diff --git a/include/tkDNN/Layer.h b/include/tkDNN/Layer.h index 188cffb..a6c639b 100644 --- a/include/tkDNN/Layer.h +++ b/include/tkDNN/Layer.h @@ -32,7 +32,7 @@ enum layerType_t { class Layer { public: - Layer(Network *net); + Layer(Network *net, bool final = false); virtual ~Layer(); virtual layerType_t getLayerType() = 0; @@ -43,6 +43,7 @@ public: dataDim_t input_dim, output_dim; dnnType *dstData; //where results will be putted + bool final; //if the layer is the final one std::string getLayerName() { layerType_t type = getLayerType(); @@ -80,7 +81,7 @@ class LayerWgs : public Layer { public: LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kt, - std::string fname_weights, bool batchnorm = false, bool additional_bias = false); + std::string fname_weights, bool batchnorm = false, bool additional_bias = false, bool final = false); virtual ~LayerWgs(); int inputs, outputs; @@ -160,7 +161,7 @@ class Conv2d : public LayerWgs { public: Conv2d( Network *net, int out_ch, int kernelH, int kernelW, int strideH, int strideW, int paddingH, int paddingW, - std::string fname_weights, bool batchnorm = false, bool deConv = false); + std::string fname_weights, bool batchnorm = false, bool deConv = false, bool final = false); virtual ~Conv2d(); virtual layerType_t getLayerType() { return LAYER_CONV2D; }; @@ -217,15 +218,15 @@ public: int out_ch; int deformableGroup; int kernelH, kernelW, strideH, strideW, paddingH, paddingW; -protected: - dnnType *ones_d1; dnnType *ones_d2; - cudnnTensorDescriptor_t biasTensorDesc; int chunk_dim; dnnType *offset, *mask; dnnType *output_conv; +protected: + + cudnnTensorDescriptor_t biasTensorDesc; void initCUDNN(); }; diff --git a/include/tkDNN/NetworkRT.h b/include/tkDNN/NetworkRT.h index d30da03..590b323 100644 --- a/include/tkDNN/NetworkRT.h +++ b/include/tkDNN/NetworkRT.h @@ -31,6 +31,7 @@ using namespace nvinfer1; #include "pluginsRT/YoloRT.h" #include "pluginsRT/UpsampleRT.h" //#include "pluginsRT/Int8Calibrator.h" +#include "pluginsRT/DeformableConvRT.h" class PluginFactory : IPluginFactory { @@ -85,6 +86,7 @@ public: nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l); nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Yolo *l); nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Upsample *l); + nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, DeformConv2d *l); bool serialize(const char *filename); bool deserialize(const char *filename); diff --git a/include/tkDNN/pluginsRT/DeformableConvRT.h b/include/tkDNN/pluginsRT/DeformableConvRT.h new file mode 100644 index 0000000..6a85538 --- /dev/null +++ b/include/tkDNN/pluginsRT/DeformableConvRT.h @@ -0,0 +1,80 @@ +#include +#include "../kernels.h" + + +class DeformableConvRT : public IPlugin { + + + +public: + DeformableConvRT(tk::dnn::DeformConv2d *deformable) { + this->defRT = deformable; + } + + ~DeformableConvRT(){ + + } + + int getNbOutputs() const override { + return 1; + } + + Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override { + return DimsCHW{defRT->output_dim.c, defRT->output_dim.h, defRT->output_dim.w}; + } + + void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override { + } + + int initialize() override { + + return 0; + } + + virtual void terminate() override { + } + + virtual size_t getWorkspaceSize(int maxBatchSize) const override { + return 0; + } + + virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override { + + dnnType *srcData = (dnnType*)reinterpret_cast(inputs[0]); + dnnType *output_conv = (dnnType*)reinterpret_cast(inputs[1]); + + // split conv2d outputs into offset to mask + checkCuda(cudaMemcpy(defRT->offset, defRT->output_conv, 2*defRT->chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); + checkCuda(cudaMemcpy(defRT->mask, defRT->output_conv + 2*defRT->chunk_dim, defRT->chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); + // kernel sigmoide + activationSIGMOIDForward(defRT->mask, defRT->mask, defRT->chunk_dim); + + // deformable convolution + dcn_v2_cuda_forward(srcData, defRT->data_d, + defRT->bias2_d, defRT->ones_d1, + defRT->offset, defRT->mask, + reinterpret_cast(outputs[0]), defRT->ones_d2, + defRT->kernelH, defRT->kernelW, + defRT->strideH, defRT->strideW, + defRT->paddingH, defRT->paddingW, + 1, 1, + defRT->deformableGroup, + defRT->preconv->input_dim.n, defRT->preconv->input_dim.c, defRT->preconv->input_dim.h, defRT->preconv->input_dim.w, + defRT->output_dim.n, defRT->output_dim.c, defRT->output_dim.h, defRT->output_dim.w, + defRT->chunk_dim); + + return 0; + } + + + virtual size_t getSerializationSize() override { + return 0; + } + + virtual void serialize(void* buffer) override { + char *buf = reinterpret_cast(buffer); + } + + int size; + tk::dnn::DeformConv2d *defRT; +}; diff --git a/src/Conv2d.cpp b/src/Conv2d.cpp index 05fec83..b8296ac 100644 --- a/src/Conv2d.cpp +++ b/src/Conv2d.cpp @@ -119,10 +119,10 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) { Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW, int strideH, int strideW, int paddingH, int paddingW, - std::string fname_weights, bool batchnorm, bool deConv) : + std::string fname_weights, bool batchnorm, bool deConv, bool final) : LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1, - fname_weights, batchnorm) { + fname_weights, batchnorm, false, final) { this->kernelH = kernelH; this->kernelW = kernelW; diff --git a/src/DeformConv2d.cpp b/src/DeformConv2d.cpp index 712419d..3d1fad2 100644 --- a/src/DeformConv2d.cpp +++ b/src/DeformConv2d.cpp @@ -25,25 +25,23 @@ void DeformConv2d::initCUDNN() { if (dst_dim % 3 != 0 ) std::cout<<"take attention\n\n"; chunk_dim = dst_dim/3; - checkCuda(cudaMalloc(&offset, 2*chunk_dim*sizeof(dnnType))); - checkCuda(cudaMalloc(&mask, chunk_dim*sizeof(dnnType))); + checkCuda( cudaMalloc(&offset, 2*chunk_dim*sizeof(dnnType))); + checkCuda( cudaMalloc(&mask, chunk_dim*sizeof(dnnType))); // kernel ones - cudaMallocHost(&ones_d1, (height_ones*width_ones)*sizeof(dnnType)); + checkCuda( cudaMalloc(&ones_d1, (height_ones*width_ones)*sizeof(dnnType)) ); float aus1[height_ones*width_ones]; for(int i=0; inet = net; - + this->final = final; if(net != nullptr) { this->input_dim = net->getOutputDim(); this->output_dim = input_dim; diff --git a/src/LayerWgs.cpp b/src/LayerWgs.cpp index 0f623c2..2f7c7fc 100644 --- a/src/LayerWgs.cpp +++ b/src/LayerWgs.cpp @@ -8,12 +8,12 @@ namespace tk { namespace dnn { LayerWgs::LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kl, - std::string fname_weights, bool batchnorm, bool additional_bias) : Layer(net) { + std::string fname_weights, bool batchnorm, bool additional_bias, bool final) : Layer(net, final) { this->inputs = inputs; this->outputs = outputs; this->weights_path = std::string(fname_weights); - + std::cout<<"Reading weights: I="<dontLoadWeights); diff --git a/src/NetworkRT.cpp b/src/NetworkRT.cpp index 4500be5..884a4a3 100644 --- a/src/NetworkRT.cpp +++ b/src/NetworkRT.cpp @@ -75,7 +75,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) { input = Ilay->getOutput(0); input->setName( (l->getLayerName() + std::to_string(i) + "_out").c_str() ); - if(l->getLayerType() == LAYER_YOLO) + if(l->getLayerType() == LAYER_YOLO || l->final) networkRT->markOutput(*input); tensors[l] = input; } @@ -182,6 +182,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) { return convert_layer(input, (Yolo*) l); if(type == LAYER_UPSAMPLE) return convert_layer(input, (Upsample*) l); + if(type == LAYER_DEFORMCONV2D) + return convert_layer(input, (DeformConv2d*) l); std::cout<getLayerName()<<"\n"; FatalError("Layer not implemented in tensorRT"); @@ -254,6 +256,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) { lRTconv->setPadding(DimsHW{l->paddingH, l->paddingW}); lRT = (ILayer*) lRTconv; + Dims d = lRTconv->getOutput(0)->getDimensions(); + std::cout<<"DECONV: "<preconv); + + ITensor **inputs = new ITensor*[2]; + inputs[0] = input; + inputs[1] = preconv->getOutput(0); + + //std::cout<<"New plugin DEFORMABLE\n"; + IPlugin *plugin = new DeformableConvRT(l); + IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin); + checkNULL(lRT); + + // batchnorm + void *bias_b, *power_b, *mean_b, *variance_b, *scales_b; + if(dtRT == DataType::kHALF) { + bias_b = l->bias16_h; + power_b = l->power16_h; + mean_b = l->mean16_h; + variance_b = l->variance16_h; + scales_b = l->scales16_h; + } else { + bias_b = l->bias_h; + power_b = l->power_h; + mean_b = l->mean_h; + variance_b = l->variance_h; + scales_b = l->scales_h; + } + + Weights power{dtRT, power_b, l->outputs}; + Weights shift{dtRT, mean_b, l->outputs}; + Weights scale{dtRT, variance_b, l->outputs}; + std::cout<getNbOutputs()<addScale(*lRT->getOutput(0), ScaleMode::kCHANNEL, + shift, scale, power); + + checkNULL(lRT2); + + Weights shift2{dtRT, bias_b, l->outputs}; + Weights scale2{dtRT, scales_b, l->outputs}; + IScaleLayer *lRT3 = networkRT->addScale(*lRT2->getOutput(0), ScaleMode::kCHANNEL, + shift2, scale2, power); + checkNULL(lRT3); + + return lRT3; +} + bool NetworkRT::serialize(const char *filename) { std::ofstream p(filename); diff --git a/tests/resnet101_cnet/resnet101_cnet.cpp b/tests/resnet101_cnet/resnet101_cnet.cpp index 33d60b8..c89591a 100644 --- a/tests/resnet101_cnet/resnet101_cnet.cpp +++ b/tests/resnet101_cnet/resnet101_cnet.cpp @@ -172,9 +172,9 @@ const char *reg_conv2_bin = "../tests/resnet101_cnet/layers/reg-2.bin"; const char *fc_bin = "../tests/resnet101_cnet/layers/fc.bin"; const char *output_bin[]={ -"../tests/resnet101_cnet/debug/hm.bin", -"../tests/resnet101_cnet/debug/wh.bin", -"../tests/resnet101_cnet/debug/reg.bin"}; + "../tests/resnet101_cnet/debug/hm.bin", + "../tests/resnet101_cnet/debug/wh.bin", + "../tests/resnet101_cnet/debug/reg.bin"}; int main() { @@ -317,17 +317,17 @@ int main() tk::dnn::Layer *route_1_0_layers[1] = { layer2_deconv1_relu }; tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false); tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU); - tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false); + tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false, false, true); tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1); tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false); tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU); - tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false); + tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false, false, true); tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1); tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false); tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU); - tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false); + tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false, false, true); // Load input dnnType *data; @@ -339,7 +339,7 @@ int main() net.print(); //convert network to tensorRT -// tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt"); + tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt"); tk::dnn::dataDim_t dim1 = dim; //input dim @@ -354,7 +354,7 @@ int main() // printDeviceVector(64, cudnn_out, true); -/* tk::dnn::dataDim_t dim2 = dim; + tk::dnn::dataDim_t dim2 = dim; printCenteredTitle(" TENSORRT inference ", '=', 30); { dim2.print(); @@ -363,10 +363,9 @@ int main() TIMER_STOP dim2.print(); } - rt_out = (dnnType *)netRT.buffersRT[1]; -*/ - tk::dnn::Conv2d *outs[3] = { hm, wh, reg }; + tk::dnn::Layer *outs[3] = { hm, wh, reg }; + for(int i=0; i<3; i++) { printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30); @@ -382,14 +381,15 @@ int main() dnnType *cudnn_out, *rt_out; cudnn_out = outs[i]->dstData; + rt_out = (dnnType *)netRT.buffersRT[i+1]; std::cout << "CUDNN vs correct"; checkResult(odim, cudnn_out, out); - /* std::cout << "TRT vs correct"; + std::cout << "TRT vs correct"; checkResult(odim, rt_out, out); std::cout << "CUDNN vs TRT "; - checkResult(odim, cudnn_out, rt_out);*/ + checkResult(odim, cudnn_out, rt_out); } return 0; }