SqueezeNet运用到Faster RCNN进行目标检测

目录

• • 目录
  • 一SqueezeNet介绍
    • MOTIVATION
    • FIRE MODULE
    • ARCHITECTURE
    • EVALUATION
  • 二SqueezeNet与Faster RCNN结合
  • 三SqueezeNetFaster RCNNOHEM
  • 原文链接

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一、SqueezeNet介绍

论文提交ICLR 2017 
论文地址：https://arxiv.org/abs/1602.07360 
代码地址：https://github.com/DeepScale/SqueezeNet 
注：代码只放出了prototxt文件和训练好的caffemodel，因为整个网络都是基于caffe的，有这两样东西就足够了。 
在这里只是简要的介绍文章的内容，具体细节的东西可以自行翻阅论文。

MOTIVATION

在相同的精度下，模型参数更少有3个好处：

• More efficient distributed training
• Less overhead when exporting new models to clients
• Feasible FPGA and embedded deployment

即 高效的分布式训练、更容易替换模型、更方便FPGA和嵌入式部署。 
鉴于此，提出3种策略：

• Replace 3x3 filters with 1x1 filters.
• Decrease the number of input channels to 3x3 filters.
• Downsample late in the network so that convolution layers have large activation maps.

即

1. 使用1x1的核替换3x3的核，因为1x1核参数是3x3的1/9；
2. 输入通道减少3x3核的数量，因为参数的数量由输入通道数、卷积核数、卷积核的大小决定。因此，减少1x1的核数量还不够，还需要减少输入通道数量，在文中，作者使用squeeze layer来达到这一目的；
3. 后移池化层，得到更大的feature map。作者认为在网络的前段使用大的步长进行池化，后面的feature map将会减小，而大的feature map会有较高的准确率。

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FIRE MODULE

由上面的思路，作者提出了Fire Module，结构如下： 

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ARCHITECTURE

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关于SqueezeNet的构建细节在文中也有详细的描述

• 为了3x3的核输出的feature map和1x1的大小相同，padding取1（主要是为了concat）
• squeezelayer和expandlayer后面跟ReLU激活函数
• Dropout比例为0.5，跟在fire9后面
• 取消全连接，参考NIN结构
• 训练过程采用多项式学习率（我用来做检测时改为了step策略）
• 由于caffe不支持同一个卷积层既有1x1，又有3x3，所以需要concat，将两个分辨率的图在channel维度concat。这在数学上是等价的

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EVALUATION

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二、SqueezeNet与Faster RCNN结合

这里，我首先尝试的是使用alt-opt，但是很遗憾的是，出来的结果很糟糕，基本不能用，后来改为使用end2end，在最开始的时候，采用的就是faster rcnn官方提供的zfnet end2end训练的solvers，又很不幸的是，在网络运行大概400步后出现：

loss = NAN
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1

遇到这个问题，把学习率改为以前的1/10，解决。 
直接上prototxt文件，前面都是一样的，只需要改动zfnet中的conv1-con5部分，外加把fc6-fc7改成squeeze中的卷积链接。 
prototxt太长，给出每个部分的前面和后面部分：

name: "Alex_Squeeze_v1.1"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': 4"  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 64    kernel_size: 3    stride: 2  }}...layer {  name: "drop9"  type: "Dropout"  bottom: "fire9/concat"  top: "fire9/concat"  dropout_param {    dropout_ratio: 0.5  }}#========= RPN ============layer {  name: "rpn_conv/3x3"  type: "Convolution"  bottom: "fire9/concat"  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: "drop9"  type: "Dropout"  bottom: "fire9/concat"  top: "fire9/concat"  dropout_param {    dropout_ratio: 0.5  }}#========= RPN ============layer {  name: "rpn_conv/3x3"  type: "Convolution"  bottom: "fire9/concat"  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: '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': 4"  }}#===================== RCNN =============layer {  name: "roi_pool5"  type: "ROIPooling"  bottom: "fire9/concat"  bottom: "rois"  top: "roi_pool5"  roi_pooling_param {    pooled_w: 7    pooled_h: 7    spatial_scale: 0.0625 # 1/16  }}layer {  name: "conv1_last"  type: "Convolution"  bottom: "roi_pool5"  top: "conv1_last"  param { lr_mult: 1.0 }  param { lr_mult: 1.0 }  convolution_param {    num_output: 1000    kernel_size: 1    weight_filler {      type: "gaussian"      mean: 0.0      std: 0.01    }  }}layer {  name: "relu/conv1_last"  type: "ReLU"  bottom: "conv1_last"  top: "relu/conv1_last"}layer {  name: "cls_score"  type: "InnerProduct"  bottom: "relu/conv1_last"  top: "cls_score"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 5    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "bbox_pred"  type: "InnerProduct"  bottom: "relu/conv1_last"  top: "bbox_pred"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 20    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: "loss_cls"  loss_weight: 1}layer {  name: "loss_bbox"  type: "SmoothL1Loss"  bottom: "bbox_pred"  bottom: "bbox_targets"  bottom: "bbox_inside_weights"  bottom: "bbox_outside_weights"  top: "loss_bbox"  loss_weight: 1}
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后面一部分的结构如图： 
 
注意红圈部分，以前的fc换成了squ中的卷积层，这样网络参数大大减少，因为我改动了rpn部分选proposal的比例和数量，共采用改了70种选择，所以最后训练出来的模型为17M，比初始化4.8M大很多，不过也已经很小了。

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三、SqueezeNet+Faster RCNN+OHEM

OHEM无非就是多了一个readonly部分，不过加上之后效果会好很多，和上面的方式一致，放出一部分prototxt，其他的课自行补上。从rpn那里开始，前面部分和上面给出的完全一样

#====== 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: 140 dim: -1 dim: 0 } }}layer {  name: 'proposal'  type: 'Python'  bottom: 'rpn_cls_prob_reshape'  bottom: 'rpn_bbox_pred'  bottom: 'im_info'  top: 'rpn_rois'  python_param {    module: 'rpn.proposal_layer'    layer: 'ProposalLayer'    param_str: "'feat_stride': 16"  }}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': 4"  }}############################ Readonly RoI Network ########### Start ##########layer {  name: "roi_pool5_readonly"  type: "ROIPooling"  bottom: "fire9/concat"  bottom: "rois"  top: "pool5_readonly"  propagate_down: false  propagate_down: false  roi_pooling_param {    pooled_w: 6    pooled_h: 6    spatial_scale: 0.0625 # 1/16  }}layer {  name: "conv1_last_readonly"  type: "Convolution"  bottom: "pool5_readonly"  top: "conv1_last_readonly"  propagate_down: false    param {    name: "conv1_last_w"  }  param {    name: "conv1_last_b"  }  convolution_param {    num_output: 1000    kernel_size: 1    weight_filler {      type: "gaussian"      mean: 0.0      std: 0.01    }  }}layer {  name: "relu/conv1_last_readonly"  type: "ReLU"  bottom: "conv1_last_readonly"  top: "relu/conv1_last_readonly"  propagate_down: false}layer {  name: "cls_score_readonly"  type: "InnerProduct"  bottom: "relu/conv1_last_readonly"  top: "cls_score_readonly"  propagate_down: false  param {    name: "cls_score_w"  }  param {    name: "cls_score_b"  }  inner_product_param {    num_output: 4    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "bbox_pred_readonly"  type: "InnerProduct"  bottom: "relu/conv1_last_readonly"  top: "bbox_pred_readonly"  propagate_down: false  param {    name: "bbox_pred_w"  }  param {    name: "bbox_pred_b"  }  inner_product_param {    num_output: 16    weight_filler {      type: "gaussian"      std: 0.001    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "cls_prob_readonly"  type: "Softmax"  bottom: "cls_score_readonly"  top: "cls_prob_readonly"  propagate_down: false}layer {  name: "hard_roi_mining"  type: "Python"  bottom: "cls_prob_readonly"  bottom: "bbox_pred_readonly"  bottom: "rois"  bottom: "labels"  bottom: "bbox_targets"  bottom: "bbox_inside_weights"  bottom: "bbox_outside_weights"  top: "rois_hard"  top: "labels_hard"  top: "bbox_targets_hard"  top: "bbox_inside_weights_hard"  top: "bbox_outside_weights_hard"  propagate_down: false  propagate_down: false  propagate_down: false  propagate_down: false  propagate_down: false  propagate_down: false  propagate_down: false  python_param {    module: "roi_data_layer.layer"    layer: "OHEMDataLayer"    param_str: "'num_classes': 4"  }}########## End ############# Readonly RoI Network #############################===================== RCNN =============layer {  name: "roi_pool5"  type: "ROIPooling"  bottom: "fire9/concat"  bottom: "rois_hard"  top: "roi_pool5"  propagate_down: true  propagate_down: false  roi_pooling_param {    pooled_w: 7    pooled_h: 7    spatial_scale: 0.0625 # 1/16  }}layer {  name: "conv1_last"  type: "Convolution"  bottom: "roi_pool5"  top: "conv1_last"  param {       lr_mult: 1.0       name: "conv1_last_w"      }  param {       lr_mult: 1.0       name: "conv1_last_b"      }  convolution_param {    num_output: 1000    kernel_size: 1    weight_filler {      type: "gaussian"      mean: 0.0      std: 0.01    }  }}layer {  name: "relu/conv1_last"  type: "ReLU"  bottom: "conv1_last"  top: "relu/conv1_last"}layer {  name: "cls_score"  type: "InnerProduct"  bottom: "relu/conv1_last"  top: "cls_score"  param {    lr_mult: 1    name: "cls_score_w"  }  param {    lr_mult: 2    name: "cls_score_b"  }  inner_product_param {    num_output: 4    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "bbox_pred"  type: "InnerProduct"  bottom: "relu/conv1_last"  top: "bbox_pred"  param {    lr_mult: 1    name: "bbox_pred_w"  }  param {    lr_mult: 2    name: "bbox_pred_b"  }  inner_product_param {    num_output: 16    weight_filler {      type: "gaussian"      std: 0.001    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "loss_cls"  type: "SoftmaxWithLoss"  bottom: "cls_score"  bottom: "labels_hard"  propagate_down: true  propagate_down: false  top: "loss_cls"  loss_weight: 1}layer {  name: "loss_bbox"  type: "SmoothL1Loss"  bottom: "bbox_pred"  bottom: "bbox_targets_hard"  bottom: "bbox_inside_weights_hard"  bottom: "bbox_outside_weights_hard"  top: "loss_bbox"  loss_weight: 1  propagate_down: false  propagate_down: false  propagate_down: false  propagate_down: false}
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结构图如下： 
 
比前面训练的多一个readonly部分，具体可参考论文： 
Training Region-based Object Detectors with Online Hard Example Mining 
https://arxiv.org/abs/1604.03540 
 
至此，SqueezeNet+Faster RCNN 框架便介绍完了，运行速度在GPU下大概是ZF的5倍，CPU下大概为2。5倍。

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原文链接：

http://blog.csdn.net/u011956147/article/details/53714616