ChenYingpeng / Darknet2caffe
Convert darknet weights to caffemodel
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Requirements
Python2.7
Caffe
Pytorch >= 0.40
Add Caffe Layers
- Copy
caffe_layers/mish_layer/mish_layer.hpp,caffe_layers/upsample_layer/upsample_layer.hppintoinclude/caffe/layers/. - Copy
caffe_layers/mish_layer/mish_layer.cpp mish_layer.cu,caffe_layers/upsample_layer/upsample_layer.cpp upsample_layer.cuintosrc/caffe/layers/. - Copy
caffe_layers/pooling_layer/pooling_layer.cppintosrc/caffe/layers/.Note:only work for yolov3-tiny,use with caution. - Add below code into
src/caffe/proto/caffe.proto.
// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
optional TileParameter tile_param = 138;
optional VideoDataParameter video_data_param = 207;
optional WindowDataParameter window_data_param = 129;
++optional UpsampleParameter upsample_param = 149; //added by chen for Yolov3, make sure this id 149 not the same as before.
++optional MishParameter mish_param = 150; //added by chen for yolov4,make sure this id 150 not the same as before.
}
// added by chen for YoloV3
++message UpsampleParameter{
++ optional int32 scale = 1 [default = 1];
++}
// Message that stores parameters used by MishLayer
++message MishParameter {
++ enum Engine {
++ DEFAULT = 0;
++ CAFFE = 1;
++ CUDNN = 2;
++ }
++ optional Engine engine = 2 [default = DEFAULT];
++}
5.remake caffe.
Demo
$ python cfg[in] weights[in] prototxt[out] caffemodel[out]
Example
python cfg/yolov4.cfg weights/yolov4.weights prototxt/yolov4.prototxt caffemodel/yolov4.caffemodel
partial log as below.
I0522 10:19:19.015708 25251 net.cpp:228] layer1-act does not need backward computation.
I0522 10:19:19.015712 25251 net.cpp:228] layer1-scale does not need backward computation.
I0522 10:19:19.015714 25251 net.cpp:228] layer1-bn does not need backward computation.
I0522 10:19:19.015718 25251 net.cpp:228] layer1-conv does not need backward computation.
I0522 10:19:19.015722 25251 net.cpp:228] input does not need backward computation.
I0522 10:19:19.015725 25251 net.cpp:270] This network produces output layer139-conv
I0522 10:19:19.015731 25251 net.cpp:270] This network produces output layer150-conv
I0522 10:19:19.015736 25251 net.cpp:270] This network produces output layer161-conv
I0522 10:19:19.015911 25251 net.cpp:283] Network initialization done.
unknow layer type yolo
unknow layer type yolo
save prototxt to prototxt/yolov4.prototxt
save caffemodel to caffemodel/yolov4.caffemodel
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