版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/sinat_30071459/article/details/53202977
说明：
本文假设你已经做好数据集，格式和VOC2007一致，并且Linux系统已经配置好caffe所需环境（博客里教程很多），下面是训练的一些修改。
py-R-FCN源码下载地址：
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。
本文主要参考https://github.com/Orpine/py-R-FCN。
准备工作：
（1）配置caffe环境(网上找教程)
（2）安装cython, python-opencv, easydict
pip install cython
pip install easydict
apt-get install python-opencv然后，我们就可以开始配置R-FCN了。
1.下载py-R-FCN
git clone https://github.com/Orpine/py-R-FCN.git下面称你的py-R-FCN路径为RFCN_ROOT.
2.下载caffe
注意，该caffe版本是微软版本
cd $RFCN_ROOT
git clone https://github.com/Microsoft/caffe.git3.Build Cython
cd $RFCN_ROOT/lib
make4.Build caffe和pycaffe
cd $RFCN_ROOT/caffe
cp Makefile.config.example Makefile.config
接着：
cd $RFCN_ROOT/caffe
make -j8 && make pycaffe
5.测试Demo
经过上面的工作，我们可以测试一下是否可以正常运行。
我们需要下载作者训练好的模型，地址：链接：http://pan.baidu.com/s/1kVGy8DL 密码：pwwg
然后将模型放在$RFCN_ROOT/data。看起来是这样的：
$RFCN_ROOT/data/rfcn_models/resnet50_rfcn_final.caffemodel $RFCN_ROOT/data/rfcn_models/resnet101_rfcn_final.caffemodel运行：
cd $RFCN_ROOT
./tools/demo_rfcn.py --net ResNet-50
6.用我们的数据集训练
（1）拷贝数据集
假设我们已经做好数据集了，格式是和VOC2007一致，将你的数据集
拷贝到$RFCN_ROOT/data下。看起来是这样的：
$VOCdevkit0712/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC0712 # image sets, annotations, etc. # ... and several other directories ...如果你的文件夹名字不是VOCdevkit0712和VOC0712，修改成0712就行了。
（作者是用VOC2007和VOC2012训练的，所以文件夹名字带0712。也可以修改代码，但是那样比较麻烦一些，修改文件夹比较简单）
（2）下载预训练模型
本文以ResNet-50为例，因此下载ResNet-50-model.caffemodel。下载地址：链接：http://pan.baidu.com/s/1slRHD0L 密码：r3ki
然后将caffemodel放在$RFCN_ROOT/data/imagenet_models (data下没有该文件夹就新建一个)
（3）修改模型网络
打开$RFCN_ROOT/models/pascal_voc/ResNet-50/rfcn_end2end (以end2end为例)
注意：下面的cls_num指的是你数据集的类别数+1（背景）。比如我有15类，+1类背景，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
}
}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 ###
}
}
}(4)修改代码
<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
'你的标签1','你的标签2',你的标签3','你的标签4'
)
<2>$RFCN_ROOT/lib/datasets/imdb.py
主要是assert (boxes[:, 2] >= boxes[:, 0]).all()可能出现AssertionError，具体解决办法参考：
PS：
上面将有无ohem的prototxt都改了，但是这里训练用的是ohem。
另外，默认的迭代次数很大，可以修改$RFCN\experiments\scripts\rfcn_end2end_ohem.sh:
case $DATASET in
pascal_voc)
TRAIN_IMDB="voc_0712_trainval"
TEST_IMDB="voc_0712_test"
PT_DIR="pascal_voc"
ITERS=110000修改ITERS为你想要的迭代次数即可。
（5）开始训练
cd $RFCN_ROOT
./experiments/scripts/rfcn_end2end_ohem.sh 0 ResNet-50 pascal_voc
正常的话，就开始迭代了：
$
RFCN_ROOT/experiments/scripts里还有一些其他的训练方法，也可以测试一下（经过上面的修改，无ohem的end2end训练也改好了，其他训练方法修改的过程差不多）。
（6）结果
将训练得到的模型($RFCN_ROOT/output/rfcn_end2end_ohem/voc_0712_trainval里最后的caffemodel)拷贝到$RFCN_ROOT/data/rfcn_models下，然后打开$RFCN_ROOT/tools/demo_rfcn.py，将CLASSES修改成你的标签，NETS修改成你的model，im_names修改成你的测试图片(放在data/demo下),最后：
cd $RFCN_ROOT
./tools/demo_rfcn.py --net ResNet-50