深度学习: Faster R-CNN 网络

JNingWei

发布于 2018-09-27 15:57:33

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发布于 2018-09-27 15:57:33

文章被收录于专栏：JNing的专栏JNing的专栏

Structure

看不清的可以右键，在新tab中查看该图片：

其中，“方框”代表“操作”，“菱框”代表“输出结果”。

以下是我画的Faster R-CNN结构图：

前部

Faster R-CNN 头部负责对输入图像进行 特征提取 ：

网络结构有两种，一种是将ZFNet（扔掉了尾端的全连接层）拿来用，另一种则是将VGG拿来用（扔掉了尾端的全连接层）。论文中给出的是第一种（绿框内为拿来用的那部分）：

中部

Faster R-CNN 中部负责对 特征图 (即前部所提取到的特征) 进行 生成anchor并从中筛选出proposal ：

双分支：

绿框内：【RPN（生成anchor –> 洗涤anchor成proposal）】。
红框内：【特征图的无损传递】。

最后统一交付给 黄框内：【RoIPooling】去输出相同size的RoI。

后部

交给 分类任务 和 边框回归任务 来进一步提升location精度和class精度，并输出检测结果：

Loss Computation

多任务： Faster R-CNN论文笔记——FR

Fast R-CNN网络有两个同级输出层（cls score和bbox_prdict层），都是全连接层，称为multi-task。 ① clsscore层：用于分类，输出k+1维数组p，表示属于k类和背景的概率。对每个RoI（Region of Interesting）输出离散型概率分布

通常，p由k+1类的全连接层利用softmax计算得出。

② bbox_prdict层：用于调整候选区域位置，输出bounding box回归的位移，输出4*K维数组t，表示分别属于k类时，应该平移缩放的参数。

k表示类别的索引，

是指相对于objectproposal尺度不变的平移，

是指对数空间中相对于objectproposal的高与宽。 loss_cls层评估分类损失函数。由真实分类u对应的概率决定：

loss_bbox评估检测框定位的损失函数。比较真实分类对应的预测平移缩放参数

和真实平移缩放参数为

的差别：

其中，smooth L1损失函数为：

smooth L1损失函数曲线如下图9所示，作者这样设置的目的是想让loss对于离群点更加鲁棒，相比于L2损失函数，其对离群点、异常值（outlier）不敏感，可控制梯度的量级使训练时不容易跑飞。

最后总损失为（两者加权和，如果分类为背景则不考虑定位损失）：

规定u=0为背景类（也就是负标签），那么艾弗森括号指数函数[u≥1]表示背景候选区域即负样本不参与回归损失，不需要对候选区域进行回归操作。λ控制分类损失和回归损失的平衡。Fast R-CNN论文中，所有实验λ=1。艾弗森括号指数函数为：

源码中bbox_loss_weights用于标记每一个bbox是否属于某一个类。

Code

附上作者的源码 rbgirshick/py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_end2end/train.prototxt ：

name: "ZF"
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"
  }
}

#========= conv1-conv5 ============

layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
        num_output: 96
        kernel_size: 7
        pad: 3
        stride: 2
    }
}
layer {
    name: "relu1"
    type: "ReLU"
    bottom: "conv1"
    top: "conv1"
}
layer {
    name: "norm1"
    type: "LRN"
    bottom: "conv1"
    top: "norm1"
    lrn_param {
        local_size: 3
        alpha: 0.00005
        beta: 0.75
        norm_region: WITHIN_CHANNEL
    engine: CAFFE
    }
}
layer {
    name: "pool1"
    type: "Pooling"
    bottom: "norm1"
    top: "pool1"
    pooling_param {
        kernel_size: 3
        stride: 2
        pad: 1
        pool: MAX
    }
}
layer {
    name: "conv2"
    type: "Convolution"
    bottom: "pool1"
    top: "conv2"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
        num_output: 256
        kernel_size: 5
        pad: 2
        stride: 2
    }
}
layer {
    name: "relu2"
    type: "ReLU"
    bottom: "conv2"
    top: "conv2"
}
layer {
    name: "norm2"
    type: "LRN"
    bottom: "conv2"
    top: "norm2"
    lrn_param {
        local_size: 3
        alpha: 0.00005
        beta: 0.75
        norm_region: WITHIN_CHANNEL
    engine: CAFFE
    }
}
layer {
    name: "pool2"
    type: "Pooling"
    bottom: "norm2"
    top: "pool2"
    pooling_param {
        kernel_size: 3
        stride: 2
        pad: 1
        pool: MAX
    }
}
layer {
    name: "conv3"
    type: "Convolution"
    bottom: "pool2"
    top: "conv3"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
        num_output: 384
        kernel_size: 3
        pad: 1
        stride: 1
    }
}
layer {
    name: "relu3"
    type: "ReLU"
    bottom: "conv3"
    top: "conv3"
}
layer {
    name: "conv4"
    type: "Convolution"
    bottom: "conv3"
    top: "conv4"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
        num_output: 384
        kernel_size: 3
        pad: 1
        stride: 1
    }
}
layer {
    name: "relu4"
    type: "ReLU"
    bottom: "conv4"
    top: "conv4"
}
layer {
    name: "conv5"
    type: "Convolution"
    bottom: "conv4"
    top: "conv5"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
        num_output: 256
        kernel_size: 3
        pad: 1
        stride: 1
    }
}
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_conv/3x3"
#  type: "Convolution"
#  bottom: "conv5"
#  top: "rpn_conv/3x3"
#  param { lr_mult: 1.0 }
#  param { lr_mult: 2.0 }
#  convolution_param {
#    num_output: 192
#    kernel_size: 3 pad: 1 stride: 1
#    weight_filler { type: "gaussian" std: 0.01 }
#    bias_filler { type: "constant" value: 0 }
#  }
#}
#layer {
#  name: "rpn_conv/5x5"
#  type: "Convolution"
#  bottom: "conv5"
#  top: "rpn_conv/5x5"
#  param { lr_mult: 1.0 }
#  param { lr_mult: 2.0 }
#  convolution_param {
#    num_output: 64
#    kernel_size: 5 pad: 2 stride: 1
#    weight_filler { type: "gaussian" std: 0.0036 }
#    bias_filler { type: "constant" value: 0 }
#  }
#}
#layer {
#  name: "rpn/output"
#  type: "Concat"
#  bottom: "rpn_conv/3x3"
#  bottom: "rpn_conv/5x5"
#  top: "rpn/output"
#}
#layer {
#  name: "rpn_relu/output"
#  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 }
}

#========= RoI Proposal ============

layer {
  name: "rpn_cls_prob"
  type: "Softmax"
  bottom: "rpn_cls_score_reshape"
  top: "rpn_cls_prob"
}
layer {
  name: 'rpn_cls_prob_reshape'
  type: 'Reshape'
  bottom: 'rpn_cls_prob'
  top: 'rpn_cls_prob_reshape'
  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
}
layer {
  name: 'proposal'
  type: 'Python'
  bottom: 'rpn_cls_prob_reshape'
  bottom: 'rpn_bbox_pred'
  bottom: 'im_info'
  top: 'rpn_rois'
#  top: 'rpn_scores'
  python_param {
    module: 'rpn.proposal_layer'
    layer: 'ProposalLayer'
    param_str: "'feat_stride': 16"
  }
}
#layer {
#  name: 'debug-data'
#  type: 'Python'
#  bottom: 'data'
#  bottom: 'rpn_rois'
#  bottom: 'rpn_scores'
#  python_param {
#    module: 'rpn.debug_layer'
#    layer: 'RPNDebugLayer'
#  }
#}
layer {
  name: 'roi-data'
  type: 'Python'
  bottom: 'rpn_rois'
  bottom: 'gt_boxes'
  top: 'rois'
  top: 'labels'
  top: 'bbox_targets'
  top: 'bbox_inside_weights'
  top: 'bbox_outside_weights'
  python_param {
    module: 'rpn.proposal_target_layer'
    layer: 'ProposalTargetLayer'
    param_str: "'num_classes': 21"
  }
}

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

layer {
  name: "roi_pool_conv5"
  type: "ROIPooling"
  bottom: "conv5"
  bottom: "rois"
  top: "roi_pool_conv5"
  roi_pooling_param {
    pooled_w: 6
    pooled_h: 6
    spatial_scale: 0.0625 # 1/16
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "roi_pool_conv5"
  top: "fc6"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
    scale_train: false
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
    scale_train: false
  }
}
layer {
  name: "cls_score"
  type: "InnerProduct"
  bottom: "fc7"
  top: "cls_score"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 21
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "bbox_pred"
  type: "InnerProduct"
  bottom: "fc7"
  top: "bbox_pred"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 84
    weight_filler {
      type: "gaussian"
      std: 0.001
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "cls_score"
  bottom: "labels"
  propagate_down: 1
  propagate_down: 0
  top: "cls_loss"
  loss_weight: 1
  loss_param {
    ignore_label: -1
    normalize: true
  }
}
layer {
  name: "loss_bbox"
  type: "SmoothL1Loss"
  bottom: "bbox_pred"
  bottom: "bbox_targets"
  bottom: 'bbox_inside_weights'
  bottom: 'bbox_outside_weights'
  top: "bbox_loss"
  loss_weight: 1
}

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原始发表：2017年12月21日，如有侵权请联系 cloudcommunity@tencent.com 删除

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