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R-FCN+ResNet-50 训练模型
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本文轉載自：
http://blog.csdn.net/sinat_30071459/article/details/53202977
說明：
本文假設你已經做好數據集，格式和VOC2007一致，并且Linux系統已經配置好caffe所需環境（博客里教程很多），下面是訓練的一些修改。
py-R-FCN源碼下載地址：
https://github.com/Orpine/py-R-FCN
也有Matlab版本：
https://github.com/daijifeng001/R-FCN
本文用到的是Python版本。
本文主要參考https://github.com/Orpine/py-R-FCN。
準備工作：
（1）配置caffe環境(網上找教程)
（2）安裝cython,?python-OpenCV,?easydict
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pip?install?cython?? pip?install?easydict?? apt-get?install?python-opencv??
然后，我們就可以開始配置R-FCN了。
1.下載py-R-FCN
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git?clone?https://github.com/Orpine/py-R-FCN.git??
下面稱你的py-R-FCN路徑為RFCN_ROOT.
2.下載caffe
注意，該caffe版本是微軟版本
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cd?$RFCN_ROOT?? git?clone?https://github.com/Microsoft/caffe.git?? 如果一切正常的話，python代碼會自動添加環境變量 $RFCN_ROOT/caffe/python，否則，你需要自己添加環境變量。
3.Build Cython
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cd?$RFCN_ROOT/lib?? make?? 4.Build caffe和pycaffe
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cd?$RFCN_ROOT/caffe?? cp?Makefile.config.example?Makefile.config?? 然后修改Makefile.config。caffe必須支持python層，所以WITH_PYTHON_LAYER := 1是必須的。其他配置可參考：Makefile.config
接著：
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cd?$RFCN_ROOT/caffe?? make?-j8?&&?make?pycaffe?? 如果沒有出錯，則：
5.測試Demo
經過上面的工作，我們可以測試一下是否可以正常運行。
我們需要下載作者訓練好的模型，地址：鏈接：http://pan.baidu.com/s/1kVGy8DL?密碼：pwwg
然后將模型放在$RFCN_ROOT/data。看起來是這樣的：
$RFCN_ROOT/data/rfcn_models/resnet50_rfcn_final.caffemodel
$RFCN_ROOT/data/rfcn_models/resnet101_rfcn_final.caffemodel 運行：
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cd?$RFCN_ROOT?? ./tools/demo_rfcn.py?--net?ResNet-50?? 6.用我們的數據集訓練
（1）拷貝數據集
假設我們已經做好數據集了，格式是和VOC2007一致，將你的數據集
拷貝到$RFCN_ROOT/data下。看起來是這樣的：
$VOCdevkit0712/
# development kit
$VOCdevkit/VOCcode/
# VOC utility code
$VOCdevkit/VOC0712
# image sets, annotations, etc.
# ... and several other directories ... 如果你的文件夾名字不是VOCdevkit0712和VOC0712，修改成0712就行了。
（作者是用VOC2007和VOC2012訓練的，所以文件夾名字帶0712。也可以修改代碼，但是那樣比較麻煩一些，修改文件夾比較簡單）
（2）下載預訓練模型
本文以ResNet-50為例，因此下載ResNet-50-model.caffemodel。下載地址：鏈接：http://pan.baidu.com/s/1slRHD0L?密碼：r3ki
然后將caffemodel放在$RFCN_ROOT/data/imagenet_models ?(data下沒有該文件夾就新建一個)
（3）修改模型網絡
打開$RFCN_ROOT/models/pascal_voc/ResNet-50/rfcn_end2end ?(以end2end為例)
注意：下面的cls_num指的是你數據集的類別數+1（背景）。比如我有15類，+1類背景，cls_num=16.
<1>修改class-aware/train_ohem.prototxt
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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?? ??}?? }?? [plain]?view plaincopy
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?? ??}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }??
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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
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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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
layer?{?? ????name:?"cls_prob_reshape"?? ????type:?"Reshape"?? ????bottom:?"cls_prob_pre"?? ????top:?"cls_prob"?? ????reshape_param?{?? ????????shape?{?? ????????????dim:?-1?? ????????????dim:?16??#cls_num?? ????????}?? ????}?? }?? [plain]?view plaincopy
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
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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?? ??}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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
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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?###?? ??}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
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
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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?? ????}?? }?? [plain]?view plaincopy
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?? ????}?? }?? [plain]?view plaincopy
layer?{?? ????name:?"cls_prob_reshape"?? ????type:?"Reshape"?? ????bottom:?"cls_prob_pre"?? ????top:?"cls_prob"?? ????reshape_param?{?? ????????shape?{?? ????????????dim:?-1?? ????????????dim:?16?#cls_num???###?? ????????}?? ????}?? }?? (4)修改代碼
<1>$RFCN/lib/datasets/pascal_voc.py
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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?? ?????????????????????????'你的標簽1','你的標簽2',你的標簽3','你的標簽4'?? ??????????????????????)?? 改成你的數據集標簽。
<2>$RFCN_ROOT/lib/datasets/imdb.py
主要是assert (boxes[:, 2] >= boxes[:, 0]).all()可能出現AssertionError，具體解決辦法參考：
http://blog.csdn.net/xzzppp/article/details/52036794
PS：
上面將有無ohem的prototxt都改了，但是這里訓練用的是ohem。
另外，默認的迭代次數很大，可以修改$RFCN\experiments\scripts\rfcn_end2end_ohem.sh:
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case?$DATASET?in?? ??pascal_voc)?? ????TRAIN_IMDB="voc_0712_trainval"?? ????TEST_IMDB="voc_0712_test"?? ????PT_DIR="pascal_voc"?? ????ITERS=110000?? 修改ITERS為你想要的迭代次數即可。
（5）開始訓練
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cd?$RFCN_ROOT?? ./experiments/scripts/rfcn_end2end_ohem.sh?0?ResNet-50?pascal_voc?? 正常的話，就開始迭代了：
$
RFCN_ROOT/experiments/scripts里還有一些其他的訓練方法，也可以測試一下（經過上面的修改，無ohem的end2end訓練也改好了，其他訓練方法修改的過程差不多）。
（6）結果
將訓練得到的模型($RFCN_ROOT/output/rfcn_end2end_ohem/voc_0712_trainval里最后的caffemodel)拷貝到$RFCN_ROOT/data/rfcn_models下，然后打開$RFCN_ROOT/tools/demo_rfcn.py，將CLASSES修改成你的標簽，NETS修改成你的model，im_names修改成你的測試圖片(放在data/demo下),最后：
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cd?$RFCN_ROOT?? ./tools/demo_rfcn.py?--net?ResNet-50??
我將顯示的標簽改為了中文，修改方法參考：http://blog.csdn.net/sinat_30071459/article/details/51694037
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