RPN网络代码解读

傷城~ 2022-04-12 05:53 801阅读 0赞

# 1. 说在前面的话 #

在目标检测领域Faster RCNN可以说是无人不知无人不晓，它里面有一个网络结构RPN（Region Proposal Network）用于在特征图上产生候选预测区域。但是呢，这个网络结构具体是怎么工作的呢？网上有很多种解释，但是都是云里雾里的，还是直接撸代码来得直接，这里就直接从代码入手直接撸吧-\_-||。  
首先，来看一下Faster RCNN中RPN的结构是什么样子的吧。可以看到RPN直接通过一个卷积层rpn\_conv/3x3直接接在了分类网络的特征层输出上面，之后接上两个卷积层rpn\_clc\_score与rpn\_bbox\_pred分别用于产生前景背景分类与预测框。之后再由python层AnchorTargetLayer产生anchor机制的分类与预测框。然后，经过ROI Proposal产生ROI区域的候选，并通过ROI Pooling规范到相同的尺寸上进行后续处理。大体的结构如下图所示：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21fYnVkZHk_size_16_color_FFFFFF_t_70]  
虽然在上面的图中能够对RPN网络有一个比较直观但是笼统的概念，其具体内部搞了啥子，并不清楚。所以还是撸一下它里面的代码看看吧，首先来看RPN模块中各个文件说明。  
（1）generate\_anchors.py  
在\[0,0,15,15\]基础anchor的基础上生成不同宽高比例以及缩放大小的anchor。  
Generates a regular grid of multi-scale, multi-aspect anchor boxes.  
（2）proposal\_layer.py  
将RPN网络的每个anchor的分类得分以及检测框回归预估转换为目标候选  
Converts RPN outputs (per-anchor scores and bbox regression estimates) into object proposals.  
（3）anchor\_target\_layer.py  
为每个anchor生成训练目标或标签，分类的标签只是0（非目标）1（是目标）-1（忽略）。当分类的标签大于0的时候预测框的回归才被指定。  
Generates training targets/labels for each anchor. Classification labels are 1 (object), 0 (not object) or -1 (ignore).  
Bbox regression targets are specified when the classification label is > 0.  
（4）proposal\_target\_layer.py  
为每个目标候选生成训练目标或标签，分类标签从 0 − K 0-K 0−K（背景0或目标类别 1 , … , K 1, \\dots, K 1,…,K），自然lable值大于0的才被指定预测框回归。  
Generates training targets/labels for each object proposal: classification labels 0 - K (bg or object class 1, … , K)  
and bbox regression targets in that case that the label is > 0.  
（5）[generate.py][]  
使用RPN从IMDB输入数据上产生目标候选。  
Generate object detection proposals from an imdb using an RPN.  
现在对RPN网络的结构和RPN模块中文件有了一个大体的认识，那么接下来就开始阅读里面的实现代码，看看它究竟干了些什么事情。

# 2. RPN网络部分 #

这个部分使用到的文件有anchor\_target\_layer.py、generate\_anchors.py。这里的generate\_anchors.py是用来产生模型需要的anchor的，其中也包含了一些其它的辅助函数，它不是讲解说明的重点，这里不作介绍。主要来看anchor\_target\_layer.py文件。  
首先，来看看这个层的初始化函数：

def setup(self, bottom, top):
        layer_params = yaml.load(self.param_str_)
        anchor_scales = layer_params.get('scales', (8, 16, 32)) # 尺度变化参数
        self._anchors = generate_anchors(scales=np.array(anchor_scales)) # 生成默认的9个anchor
        self._num_anchors = self._anchors.shape[0]
        self._feat_stride = layer_params['feat_stride']
    
        # allow boxes to sit over the edge by a small amount
    	# 设为0，则取出任何超过图像边界的proposals，只要超出一点点，都要去除
        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 是否为目标的分类
        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)

接下来就是重头的forward函数，首先，该函数在特征图生成需要运算的总的anchor

# 1. Generate proposals from bbox deltas and shifted anchors
    # x方向的偏移个数，大小为特征图的width
    shift_x = np.arange(0, width) * self._feat_stride
    # y方向的偏移个数，大小为特征图的height
    shift_y = np.arange(0, height) * self._feat_stride
    # shift_x，shift_y均为width×height的二维数组（meshgrid生成），对应位置的元素组合即构成图像上需要偏移量大小
    #（偏移量大小是相对与图像最左上角的那9个anchor的偏移量大小），也就是说总共会得到width×height×9个偏移值对。
    # 这些偏移值对与初始的anchor相加即可得到
    # 所有的anchors，所以总共会产生width×height×9个anchors，且存储在all_anchors变量中
    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() # 维度输出为(width*height)*4
    # 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] # K=width*height
    # 在之前9个anchor的基础上产生K*A个anchor，既是总的anchor数量
    all_anchors = (self._anchors.reshape((1, A, 4)) +
                   shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
    all_anchors = all_anchors.reshape((K * A, 4))
    total_anchors = int(K * A) # 总的anchor数量

产生这么多的anchor自然有一些超出了边界，那么就需要对其进行剔除

# only keep anchors inside the image 在图像内部的anchor，即是有效anchor，边界之外的删除掉
    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]

初始化可用anchor对应的lable，分类标签的含义下面写了

# label: 1 is positive, 0 is negative, -1 is dont care
    # 图像内部anchor对应的分类，是否为目标的分类，大小为符合条件anchor的数量
    labels = np.empty((len(inds_inside), ), dtype=np.float32)
    labels.fill(-1)

在之前生成了计算需要的anchor了那么接下来就是需要计算anchor与gt之间的关系了，也就是使用overlap area的面积来度量，每个anchor的是否为目标分类也是根据这个度量来设置的。

# overlaps between the anchors and the gt boxes
    # overlaps (ex, gt)返回维度为【anchors * gt_boxes】大小的二维数组
    overlaps = bbox_overlaps(
        np.ascontiguousarray(anchors, dtype=np.float),
        np.ascontiguousarray(gt_boxes, dtype=np.float))
    argmax_overlaps = overlaps.argmax(axis=1) # 求取于anchor重叠最大的gt
    max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] # 取出与每个anchor重叠最大gt的重叠面积
    gt_argmax_overlaps = overlaps.argmax(axis=0) # 求出与每个gt重叠面积最大的anchor
    gt_max_overlaps = overlaps[gt_argmax_overlaps,
                                       np.arange(overlaps.shape[1])] # 取出与每个gt重叠面积最大的
    gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
    
    # 重叠面积小于阈值0.3的标注为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 与gt图重叠最大的对应anchor分类被设置为1
    labels[gt_argmax_overlaps] = 1
    
    # fg label: above threshold IOU 将与gt重叠的面积大于阈值0.7的anchor也将其分类设置为1
    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

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。  
下面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anchor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。

# subsample positive labels if we have too many 要是运行到这里得到的分类为1的太多了那就进行采样
    # 从所有label为1的anchor中选择128个，剩下的anchor的label全部置为-1
    num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE) # 采样的阈值
    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 要是被分类为非1的太多了那么也要进行采样
    # 这里num_bg不是直接设为128，而是256减去label为1的个数，这样如果label为1的不够，就用label为0的填充，这个代码实现很巧
    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

论文中RPN的损失函数是这样定义的：  
![在这里插入图片描述][20181201213854403.png]  
这个loss函数和Fast RCNN中的loss函数差不多，所以在计算的时候是每个坐标单独进行smoothL1计算，所以参数 P i ∗ Pi^\* Pi∗和 N r e g N\_\{reg\} Nreg必须弄成4维的向量，并不是在论文中的就一个数值。  
bbox\_inside\_weights实际上指的就是 P i ∗ Pi^\* Pi∗，bbox\_outside\_weights指的是 N r e g N\_\{reg\} Nreg。论文中说如果anchor是前景， P i ∗ Pi^\* Pi∗就是1，为背景， P i ∗ Pi^\* Pi∗就是0。label为-1的，在这个代码来看也是设置为0，应该是在后面不会参与计算，这个设置为多少都无所谓。  
 N r e g N\_\{reg\} Nreg是进行标准化操作，就是取平均。这个平均是把所有的label 0和label 1加起来。因为选的是256个anchor做训练，所以实际上这个值是 1 256 \\frac\{1\}\{256\} 2561。

bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32) # 之前anchor过滤之后与之对应的bbox
    bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) # 计算anchor框与gt框之间的残差用于回归
    
    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)
    # 对样本权重进行归一化
    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

之后将计算的anchor映射回原来的全部的anchor中去：

# map up to original set of anchors
    # 主要是将长度为len(inds_inside)的数据映射回长度total_anchors的数据，total_anchors=(width*height)×9
    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)

值得注意的是，rpn网络的训练是256个anchor，128个positive，128个negative。但anchor\_target\_layer层的输出并不是只有256个anchor的label和坐标变换，而是所有的anchor。其中\_unmap函数就很好体现了这一点。那训练的时候怎么实现训练这256个呢？实际上，这一层的4个输出，rpn\_labels是需要输出到rpn\_loss\_cls层，其他的3个输出到rpn\_loss\_bbox，label实际上就是loss function前半部分中的 P i ∗ Pi^\* Pi∗（即计算分类的loss），这是一个log loss，为-1的label是无法进行log计算的，剩下的0、1就直接计算，这一部分实现了256。loss function后半部分是计算bbox坐标的loss， P i ∗ Pi^\* Pi∗，也就是bbox\_inside\_weights，论文中说了activated only for positive anchors，只有为正例的anchor才去计算坐标的损失，这是 P i ∗ Pi^\* Pi∗是1，其他情况都是0。所以呢，只有那256个才真正改变了loss值，其他的都是0。

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)

这段代码也体现了这个思想，所以这也实现了256。

最后就是维度转换并设置这个层的4个输出了

# 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

到这里，由特征图与anchor生成anchor分类与预测框的流程梳理完了，接下来就是根据对该层输出计算RPN部分的loss了。

**PS：** 我们注意到，该层中没有并没有实现反向传播，这是为毛啊？没有给网络提供梯度。其实是因为这个层的输入信息rpn\_cls\_score就提供了一个长宽信息就回家洗洗睡了，所以就没必要传递梯度了。

# 3. ROI Proposal网络部分 #

## 3.1 ProposalLayer ##

该层有3个输入：fg/bg anchors分类器结果rpn\_cls\_prob\_reshape，对应的bbox reg的 \[ d x ( A ) ， d y ( A ) ， d w ( A ) ， d h ( A ) \] \[dx(A)，dy(A)，dw(A)，dh(A)\] \[dx(A)，dy(A)，dw(A)，dh(A)\]变换量rpn\_bbox\_pred，以及im\_info；另外还有参数feat\_stride=16。  
缩进首先解释im\_info。对于一副任意大小图像，传入Faster RCNN前首先reshape到固定 M ∗ N M\*N M∗N， i m \_ i n f o = \[ M , N , s c a l e \_ f a c t o r \] im\\\_info=\[M, N, scale\\\_factor\] im\_info=\[M,N,scale\_factor\]则保存了此次缩放的所有信息。然后经过Conv Layers，经过4次pooling变为 ( M / 16 ) ∗ ( N / 16 ) (M/16)\*(N/16) (M/16)∗(N/16)大小，其中 f e a t u r e \_ s t r i d e = 16 feature\\\_stride=16 feature\_stride=16则保存了该信息。所有这些数值都是为了将proposal映射回原图而设置的。  
首先来看，该层的初始函数

def setup(self, bottom, top):
        # parse the layer parameter string, which must be valid YAML
        layer_params = yaml.load(self.param_str_)
    
        self._feat_stride = layer_params['feat_stride']
        anchor_scales = layer_params.get('scales', (8, 16, 32))
        self._anchors = generate_anchors(scales=np.array(anchor_scales)) # 产生默认的9个anchor
        self._num_anchors = self._anchors.shape[0]
    
        if DEBUG:
            print 'feat_stride: {}'.format(self._feat_stride)
            print 'anchors:'
            print self._anchors
    
        # rois blob: holds R regions of interest, each is a 5-tuple
        # (n, x1, y1, x2, y2) specifying an image batch index n and a
        # rectangle (x1, y1, x2, y2)
        top[0].reshape(1, 5)
    
        # scores blob: holds scores for R regions of interest
        if len(top) > 1:
            top[1].reshape(1, 1, 1, 1)

在进行前向运算之前，需要载入一些配置项：

cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' 阶段为train和test的时候nms的输入输出数目不一样
    # Number of top scoring boxes to keep before apply NMS to RPN proposals
    # 对RPN结果使用NMS之前需要保留的框
    pre_nms_topN  = cfg[cfg_key].RPN_PRE_NMS_TOP_N # 12000
    # Number of top scoring boxes to keep after applying NMS to RPN proposals
    # 对RPN结果使用NMS之后需要保留的框
    post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N # 1200
    ## NMS threshold used on RPN proposals 使用nms时候的阈值
    nms_thresh    = cfg[cfg_key].RPN_NMS_THRESH # 0.7
    # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
    min_size      = cfg[cfg_key].RPN_MIN_SIZE # 16
    
    # the first set of _num_anchors channels are bg probs
    # the second set are the fg probs, which we want
    # 前9个通道为背景类；后9个通道为非背景类
    scores = bottom[0].data[:, self._num_anchors:, :, :] # 预测的分类（卷积输出：18）
    bbox_deltas = bottom[1].data # 预测框的偏移量
    im_info = bottom[2].data[0, :] # 图像的信息

接下来就开始proposal了  
**step1：再次生成anchor，并使用bbox\_deltas得到预测框**

# 1. Generate proposals from bbox deltas and shifted anchors
    height, width = scores.shape[-2:]
    
    if DEBUG:
        print 'score map size: {}'.format(scores.shape)
    
    # Enumerate all shifts 这部分同anchor_target_layer
    shift_x = np.arange(0, width) * self._feat_stride
    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()
    
    # Enumerate all shifted anchors:
    #
    # 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]
    anchors = self._anchors.reshape((1, A, 4)) + \
                      shifts.reshape((1, K, 4)).transpose((1, 0, 2))
    anchors = anchors.reshape((K * A, 4))
    
    # Transpose and reshape predicted bbox transformations to get them
    # into the same order as the anchors:
    #
    # bbox deltas will be (1, 4 * A, H, W) format
    # transpose to (1, H, W, 4 * A)
    # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
    # in slowest to fastest order
    bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
    
    # Same story for the scores:
    #
    # scores are (1, A, H, W) format
    # transpose to (1, H, W, A)
    # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
    scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
    
    # Convert anchors into proposals via bbox transformations
    # 利用 bbox_deltas 对anchors进行修正，得到proposals的预测位置，可以参考论文中公式
    # 对于x,y使用线性变换，对于w,h使用exp
    proposals = bbox_transform_inv(anchors, bbox_deltas)

**step2：剪裁预测框使之在图像范围之内**

# 2. clip predicted boxes to image
    # 剪裁预测框到图像的边界内
    proposals = clip_boxes(proposals, im_info[:2])

**step3：去除小的预测框，阈值为16**

# 3. remove predicted boxes with either height or width < threshold
    # (NOTE: convert min_size to input image scale stored in im_info[2])
    # 去除长宽小于16的预测框，因为进行过4次Pooling呀
    keep = _filter_boxes(proposals, min_size * im_info[2])
    proposals = proposals[keep, :]
    scores = scores[keep]

**step4：对于预测框的分数进行排序，并且取前N个送去NMS**

# 4. sort all (proposal, score) pairs by score from highest to lowest
    # 5. take top pre_nms_topN (e.g. 6000) 选出Top_N，后面再进行 NMS，见前面的设置
    order = scores.ravel().argsort()[::-1]
    if pre_nms_topN > 0:
        order = order[:pre_nms_topN]
    proposals = proposals[order, :] # 保留了前pre_nms_topN个框的坐标信息
    scores = scores[order] # 保留了前pre_nms_topN个框的分数信息

**step5：进行NMS并取前N个**

# 6. apply nms (e.g. threshold = 0.7)
    # 7. take after_nms_topN (e.g. 300)
    # 8. return the top proposals (-> RoIs top) 对预测框进行nms
    keep = nms(np.hstack((proposals, scores)), nms_thresh)
    if post_nms_topN > 0:
        keep = keep[:post_nms_topN]
    proposals = proposals[keep, :] # 对nms之后的预测框取前after_nms_topN个
    scores = scores[keep]

**step6：输出结果**

# Output rois blob
    # Our RPN implementation only supports a single input image, so all
    # batch inds are 0
    batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
    blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
    top[0].reshape(*(blob.shape))
    top[0].data[...] = blob
    
    # [Optional] output scores blob
    if len(top) > 1:
        top[1].reshape(*(scores.shape))
        top[1].data[...] = scores

## 3.2 ProposalTargetLayer ##

这个层主要完成由RPN得到的预测框到对应分类的匹配，其中对每次训练的预测框进行了限制（每次只处理32个目标预测框，总数的1/4），详见\_sample\_rois函数。首先，得到分类的数目，并初始化输出blob的shape

def setup(self, bottom, top):
        layer_params = yaml.load(self.param_str_)
        self._num_classes = layer_params['num_classes']
    
        # sampled rois (0, x1, y1, x2, y2)
        top[0].reshape(1, 5)
        # labels
        top[1].reshape(1, 1)
        # bbox_targets
        top[2].reshape(1, self._num_classes * 4)
        # bbox_inside_weights
        top[3].reshape(1, self._num_classes * 4)
        # bbox_outside_weights
        top[4].reshape(1, self._num_classes * 4)

前向传播函数

def forward(self, bottom, top):
        # Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
        # (i.e., rpn.proposal_layer.ProposalLayer), or any other source
        all_rois = bottom[0].data # RPN预测框，维度为[N,5]
        # GT boxes (x1, y1, x2, y2, label)
        # TODO(rbg): it's annoying that sometimes I have extra info before
        # and other times after box coordinates -- normalize to one format
        gt_boxes = bottom[1].data # GT信息，维度[M,5]
    
        # Include ground-truth boxes in the set of candidate rois
        # 将ground truth框加入到待分类的框里面(相当于增加正样本个数)
        # all_rois输出维度[N+M,5]，前一维表示是从RPN的输出选出的框和ground truth框合在一起了
        zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
        all_rois = np.vstack(
            (all_rois, np.hstack((zeros, gt_boxes[:, :-1])))
        ) # 先在每个ground truth框前面插入0(这样才能和N个从RPN的输出选出的框对齐)，然后把ground truth框插在最后
    
        # Sanity check: single batch only
        assert np.all(all_rois[:, 0] == 0), \
            'Only single item batches are supported'
    
        num_images = 1
        rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images #cfg.TRAIN.BATCH_SIZE为128
        # cfg.TRAIN.FG_FRACTION为0.25，即在一次分类训练中前景框只能有32个
        fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
    
        # Sample rois with classification labels and bounding box regression
        # targets
        # _sample_rois选择进行分类训练的框，并求取他们类别和坐标的ground truth和计算边框损失loss时需要的bbox_inside_weights
        labels, rois, bbox_targets, bbox_inside_weights = _sample_rois(
            all_rois, gt_boxes, fg_rois_per_image,
            rois_per_image, self._num_classes)
    
        if DEBUG:
            print 'num fg: {}'.format((labels > 0).sum())
            print 'num bg: {}'.format((labels == 0).sum())
            self._count += 1
            self._fg_num += (labels > 0).sum()
            self._bg_num += (labels == 0).sum()
            print 'num fg avg: {}'.format(self._fg_num / self._count)
            print 'num bg avg: {}'.format(self._bg_num / self._count)
            print 'ratio: {:.3f}'.format(float(self._fg_num) / float(self._bg_num))
    
        # sampled rois 采样之后最终保留的全部预测框
        top[0].reshape(*rois.shape)
        top[0].data[...] = rois
    
        # classification labels 预测框的分类
        top[1].reshape(*labels.shape)
        top[1].data[...] = labels
    
        # bbox_targets 预测框与GT的残差
        top[2].reshape(*bbox_targets.shape)
        top[2].data[...] = bbox_targets
    
        # bbox_inside_weights
        top[3].reshape(*bbox_inside_weights.shape)
        top[3].data[...] = bbox_inside_weights
    
        # bbox_outside_weights
        top[4].reshape(*bbox_inside_weights.shape)
        top[4].data[...] = np.array(bbox_inside_weights > 0).astype(np.float32)

对预测框进行采样并计算残差，在GT上找到其对应的分类

def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
        """Generate a random sample of RoIs comprising foreground and background examples. """
        # overlaps: (rois x gt_boxes)
        # 计算所有roi和ground truth框之间的重合度
        # 只取坐标信息，roi中取第二到第五个数（因为补0了呀），ground truth框中取第一到第四个数
        overlaps = bbox_overlaps(
            np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
            np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
        gt_assignment = overlaps.argmax(axis=1) # 对于每个roi，找到对应的gt_box坐标 shape: [len(all_rois),]
        max_overlaps = overlaps.max(axis=1) # 对于每个roi，找到与gt_box重合的最大的overlap shape: [len(all_rois),]
        labels = gt_boxes[gt_assignment, 4] #对于每个roi，找到归属的类别: [len(all_rois),]
    
        # Select foreground RoIs as those with >= FG_THRESH overlap
        # 大于阈值的实际前景的数量
        fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
        # Guard against the case when an image has fewer than fg_rois_per_image
        # foreground RoIs 求取用于回归的前景框数量
        fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size)
        # Sample foreground regions without replacement
        # 如果需要的话，就随机地排除一些前景框
        if fg_inds.size > 0:
            fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image, replace=False)
    
        # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
        # 找到属于背景的rois(就是与gt_box覆盖介于0和0.5之间的)
        bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
                           (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
        # Compute number of background RoIs to take from this image (guarding
        # against there being fewer than desired)
        bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image # 128-32个
        bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size) # 以下操作同fg
        # Sample background regions without replacement
        if bg_inds.size > 0:
            bg_inds = npr.choice(bg_inds, size=bg_rois_per_this_image, replace=False)
    
        # The indices that we're selecting (both fg and bg)
        keep_inds = np.append(fg_inds, bg_inds) # 记录一下运算之后最终保留的框
        # Select sampled values from various arrays:
        labels = labels[keep_inds]  # 记录一下最终保留的框对应的label
        # Clamp labels for the background RoIs to 0
        labels[fg_rois_per_this_image:] = 0 # 把背景框的分类置0
        rois = all_rois[keep_inds] # 取出最终保留的rois
    
        # 得到最终保留的框的类别ground truth值和坐标变换ground truth值，得到预测框的误差
        bbox_target_data = _compute_targets(
            rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
    
        # 得到最终计算loss时使用的ground truth边框回归值和bbox_inside_weights
        bbox_targets, bbox_inside_weights = \
            _get_bbox_regression_labels(bbox_target_data, num_classes)
    
        return labels, rois, bbox_targets, bbox_inside_weights

计算预测框残差：

def _compute_targets(ex_rois, gt_rois, labels):
        """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] == 4
    
        targets = bbox_transform(ex_rois, gt_rois) # 获得预测框与gt的残差
        if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: # 是否需要进行归一化
            # Optionally normalize targets by a precomputed mean and stdev
            targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
                    / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
        # 将残差插到lable的后面（水平插入）
        return np.hstack(
                (labels[:, np.newaxis], targets)).astype(np.float32, copy=False)

整理数据到需要的格式：

def _get_bbox_regression_labels(bbox_target_data, num_classes):
        """Bounding-box regression targets (bbox_target_data) are stored in a compact form N x (class, tx, ty, tw, th) This function expands those targets into the 4-of-4*K representation used by the network (i.e. only one class has non-zero targets). Returns: bbox_target (ndarray): N x 4K blob of regression targets bbox_inside_weights (ndarray): N x 4K blob of loss weights """
    
        clss = bbox_target_data[:, 0]  # 每个预测框通过重叠面积与gt比较得到的分类
        # 对应分类上预测框的误差
        bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
        # 用全0初始化一下bbox_inside_weights
        bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
        inds = np.where(clss > 0)[0] # 非背景类
        for ind in inds:
            cls = clss[ind]
            start = 4 * cls # 找到从属的类别对应的坐标回归值的起始位置
            end = start + 4  # 找到从属的类别对应的坐标回归值的结束位置
            bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]  #在对应类的坐标回归上置相应的值（预测框误差）
            # 将bbox_inside_weights上的对应类的坐标回归值置1
            bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS # (1.0, 1.0, 1.0, 1.0)
        return bbox_targets, bbox_inside_weights

# 4. ROI Pooling #

这部分参考：[  
关于ROI Pooling Layer的解读][_ROI Pooling Layer]

# 5. REF #

1.  [ anchor\_target\_layer层其他部分解读][anchor_target_layer]
2.  [ 详细的Faster R-CNN源码解析之proposal\_layer和proposal\_target\_layer源码解析][_Faster R-CNN_proposal_layer_proposal_target_layer]
3.  [ Faster RCNN原理分析（二）：Region Proposal Networks详解][Faster RCNN_Region Proposal Networks]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21fYnVkZHk_size_16_color_FFFFFF_t_70]: /images/20220412/6f2f00f9eac5483fbf7e7fb444f4e97d.png
[generate.py]: http://generate.py
[20181201213854403.png]: /images/20220412/44e420bf795c4b30826dbe092ffac1ef.png
[_ROI Pooling Layer]: https://blog.csdn.net/m_buddy/article/details/83445995
[anchor_target_layer]: https://www.cnblogs.com/ymjyqsx/p/7601841.html
[_Faster R-CNN_proposal_layer_proposal_target_layer]: https://blog.csdn.net/jiongnima/article/details/80478597
[Faster RCNN_Region Proposal Networks]: http://lib.csdn.net/article/deeplearning/61641