上篇我們講解了rpn與fast rcnn的資料準備階段，接下來我們講解rpn的整個訓練過程。最後 講解rpn訓練完畢後rpn的生成。

我們順著stage1_rpn_train.pt的內容講解。

name: "VGG_CNN_M_1024"
layer {
  name: 'input-data'
  type: 'Python'
  top: 'data'
  top: 'im_info'
  top: 'gt_boxes'
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'RoIDataLayer'
    param_str: "'num_classes': 21"
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param { lr_mult: 0 decay_mult: 0 }
  param { lr_mult: 0 decay_mult: 0 }
  convolution_param {
    num_output: 96
    kernel_size: 7 stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3 stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param { lr_mult: 1 }
  param { lr_mult: 2 }
  convolution_param {
    num_output: 256
    pad: 1 kernel_size: 5 stride: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3 stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param { lr_mult: 1 }
  param { lr_mult: 2 }
  convolution_param {
    num_output: 512
    pad: 1 kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param { lr_mult: 1 }
  param { lr_mult: 2 }
  convolution_param {
    num_output: 512
    pad: 1 kernel_size: 3
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param { lr_mult: 1 }
  param { lr_mult: 2 }
  convolution_param {
    num_output: 512
    pad: 1 kernel_size: 3
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}

#========= RPN ============

layer {
  name: "rpn_conv/3x3"
  type: "Convolution"
  bottom: "conv5"
  top: "rpn/output"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 256
    kernel_size: 3 pad: 1 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
  name: "rpn_relu/3x3"
  type: "ReLU"
  bottom: "rpn/output"
  top: "rpn/output"
}
layer {
  name: "rpn_cls_score"
  type: "Convolution"
  bottom: "rpn/output"
  top: "rpn_cls_score"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 18   # 2(bg/fg) * 9(anchors)
    kernel_size: 1 pad: 0 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
  name: "rpn_bbox_pred"
  type: "Convolution"
  bottom: "rpn/output"
  top: "rpn_bbox_pred"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 36   # 4 * 9(anchors)
    kernel_size: 1 pad: 0 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
   bottom: "rpn_cls_score"
   top: "rpn_cls_score_reshape"
   name: "rpn_cls_score_reshape"
   type: "Reshape"
   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
  name: 'rpn-data'
  type: 'Python'
  bottom: 'rpn_cls_score'
  bottom: 'gt_boxes'
  bottom: 'im_info'
  bottom: 'data'
  top: 'rpn_labels'
  top: 'rpn_bbox_targets'
  top: 'rpn_bbox_inside_weights'
  top: 'rpn_bbox_outside_weights'
  python_param {
    module: 'rpn.anchor_target_layer'
    layer: 'AnchorTargetLayer'
    param_str: "'feat_stride': 16"
  }
}
layer {
  name: "rpn_loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "rpn_cls_score_reshape"
  bottom: "rpn_labels"
  propagate_down: 1
  propagate_down: 0
  top: "rpn_cls_loss"
  loss_weight: 1
  loss_param {
    ignore_label: -1
    normalize: true
  }
}
layer {
  name: "rpn_loss_bbox"
  type: "SmoothL1Loss"
  bottom: "rpn_bbox_pred"
  bottom: "rpn_bbox_targets"
  bottom: 'rpn_bbox_inside_weights'
  bottom: 'rpn_bbox_outside_weights'
  top: "rpn_loss_bbox"
  loss_weight: 1
  smooth_l1_loss_param { sigma: 3.0 }
}

#========= RCNN ============

layer {
  name: "dummy_roi_pool_conv5"
  type: "DummyData"
  top: "dummy_roi_pool_conv5"
  dummy_data_param {
    shape { dim: 1 dim: 18432 }
    data_filler { type: "gaussian" std: 0.01 }
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "dummy_roi_pool_conv5"
  top: "fc6"
  param { lr_mult: 0 decay_mult: 0 }
  param { lr_mult: 0 decay_mult: 0 }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param { lr_mult: 0 decay_mult: 0 }
  param { lr_mult: 0 decay_mult: 0 }
  inner_product_param {
    num_output: 1024
  }
}
layer {
  name: "silence_fc7"
  type: "Silence"
  bottom: "fc7"
}
 

它的示意圖如下： 這裡借用了http://blog.csdn.net/zy1034092330/article/details/62044941裡的圖。

上面Conv layers包含了五層卷積層。 接下來，對於第五層卷積層，進行了3*3的卷積操作，輸出了256個通道，當然大小與卷積前的大小相同。

然後開始分別接入了cls層與regression層。對於cls層，使用1*1的卷積操作輸出了18（9*2 bg/fg）個通道的feature map,大小不變。而對於regression層，也使用1*1的卷積層輸出了36(4*9)個通道的feature map，大小不變。 

對於cls層後又接了一個reshape層，為什麼要接這個層呢？引用參考文獻

[1]的話，其實只是為了便於softmax分類，至於具體原因這就要從caffe的實現形式說起了。在caffe基本資料結構blob中以如下形式儲存資料：
blob=[batch_size, channel，height，width]
對應至上面的儲存bg/fg anchors的矩陣，其在caffe blob中的儲存形式為[1, 2*9, H, W]。而在softmax分類時需要進行fg/bg二分類，所以reshape layer會將其變為[1, 2, 9*H, W]大小，即單獨“騰空”出來一個維度以便softmax分類，之後再reshape回覆原狀。

我們可以用python模擬一下，看如下的程式：

>>> a=np.array([[[1,2],[3,4]],[[5,6],[7,8]],[[9,10],[11,12]],[[13,14],[15,16]]])
 

>>> a
array([[[ 1,  2],
        [ 3,  4]],

       [[ 5,  6],
        [ 7,  8]],

       [[ 9, 10],
        [11, 12]],

       [[13, 14],
        [15, 16]]])
>>> a.shape
(4L, 2L, 2L)

然後由於caffe中是行優先，numpy也如此，那麼reshape一下的結果如下：

>>> b=a.reshape(2,4,2)
>>> b
array([[[ 1,  2],
        [ 3,  4],
        [ 5,  6],
        [ 7,  8]],

       [[ 9, 10],
        [11, 12],
        [13, 14],
        [15, 16]]])

從上面可以看出reshape是把相鄰通道的矩陣移到它的下面了。這樣就剩下兩個大的矩陣了，就可以相鄰通道之間進行softmax了。從中其實我們也能發現，對於rpn每個點的18個輸出通道，前9個為背景的預測分數，而後9個為前景的預測分數。

假定softmax昨晚後，我們看看是否能夠回到原先？

>>> b.reshape(4,2,2)
array([[[ 1,  2],
        [ 3,  4]],

       [[ 5,  6],
        [ 7,  8]],

       [[ 9, 10],
        [11, 12]],

       [[13, 14],
        [15, 16]]])

果然又回到了原始的狀態。

而對於regression呢，不需要這樣的操作，那麼他的36個通道是不是也是如上面18個通道那樣呢？即第一個9通道為dx,第二個為dy,第三個為dw，第五個是dh。還是我們比較容易想到的那種,即第一個通道是第一個盒子的迴歸量(dx1,dy1,dw1,dh1),第二個為(dx2,dy2,dw,2,dh2).....。待後面檢視對應的bbox_targets就知道了。先留個坑。

正如圖上所示，我們還需要準備一個層rpn-data。

layer {
  name: 'rpn-data'
  type: 'Python'
  bottom: 'rpn_cls_score'
  bottom: 'gt_boxes'
  bottom: 'im_info'
  bottom: 'data'
  top: 'rpn_labels'
  top: 'rpn_bbox_targets'
  top: 'rpn_bbox_inside_weights'
  top: 'rpn_bbox_outside_weights'
  python_param {
    module: 'rpn.anchor_target_layer'
    layer: 'AnchorTargetLayer'
    param_str: "'feat_stride': 16"
  }
}

這一層輸入四個量：data,gt_boxes,im_info,rpn_cls_score,其中前三個是我們在前面說過的，

data:         1*3*600*1000
gt_boxes: N*5,                   N為groundtruth box的個數，每一行為(x1, y1, x2, y2, cls) ，而且這裡的gt_box是經過縮放的。
im_info： 1*3                   （h,w,scale）

rpn_cls_score是cls層輸出的18通道，shape可以看成是1*18*H*W.  

輸出為4個量：rpn_labels 、rpn_bbox_targets（迴歸目標）、rpn_bbox_inside_weights（內權重）、rpn_bbox_outside_weights（外權重）。

通俗地來講，這一層產生了具體的anchor座標，並與groundtruth box進行了重疊度計算，輸出了kabel與迴歸目標。

接下來我們來看一下檔案anchor_target_layer.py 

 def setup(self, bottom, top):
        layer_params = yaml.load(self.param_str_)    
		#在第5個卷積層後的feature map上的每個點取anchor,尺度為（8,16,32），結合後面的feat_stride為16，
		#再縮放回原來的影象大小，正好尺度是(128,256,512),與paper一樣。
        anchor_scales = layer_params.get('scales', (8, 16, 32))  
        self._anchors = generate_anchors(scales=np.array(anchor_scales))  #產生feature map最左上角的那個點對應的anchor（x1,y1,x2,y2），
		# 尺度為原始影象的尺度（可以看成是Im_info的寬和高尺度，或者是600*1000）。
        self._num_anchors = self._anchors.shape[0]   #9
        self._feat_stride = layer_params['feat_stride'] #16

        if DEBUG:
            print 'anchors:'
            print self._anchors
            print 'anchor shapes:'
            print np.hstack((  # 輸出寬和高
                self._anchors[:, 2::4] - self._anchors[:, 0::4], #第2列減去第0列
                self._anchors[:, 3::4] - self._anchors[:, 1::4], #第3列減去第1列
            ))
            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:]   #cls後的feature map的大小
        if DEBUG:
            print 'AnchorTargetLayer: height', height, 'width', width

        A = self._num_anchors   
        # labels
        top[0].reshape(1, 1, A * height, width)     # 顯然與rpn_cls_score_reshape保持相同的shape.
        # 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)

setup設定了top輸出的shape,並且做了一些準備工作。

接下來看forward函式。

 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'     # 僅僅支援一張圖片

        # map of shape (..., H, W)
        height, width = bottom[0].data.shape[-2:]        
        # 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  
        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)))
        all_anchors = all_anchors.reshape((K * A, 4))
        total_anchors = int(K * A)                       # 根據左上角的anchor生成所有的anchor，這裡將所有的anchor按照行排列。行：K*A(K= height*width ,A=9),列：4，且按照feature map按行優先這樣排下來。

        # only keep anchors inside the image   #取所有在影象內部的anchor
        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

        # 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))
        argmax_overlaps = overlaps.argmax(axis=1)   #對於每一個anchor，取其重疊度最大的ground truth的序號
        max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]   #生成max_overlaps,(為一列)即每個anchor對應的最大重疊度
        gt_argmax_overlaps = overlaps.argmax(axis=0)          #對於每個類，選擇其對應的最大重疊度的anchor序號
        gt_max_overlaps = overlaps[gt_argmax_overlaps,       
                                   np.arange(overlaps.shape[1])]  #生成gt_max_overlaps，（為一行）即每類對應的最大重疊度
        gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]  #找到那些等於gt_max_overlaps的anchor,這些anchor將參與訓練rpn
        # 找到所有overlaps中所有等於gt_max_overlaps的元素，因為gt_max_overlaps對於每個非負類別只保留一個
        # anchor，如果同一列有多個相等的最大IOU overlap值，那麼就需要把其他的幾個值找到，並在後面將它們
        # 的label設為1，即認為它們是object，畢竟在RPN的cls任務中，只要認為它是否是個object即可，即一個
        # 二分類問題。   (總結)
		
		
		
		# 如下設定了前景(1)、背景(0)以及不關心(-1)的anchor標籤
        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    #對於最大重疊度低於0.3的設為背景

        # 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
			
			
		# 取前景與背景的anchor各一半，目前一批有256個anchor.
        # subsample positive labels if we have too many
        num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)   #256*0.5=128
        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)  #另一半256*0.5=128
        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))
        
		#計算了所有在內部的anchor與對應的ground truth的迴歸量
        bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
        bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
         
		 #只有前景類內部權重才非0，參與迴歸
        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) #(1.0, 1.0, 1.0, 1.0)

		# Give the positive RPN examples weight of p * 1 / {num positives}
		# and give negatives a weight of (1 - p)/(num negative)    
		# Set to -1.0 to use uniform example weighting
        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  # 前景與背景anchor的外引數相同，都是1/anchor個數
        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 生成全部anchor的資料，將非0的資料填入。
        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

這裡已經有詳細的註釋，總的來說，rpn_cls_score的作用就是告知第五層feature map的寬和高。便於決定生成多少個anchor. 而其他的bottom輸入才最終決定top的輸出。

首先這裡生成了所有feature map各點對應的anchors。生成的方式很特別，先考慮了左上角一個點的anchor生成，考慮到feat_stride=16，所以這個點對應原始影象（這裡統一指縮放後image）的(0,0,15,15)感受野。然後取其中心點，生成比例為1:1,1:2,2:1，尺度在128,256,512的9個anchor.然後考慮使用平移生成其他的anchor.

然後過濾掉那些不在影象內部的anchor. 對於剩下的anchor,計算與gt_boxes的重疊度，再分別計算label,bbox_targets,bbox_inside_weights,bbox_outside_weights.

最後將內部的anchor的相關變數擴充到所有的anchor,只不過不在內部的為0即可。尤其值得說的是對於內部的anchor,bbox_targets都進行了運算。但是選取了256個anchor,前景與背景比例為1:1，bbox_inside_weights中只有label=1,即前景才進行了設定。正如論文所說，對於迴歸項，需要內部引數來約束，bbox_inside_weights正好起到了這個作用。

我們統計一下top的shape:

rpn_labels ： (1, 1, 9 * height, width)

rpn_bbox_targets（迴歸目標）： (1, 36，height, width)

rpn_bbox_inside_weights（內權重）：(1, 36，height, width)

rpn_bbox_outside_weights（外權重）：(1, 36，height, width)

回到stage1_rpn_train.pt，接下里我們就可以利用rpn_cls_score_reshape與rpn_labels計算SoftmaxWithLoss，輸出rpn_cls_loss。

而regression可以利用rpn_bbox_pred，rpn_bbox_targets，rpn_bbox_inside_weights，rpn_bbox_outside_weights計算SmoothL1Loss，輸出rpn_loss_bbox。

回到我們之前有一個問題rpn_bbox_pred的shape怎麼構造的。其實從rpn_bbox_targets的生成過程中可以推斷出應該採用後一種，即第一個盒子的迴歸量(dx1,dy1,dw1,dh1),第二個為(dx2,dy2,dw,2,dh2).....，這樣順序著來。

其實怎麼樣認為都是從我們方便的角度出發。

至此我們完成了rpn的前向過程，反向過程中只需注意AnchorTargetLayer不參與反向傳播。因為它提供的都是源資料。

參考：

2. Faster RCNN anchor_target_layer.py