, anchor_ratios))
        self.rpn_conv = nn.Conv2d(in_channels, feat_channels, 3, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
        out_channels = (self.num_anchors
                        if self.use_sigmoid_cls else self.num_anchors * 2)
        self.rpn_cls = nn.Conv2d(feat_channels, out_channels, 1)
        self.rpn_reg = nn.Conv2d(feat_channels, self.num_anchors * 4, 1)
        self.debug_imgs = None

    def init_weights(self):
        normal_init(self.rpn_conv, std=0.01)
        normal_init(self.rpn_cls, std=0.01)
        normal_init(self.rpn_reg, std=0.01)

    def forward_single(self, x):
        rpn_feat = self.relu(self.rpn_conv(x))
        rpn_cls_score = self.rpn_cls(rpn_feat)
        rpn_bbox_pred = self.rpn_reg(rpn_feat)
        return rpn_cls_score, rpn_bbox_pred

    def forward(self, feats):
        return multi_apply(self.forward_single, feats)

    def get_anchors(self, featmap_sizes, img_metas):
        """Get anchors according to feature map sizes.
        Args:
            featmap_sizes (list[tuple]): Multi-level feature map sizes.
            img_metas (list[dict]): Image meta info.
        Returns:
            tuple: anchors of each image, valid flags of each image
        """
        num_imgs = len(img_metas)
        num_levels = len(featmap_sizes)

        # since feature map sizes of all images are the same, we only compute
        # anchors for one time
        multi_level_anchors = []
        for i in range(num_levels):
            anchors = self.anchor_generators[i].grid_anchors(
                featmap_sizes[i], self.anchor_strides[i])
            multi_level_anchors.append(anchors)
        anchor_list = [multi_level_anchors for _ in range(num_imgs)]

        # for each image, we compute valid flags of multi level anchors
        valid_flag_list = []
        for img_id, img_meta in enumerate(img_metas):
            multi_level_flags = []
            for i in range(num_levels):
                anchor_stride = self.anchor_strides[i]
                feat_h, feat_w = featmap_sizes[i]
                h, w, _ = img_meta['pad_shape']
                valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
                valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
                flags = self.anchor_generators[i].valid_flags(
                    (feat_h, feat_w), (valid_feat_h, valid_feat_w))
                multi_level_flags.append(flags)
            valid_flag_list.append(multi_level_flags)

        return anchor_list, valid_flag_list

    def loss_single(self, rpn_cls_score, rpn_bbox_pred, labels, label_weights,
                    bbox_targets, bbox_weights, num_total_samples, cfg):
        # classification loss
        labels = labels.contiguous().view(-1)
        label_weights = label_weights.contiguous().view(-1)
        if self.use_sigmoid_cls:
            rpn_cls_score = rpn_cls_score.permute(0, 2, 3,
                                                  1).contiguous().view(-1)
            criterion = weighted_binary_cross_entropy
        else:
            rpn_cls_score = rpn_cls_score.permute(0, 2, 3,
                                                  1).contiguous().view(-1, 2)
            criterion = weighted_cross_entropy
        loss_cls = criterion(
            rpn_cls_score, labels, label_weights, avg_factor=num_total_samples)
        # regression loss
        bbox_targets = bbox_targets.contiguous().view(-1, 4)
        bbox_weights = bbox_weights.contiguous().view(-1, 4)
        rpn_bbox_pred = rpn_bbox_pred.permute(0, 2, 3, 1).contiguous().view(
            -1, 4)
        loss_reg = weighted_smoothl1(
            rpn_bbox_pred,
            bbox_targets,
            bbox_weights,
            beta=cfg.smoothl1_beta,
            avg_factor=num_total_samples)
        return loss_cls, loss_reg

    def loss(self, rpn_cls_scores, rpn_bbox_preds, gt_bboxes, img_shapes, cfg):
        featmap_sizes = [featmap.size()[-2:] for featmap in rpn_cls_scores]
        assert len(featmap_sizes) == len(self.anchor_generators)

        anchor_list, valid_flag_list = self.get_anchors(
            featmap_sizes, img_shapes)
        cls_reg_targets = anchor_target(
            anchor_list, valid_flag_list, gt_bboxes, img_shapes,
            self.target_means, self.target_stds, cfg)
        if cls_reg_targets is None:
            return None
        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
         num_total_pos, num_total_neg) = cls_reg_targets
        losses_cls, losses_reg = multi_apply(
            self.loss_single,
            rpn_cls_scores,
            rpn_bbox_preds,
            labels_list,
            label_weights_list,
            bbox_targets_list,
            bbox_weights_list,
            num_total_samples=num_total_pos + num_total_neg,
            cfg=cfg)
        return dict(loss_rpn_cls=losses_cls, loss_rpn_reg=losses_reg)

    def get_proposals(self, rpn_cls_scores, rpn_bbox_preds, img_meta, cfg):
        num_imgs = len(img_meta)
        featmap_sizes = [featmap.size()[-2:] for featmap in rpn_cls_scores]
        mlvl_anchors = [
            self.anchor_generators[idx].grid_anchors(featmap_sizes[idx],
                                                     self.anchor_strides[idx])
            for idx in range(len(featmap_sizes))
        ]
        proposal_list = []
        for img_id in range(num_imgs):
            rpn_cls_score_list = [
                rpn_cls_scores[idx][img_id].detach()
                for idx in range(len(rpn_cls_scores))
            ]
            rpn_bbox_pred_list = [
                rpn_bbox_preds[idx][img_id].detach()
                for idx in range(len(rpn_bbox_preds))
            ]
            assert len(rpn_cls_score_list) == len(rpn_bbox_pred_list)
            proposals = self._get_proposals_single(
                rpn_cls_score_list, rpn_bbox_pred_list, mlvl_anchors,
                img_meta[img_id]['img_shape'], cfg)
            proposal_list.append(proposals)
        return proposal_list

    def _get_proposals_single(self, rpn_cls_scores, rpn_bbox_preds,
                              mlvl_anchors, img_shape, cfg):
        mlvl_proposals = []
        for idx in range(len(rpn_cls_scores)):
            rpn_cls_score = rpn_cls_scores[idx]
            rpn_bbox_pred = rpn_bbox_preds[idx]
            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
            anchors = mlvl_anchors[idx]
            if self.use_sigmoid_cls:
                rpn_cls_score = rpn_cls_score.permute(1, 2,
                                                      0).contiguous().view(-1)
                rpn_cls_prob = rpn_cls_score.sigmoid()
                scores = rpn_cls_prob
            else:
                rpn_cls_score = rpn_cls_score.permute(1, 2,
                                                      0).contiguous().view(
                                                          -1, 2)
                rpn_cls_prob = F.softmax(rpn_cls_score, dim=1)
                scores = rpn_cls_prob[:, 1]
            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).contiguous().view(
                -1, 4)
            _, order = scores.sort(0, descending=True)
            if cfg.nms_pre > 0:
                order = order[:cfg.nms_pre]
                rpn_bbox_pred = rpn_bbox_pred[order, :]
                anchors = anchors[order, :]
                scores = scores[order]
            proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means,
                                   self.target_stds, img_shape)
            w = proposals[:, 2] - proposals[:, 0] + 1
            h = proposals[:, 3] - proposals[:, 1] + 1
            valid_inds = torch.nonzero((w >= cfg.min_bbox_size) &
                                       (h >= cfg.min_bbox_size)).squeeze()
            proposals = proposals[valid_inds, :]
            scores = scores[valid_inds]
            proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1)
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.nms_post, :]
            mlvl_proposals.append(proposals)
        proposals = torch.cat(mlvl_proposals, 0)
        if cfg.nms_across_levels:
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.max_num, :]
        else:
            scores = proposals[:, 4]
            _, order = scores.sort(0, descending=True)
            num = min(cfg.max_num, proposals.shape[0])
            order = order[:num]
            proposals = proposals[order, :]
        return proposals

RPNhead处于哪一部分呢？，借用某个博客的图

RPNhead就是以上绿色分区的倒数第二块。
简单来说就是，RPN网络计算了anchor之后，进入RPNhead，计算得到 分类的预测 和 回归的预测。

这个类分成init init_weights forward_single forward get_anchors loss_single loss get_proposals _get_proposals_single
也就是 初始化、forward训练部分、获取anchors 和计算loss、得到proposals 几个部分。

前几个比较简单，主要看一下得到proposal的部分。
在训练时，

只计算处预测分类和预测回归，用来计算loss即可。
所以我们的 proposal 方法是用在inference时，得到RPN的结果——proposals ，大小是 (cfg.max_num, 5)，前四列坐标，第五列 预测得分

看_get_proposals_single 方法，这个方法计算一张图片的proposals

 def _get_proposals_single(self, rpn_cls_scores, rpn_bbox_preds,
                              mlvl_anchors, img_shape, cfg):
        mlvl_proposals = []
        for idx in range(len(rpn_cls_scores)):
            rpn_cls_score = rpn_cls_scores[idx]
            rpn_bbox_pred = rpn_bbox_preds[idx]
            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
            anchors = mlvl_anchors[idx]
            if self.use_sigmoid_cls:
                rpn_cls_score = rpn_cls_score.permute(1, 2,
                                                      0).contiguous().view(-1)
                rpn_cls_prob = rpn_cls_score.sigmoid()
                scores = rpn_cls_prob
            else:
                rpn_cls_score = rpn_cls_score.permute(1, 2,
                                                      0).contiguous().view(
                                                          -1, 2)
                rpn_cls_prob = F.softmax(rpn_cls_score, dim=1)
                scores = rpn_cls_prob[:, 1]
            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).contiguous().view(
                -1, 4)
            _, order = scores.sort(0, descending=True)
            if cfg.nms_pre > 0:
                order = order[:cfg.nms_pre]
                rpn_bbox_pred = rpn_bbox_pred[order, :]
                anchors = anchors[order, :]
                scores = scores[order]
            proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means,
                                   self.target_stds, img_shape)
            w = proposals[:, 2] - proposals[:, 0] + 1
            h = proposals[:, 3] - proposals[:, 1] + 1
            valid_inds = torch.nonzero((w >= cfg.min_bbox_size) &
                                       (h >= cfg.min_bbox_size)).squeeze()
            proposals = proposals[valid_inds, :]
            scores = scores[valid_inds]
            proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1)
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.nms_post, :]
            mlvl_proposals.append(proposals)
        proposals = torch.cat(mlvl_proposals, 0)
        if cfg.nms_across_levels:
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.max_num, :]
        else:
            scores = proposals[:, 4]
            _, order = scores.sort(0, descending=True)
            num = min(cfg.max_num, proposals.shape[0])
            order = order[:num]
            proposals = proposals[order, :]
        return proposals

在get_proposals方法中我们计算得到所有的图片的proposals， 循环调用num images次的 _get_proposals_single。
首先_get_proposals_single 的输入 rpn_cls_scores, rpn_bbox_preds, mlvl_anchors都是multi level的，也就是形如 num_level , channel , h ,w . 所以， 主体部分在一个for循环中，分别计算每个level的proposal 然后合并到一起。
for循环中，
一开始得到该level下的anchor，用在之后计算绝对坐标。
接着决定对 预测分数做 sigmod还是softmax， 如果是sigmod的方法，在初始化init中已经看到，分数只有一列，不分两类，所以输出通道数是 out_channels = (self.num_anchors if self.use_sigmoid_cls else self.num_anchors * 2)，而softmax是两倍。
同样对 预测回归 调整形状，
紧接着对分数进行排序，根据 cfg.nums_pre 去掉一波结果，接着做NMS算法，这里有阈值设定。之后根据cfg.nums_post 再筛选一波。比如得到2000个proposal。
这样我们得到了某个level下的2000个proposal。同理，计算多个level的proposal。
多尺度下的结果proposals = torch.cat(mlvl_proposals, 0) 。
接着，有两种手段做最后一次筛选，可以再做一次NMS算法，选择最后需要的cfg.max_num个proposal
或者，直接排序 预测分数， 选择 前cfg.max_num个proposal