【目標檢測四】 R-FCN使用教程（測試或訓練自己的數據）

原創 南石北岸生 2020-06-02 00:23

用了好久，都沒寫，還是按慣例寫一下。總體步驟其實就是上面流程圖那樣。安裝、配置、下載預訓練模型、放入自己數據、修改模型結構然後訓練和測試，每個模型都是這個流程。

 

安裝

1.下載源碼

https://github.com/Orpine/py-R-FCN

Py=python版本

2.安裝caffe依賴和caffe

1）安裝依賴

pip install cython

pip install easydict

apt-get install python-opencv

2）下載caffe

git clone https://github.com/Microsoft/caffe.git

3）配置caffe

4）打開終端，cd 你的RFCN路徑/lib，然後make一下

5）編譯caffe的python接口：make pycaffe

 

安裝完畢

下面下載預訓練模型測試試一試：

這個要翻牆下載，裏面有res50和res101的imagenet預訓練模型。

https://download.csdn.net/download/gusui7202/10969490

測試

把模型放到rfcn項目的對應位置

$RFCN_ROOT/data/rfcn_models/resnet50_rfcn_final.caffemodel

$RFCN_ROOT/data/rfcn_models/resnet101_rfcn_final.caffemodel

打開終端，運行

cd $RFCN_ROOT

./tools/demo_rfcn.py --net ResNet-50

訓練自己的數據

在data文件夾下放入自己的數據集，格式如下

VOCdevkit/VOC2007

VOC2007裏面就是自己的數據了，主要有三個文件夾

JPEGImages、Annotations、ImageSets

1)修改模型的結構參數

因爲自己的數據類別和預訓練的不一樣，所以輸出神經元節點的數量不一樣，要自己設置。這裏涉及到一個問題，那就是預訓練模型和測試模型的區別。

預訓練模型是別人保存的中間網絡權重參數，其實尾巴的輸出神經元數量沒有保存，所以自己微調一下就能用，而訓練好的測試模型就是將模型參數全部保存下來了，如果你的數據類別和別人一樣那麼你也可以用，當然，不一樣的可能性還是很大的。就是改改數字而已。

修改的文件一共有7個：

<1>修改class-aware/train_ohem.prototxt

<2>修改class-aware/test.prototxt

<3>修改train_agnostic.prototxt

<4>修改train_agnostic_ohem.prototxt

<5>修改test_agnostic.prototxt

<6>$RFCN/lib/datasets/pascal_voc.py

<7>$RFCN_ROOT/lib/datasets/imdb.py

都在Models/pascal_voc裏面，res50和res101分別對應不同文件夾，用哪個改哪個，這裏以50的以end2end爲例，

打開$RFCN_ROOT/models/pascal_voc/ResNet-50/rfcn_end2end  

cls_num=數據集的類別數+1（背景）

eg:15類的數據，+1類背景，cls_num=16.

 

<1>修改class-aware/train_ohem.prototxt

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': 16" #cls_num
  }
}

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': 16" #cls_num
  }
}

layer {
    bottom: "conv_new_1"
    top: "rfcn_cls"
    name: "rfcn_cls"
    type: "Convolution"
    convolution_param {
        num_output: 784 #cls_num*(score_maps_size^2)
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "conv_new_1"
    top: "rfcn_bbox"
    name: "rfcn_bbox"
    type: "Convolution"
    convolution_param {
        num_output: 3136 #4*cls_num*(score_maps_size^2)
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "rfcn_cls"
    bottom: "rois"
    top: "psroipooled_cls_rois"
    name: "psroipooled_cls_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 16  #cls_num
        group_size: 7
    }
}

layer {
    bottom: "rfcn_bbox"
    bottom: "rois"
    top: "psroipooled_loc_rois"
    name: "psroipooled_loc_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 64 #4*cls_num
        group_size: 7
    }
}

 

<2>修改class-aware/test.prototxt

layer {
    bottom: "conv_new_1"
    top: "rfcn_cls"
    name: "rfcn_cls"
    type: "Convolution"
    convolution_param {
        num_output: 784 #cls_num*(score_maps_size^2)
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "conv_new_1"
    top: "rfcn_bbox"
    name: "rfcn_bbox"
    type: "Convolution"
    convolution_param {
        num_output: 3136 #4*cls_num*(score_maps_size^2)
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "rfcn_cls"
    bottom: "rois"
    top: "psroipooled_cls_rois"
    name: "psroipooled_cls_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 16  #cls_num
        group_size: 7
    }
}

layer {
    bottom: "rfcn_bbox"
    bottom: "rois"
    top: "psroipooled_loc_rois"
    name: "psroipooled_loc_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 64  #4*cls_num
        group_size: 7
    }
}
layer {
    name: "cls_prob_reshape"
    type: "Reshape"
    bottom: "cls_prob_pre"
    top: "cls_prob"
    reshape_param {
        shape {
            dim: -1
            dim: 16  #cls_num
        }
    }
}

layer {
    name: "bbox_pred_reshape"
    type: "Reshape"
    bottom: "bbox_pred_pre"
    top: "bbox_pred"
    reshape_param {
        shape {
            dim: -1
            dim: 64  #4*cls_num
        }
    }
}

<3>修改train_agnostic.prototxt

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': 16"  #cls_num
  }
}
layer {
    bottom: "conv_new_1"
    top: "rfcn_cls"
    name: "rfcn_cls"
    type: "Convolution"
    convolution_param {
        num_output: 784 #cls_num*(score_maps_size^2)   ###
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "rfcn_cls"
    bottom: "rois"
    top: "psroipooled_cls_rois"
    name: "psroipooled_cls_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 16 #cls_num   ###
        group_size: 7
    }
}

<4>修改train_agnostic_ohem.prototxt

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': 16" #cls_num ###
  }
}

layer {
    bottom: "conv_new_1"
    top: "rfcn_cls"
    name: "rfcn_cls"
    type: "Convolution"
    convolution_param {
        num_output: 784 #cls_num*(score_maps_size^2)   ###
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "rfcn_cls"
    bottom: "rois"
    top: "psroipooled_cls_rois"
    name: "psroipooled_cls_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 16 #cls_num   ###
        group_size: 7
    }
}

<5>修改test_agnostic.prototxt

 

layer {
    bottom: "conv_new_1"
    top: "rfcn_cls"
    name: "rfcn_cls"
    type: "Convolution"
    convolution_param {
        num_output: 784 #cls_num*(score_maps_size^2) ###
        kernel_size: 1
        pad: 0
        weight_filler {
            type: "gaussian"
            std: 0.01
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
}

layer {
    bottom: "rfcn_cls"
    bottom: "rois"
    top: "psroipooled_cls_rois"
    name: "psroipooled_cls_rois"
    type: "PSROIPooling"
    psroi_pooling_param {
        spatial_scale: 0.0625
        output_dim: 16 #cls_num   ###
        group_size: 7
    }
}

layer {
    name: "cls_prob_reshape"
    type: "Reshape"
    bottom: "cls_prob_pre"
    top: "cls_prob"
    reshape_param {
        shape {
            dim: -1
            dim: 16 #cls_num   ###
        }
    }
}

2）修改部分代碼

因爲自己的數據集標籤具體名字也不一定，要自己設置

<1>$RFCN/lib/datasets/pascal_voc.py

class pascal_voc(imdb):
    def __init__(self, image_set, year, devkit_path=None):
        imdb.__init__(self, 'voc_' + year + '_' + image_set)
        self._year = year
        self._image_set = image_set
        self._devkit_path = self._get_default_path() if devkit_path is None \
                            else devkit_path
        self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
        self._classes = ('__background__', # always index 0
                         '你的標籤1','你的標籤2',你的標籤3','你的標籤4'
                      )

 

<2>$RFCN_ROOT/lib/datasets/imdb.py

這裏會報錯，參考：

http://blog.csdn.net/xzzppp/article/details/52036794

修改迭代次數在lib/data/pascal_voc.py裏面

3）訓練

cd RFCN根目錄

./experiments/scripts/rfcn_end2end_ohem.sh 0 ResNet-50 pascal_voc

 

4）測試

cd RFCN根目錄

./tools/demo_rfcn.py --net ResNet-50

參考文獻：

https://blog.csdn.net/sinat_30071459/article/details/53202977

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