- caffe训练配合TensorRT工程化，(戳这里，封装好没有的独立TensorRT)[https://github.com/dlunion/tensorRTIntegrate]
- auto_grad.cpp是自己定义的自动微分，配合CC实现自动求导
- cc_nb.cpp是网络构建，cc_network_build，实现类似torch的方式写网络结构
- cc_util.cpp是通用工具类，实现各种日常生活需要的函数
- 基于Caffe改造的C++接口框架
- 添加C++代码网络构建引擎
- 自定义层更容易，并且容易调试
- 更友好的Inference结构
- CUDA 10.0
- CUDNN 7.5
- 改造后，更容易实现和TensorRT结合
- 支持Linux、Windows，便于部署和移植
- 将要支持Tensorboard
- 1、请下载3rd共555MB，依赖的库，解压到README.md同级目录
- 2、安装cuda10
- 3、使用visual studio 2013打开windows-gpu.sln工程并选择ReleaseDLL编译即可
- 1、按照caffe for Linux安装依赖项
- 2、执行make all -j32
- 在release里面有libcaffe.dll、libcaffe.lib等文件，需要依赖cudnn64_7.dll(cudnn7.5，在3rd共555MB压缩包中有)
- 头文件是cc_v5.h
- cc_nb.h和cc_nb.cpp是network build engine文件，独立并依赖于cc_v5.h。可以根据需求修改他
- 案例在release文件夹里面，有训练和inference例子
cc::setGPU(0);
cc::installRegister();
INSTALL_LAYER(CifarDataLayer);
auto data = L::data("CifarDataLayer", {"image", "label"}, "data");
auto image = data[0];
auto label = data[1];
auto x = resnet18(image, 10);
auto test = cc::metric::classifyAccuracy(x, label, "accuracy");
auto loss = cc::loss::softmax_cross_entropy(x, label, "loss");
int trainDatasetSize = 50000;
int valDatasetSize = 10000;
int trainEpochs = 150;
int epochIters = trainDatasetSize / BatchSize;
cc::engine::caffe::buildGraphToFile(resnet18(L::input({ 1, 3, InputHeight, InputWidth }, "image"), 10), "deploy.prototxt");
auto op = cc::optimizer::momentumStochasticGradientDescent(cc::learningrate::step(0.1, 0.1, 20*epochIters), 0.9);
op->test_initialization = false;
op->test_interval = epochIters;
op->test_iter = valDatasetSize / BatchSize;
op->weight_decay = 0.0002f;
op->average_loss = 1;
op->max_iter = trainEpochs * epochIters;
op->display = 10;
op->device_ids = { 0 };
//op->reload_weights = "pretrain.caffemodel";
op->minimize({ loss, test });
//op->minimizeFromFile("net.prototxt");
saveToFile("solver.prototxt", op->seril());
cc::engine::caffe::buildGraphToFile({ loss, test }, "train.prototxt");
//setup test classifier callback
registerOnTestClassificationFunction([&](Solver* solver, float testloss, int index, const char* itemname, float accuracy){
//do smothing...
});
cc::train::caffe::run(op, [](OThreadContextSession* session, int step, float smoothed_loss){
//do smothing...
});cc::Tensor resnet18(const cc::Tensor& input, int numunit){
auto x = input;
x = resnet_conv(x, { 3, 3, 16 }, "conv1", 1, true, true);
x = resnet_block(x, 2, 1, 16, 32, 2);
x = resnet_block(x, 3, 2, 32, 64, 2);
x = resnet_block(x, 4, 2, 64, 64, 2);
x = L::avg_pooling2d(x, { 1, 1 }, { 1, 1 }, { 0, 0 }, true, "pool5");
x = L::dense(x, numunit, "fc", true);
//修改Dense（全连接层）参数，其他层也如此
L::ODense* denseLayer = (L::ODense*)x->owner.get();
denseLayer->weight_initializer.reset(new cc::Initializer("msra"));
denseLayer->bias_initializer.reset(new cc::Initializer("constant", 0));
return x;
}class LeftPooling : public cc::BaseLayer{
public:
SETUP_LAYERFUNC(LeftPooling);
virtual void setup(const char* name, const char* type, const char* param_str, int phase, Blob** bottom, int numBottom, Blob** top, int numTop){
diff_ = newBlob();
diff_->reshapeLike(bottom[0]);
top[0]->reshapeLike(bottom[0]);
}
virtual void forward(Blob** bottom, int numBottom, Blob** top, int numTop){
//top[0]->copyFrom(bottom[0]);
auto output = top[0];
auto input = bottom[0];
int fw = input->width();
int fh = input->height();
vector<float> maxColumn(fh);
vector<int> maxIndex(fh);
for (int n = 0; n < input->num(); ++n){
for (int c = 0; c < input->channel(); ++c){
float* ptr = input->mutable_cpu_data() + input->offset(n, c);
float* out = output->mutable_cpu_data() + output->offset(n, c);
float* diffptr = diff_->mutable_cpu_diff() + diff_->offset(n, c);
for (int j = 0; j < fh; ++j){
maxColumn[j] = ptr[j * fw + fw - 1];
maxIndex[j] = fw - 1;
}
for (int i = fw - 1; i >= 0; --i){
for (int j = 0; j < fh; ++j){
float ival = ptr[j * fw + i];
float lval = maxColumn[j];
float mxval = max(ival, lval);
if (ival > lval)
maxIndex[j] = i;
diffptr[j * fw + maxIndex[j]]++;
out[j * fw + i] = mxval;
maxColumn[j] = mxval;
}
}
}
}
}
virtual void backward(Blob** bottom, int numBottom, Blob** top, int numTop, const bool* propagate_down){
auto input = bottom[0];
auto output = top[0];
float* inputDiff = input->mutable_cpu_diff();
float* outputDiff = output->mutable_cpu_diff();
float* diffPtr = diff_->mutable_cpu_diff();
int count = input->count();
for (int i = 0; i < count; ++i)
*inputDiff++ = *outputDiff++ * (*diffPtr++ > 0 ? 1 : 0);
}
private:
shared_ptr<Blob> diff_;
};- 使用的时候
INSTALL_LAYER(LeftPooling);
path1 = L::custom("LeftPooling", path1, {}, "leftPooling");
模型下载pose_iter_440000.caffemodel
cc::setGPU(0);
auto image = L::input({ 1, 3, 688, 368 }, "image");
auto poseNetwork = pose(image, 5);
auto net = cc::engine::caffe::buildNet(poseNetwork);
//auto net = loadNetFromPrototxt("net.prototxt");
net->weightsFromFile("pose_iter_440000.caffemodel");
Mat im = imread("demo.jpg");
Mat show = im.clone();
Mat rawShow = show.clone();
im.convertTo(im, CV_32F, 1 / 256.0, -0.5);
net->input_blob(0)->setData(0, im);
net->forward();
float scalex = im.cols / (float)net->input_blob(0)->width();
float scaley = im.rows / (float)net->input_blob(0)->height();
Blob* keypoints = net->blob("Mconv7_stage6_L2");- 1.OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
- 2.Deformable-ConvNets-caffe