429 lines
22 KiB
C++
429 lines
22 KiB
C++
#include <iostream>
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#include <chrono>
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#include "cuda_runtime_api.h"
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#include "logging.h"
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#include "common.hpp"
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#define USE_FP16 // comment out this if want to use FP32
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#define DEVICE 0 // GPU id
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#define BATCH_SIZE 1
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#define EXPANDRATIO 1.4
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static const int INPUT_H = 640;
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static const int INPUT_W = 640;
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static const int OUTPUT_SIZE = 640*640*2;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "out";
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static Logger gLogger;
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cv::RotatedRect expandBox(const cv::RotatedRect& inBox, float ratio = 1.0)
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{
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cv::Size size = inBox.size;
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int neww = size.width * ratio;
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int newh = size.height *ratio;
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return cv::RotatedRect(inBox.center, cv::Size(neww, newh), inBox.angle);
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}
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// Creat the engine using only the API and not any parser.
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ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("E:\\LearningCodes\\DBNET\\DBNet.pytorch\\tools\\DBNet.wts");
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Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
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/* ------ Resnet18 backbone------ */
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// Add convolution layer with 6 outputs and a 5x5 filter.
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IConvolutionLayer* conv1 = network->addConvolution(*data, 64, DimsHW{ 7, 7 }, weightMap["backbone.conv1.weight"], emptywts);
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assert(conv1);
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conv1->setStride(DimsHW{ 2, 2 });
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conv1->setPadding(DimsHW{ 3, 3 });
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "backbone.bn1", 1e-5);
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// Add activation layer using the ReLU algorithm.
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IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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// Add max pooling layer with stride of 2x2 and kernel size of 2x2.
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IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{ 3, 3 });
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assert(pool1);
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pool1->setStride(DimsHW{ 2, 2 });
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pool1->setPadding(DimsHW{ 1, 1 });
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IActivationLayer* relu2 = basicBlock(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "backbone.layer1.0.");
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IActivationLayer* relu3 = basicBlock(network, weightMap, *relu2->getOutput(0), 64, 64, 1, "backbone.layer1.1."); // x2
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IActivationLayer* relu4 = basicBlock(network, weightMap, *relu3->getOutput(0), 64, 128, 2, "backbone.layer2.0.");
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IActivationLayer* relu5 = basicBlock(network, weightMap, *relu4->getOutput(0), 128, 128, 1, "backbone.layer2.1."); // x3
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IActivationLayer* relu6 = basicBlock(network, weightMap, *relu5->getOutput(0), 128, 256, 2, "backbone.layer3.0.");
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IActivationLayer* relu7 = basicBlock(network, weightMap, *relu6->getOutput(0), 256, 256, 1, "backbone.layer3.1."); //x4
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IActivationLayer* relu8 = basicBlock(network, weightMap, *relu7->getOutput(0), 256, 512, 2, "backbone.layer4.0.");
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IActivationLayer* relu9 = basicBlock(network, weightMap, *relu8->getOutput(0), 512, 512, 1, "backbone.layer4.1."); //x5
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/* ------- FPN neck ------- */
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// net weight input,outch, ksize, s, g, std::string lname
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// 1
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auto p5 = convBnLeaky(network, weightMap, *relu9->getOutput(0), 64, 1, 1, 1, "neck.reduce_conv_c5"); // k=1 s = 1 p = k/2=1/2=0
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auto c4_1 = convBnLeaky(network, weightMap, *relu7->getOutput(0), 64, 1, 1, 1, "neck.reduce_conv_c4");
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float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 64 * 2 * 2));
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for (int i = 0; i < 64 * 2 * 2; i++) {
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deval[i] = 1.0;
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}
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Weights deconvwts1{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* p4_1 = network->addDeconvolutionNd(*p5->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts1, emptywts);
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p4_1->setStrideNd(DimsHW{ 2, 2 });
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p4_1->setNbGroups(64);
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weightMap["deconv1"] = deconvwts1;
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auto p4_add = network->addElementWise(*p4_1->getOutput(0), *c4_1->getOutput(0), ElementWiseOperation::kSUM);
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auto p4 = convBnLeaky(network, weightMap, *p4_add->getOutput(0), 64, 3, 1, 1, "neck.smooth_p4"); // smooth
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// 2
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auto c3_1 = convBnLeaky(network, weightMap, *relu5->getOutput(0), 64, 1, 1, 1, "neck.reduce_conv_c3");
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Weights deconvwts2{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* p3_1 = network->addDeconvolutionNd(*p4->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts2, emptywts);
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p3_1->setStrideNd(DimsHW{ 2, 2 });
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p3_1->setNbGroups(64);
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auto p3_add = network->addElementWise(*p3_1->getOutput(0), *c3_1->getOutput(0), ElementWiseOperation::kSUM);
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auto p3 = convBnLeaky(network, weightMap, *p3_add->getOutput(0), 64, 3, 1, 1, "neck.smooth_p3"); // smooth
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// 3
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auto c2_1 = convBnLeaky(network, weightMap, *relu3->getOutput(0), 64, 1, 1, 1, "neck.reduce_conv_c2");
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Weights deconvwts3{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* p2_1 = network->addDeconvolutionNd(*p3->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts3, emptywts);
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p2_1->setStrideNd(DimsHW{ 2, 2 });
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p2_1->setNbGroups(64);
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//Dims p2_1dim = p2_1->getOutput(0)->getDimensions();
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auto p2_add = network->addElementWise(*p2_1->getOutput(0), *c2_1->getOutput(0), ElementWiseOperation::kSUM);
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auto p2 = convBnLeaky(network, weightMap, *p2_add->getOutput(0), 64, 3, 1, 1, "neck.smooth_p2"); // smooth
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// _upsample_cat
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// p3--p2 (upx2 w p s=2 0 2)
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Weights deconvwts4{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* p3_up_p2 = network->addDeconvolutionNd(*p3->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts4, emptywts);
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p3_up_p2->setStrideNd(DimsHW{ 2, 2 });
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p3_up_p2->setNbGroups(64);
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// p4--p2(upx4 wps=824)
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float *deval2 = reinterpret_cast<float*>(malloc(sizeof(float) * 64 * 8 * 8));
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for (int i = 0; i < 64 * 8 * 8; i++) {
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deval2[i] = 1.0;
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}
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Weights deconvwts5{ DataType::kFLOAT, deval2, 64 * 8 * 8 };
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IDeconvolutionLayer* p4_up_p2 = network->addDeconvolutionNd(*p4->getOutput(0), 64, DimsHW{ 8, 8 }, deconvwts5, emptywts);
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p4_up_p2->setPadding(DimsHW{ 2, 2 });
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p4_up_p2->setStrideNd(DimsHW{ 4, 4 });
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p4_up_p2->setNbGroups(64);
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weightMap["deconv2"] = deconvwts5;
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// p5--p2(upx8) wps =808
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Weights deconvwts6{ DataType::kFLOAT, deval2, 64 * 8 * 8 };
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IDeconvolutionLayer* p5_up_p2 = network->addDeconvolutionNd(*p5->getOutput(0), 64, DimsHW{ 8, 8 }, deconvwts6, emptywts);
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p5_up_p2->setStrideNd(DimsHW{ 8, 8 });
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p5_up_p2->setNbGroups(64);
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// torch.cat([p2, p3, p4, p5], dim=1)
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//Dims p2dim = p2->getOutput(0)->getDimensions();
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//Dims p3dim = p3_up_p2->getOutput(0)->getDimensions();
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//Dims p4dim = p4_up_p2->getOutput(0)->getDimensions();
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//Dims p5dim = p5_up_p2->getOutput(0)->getDimensions();
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ITensor* inputTensors[] = { p2->getOutput(0), p3_up_p2->getOutput(0), p4_up_p2->getOutput(0), p5_up_p2->getOutput(0) };
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auto neck_cat = network->addConcatenation(inputTensors, 4);
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//Dims neck_catdim = neck_cat->getOutput(0)->getDimensions();
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ILayer* neck_out = convBnLeaky2(network, weightMap, *neck_cat->getOutput(0), 256, 3, 1, 1, "neck.conv"); // smooth
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assert(neck_out);
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//Dims neck_outdim = neck_out->getOutput(0)->getDimensions();
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/* ------- head ------- */
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// shrink_maps = self.binarize(x)
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// net weight input,outch, ksize, s, g, std::string lname
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auto binarize1 = convBnLeaky2(network, weightMap, *neck_out->getOutput(0), 64, 3, 1, 1, "head.binarize"); //
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Weights deconvwts7{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* binarizeup = network->addDeconvolutionNd(*binarize1->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts7, emptywts);
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binarizeup->setStrideNd(DimsHW{ 2, 2 });
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binarizeup->setNbGroups(64);
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IScaleLayer* binarizebn1 = addBatchNorm2d(network, weightMap, *binarizeup->getOutput(0), "head.binarize.4", 1e-5);
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IActivationLayer* binarizerelu1 = network->addActivation(*binarizebn1->getOutput(0), ActivationType::kRELU);
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assert(binarizerelu1);
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Weights deconvwts8{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* binarizeup2 = network->addDeconvolutionNd(*binarizerelu1->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts8, emptywts);
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binarizeup2->setStrideNd(DimsHW{ 2, 2 });
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binarizeup2->setNbGroups(64);
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IConvolutionLayer* binarize3 = network->addConvolution(*binarizeup2->getOutput(0), 1, DimsHW{ 3, 3 }, weightMap["head.binarize.7.weight"], weightMap["head.binarize.7.bias"]);
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assert(binarize3);
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binarize3->setStride(DimsHW{ 1, 1 });
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binarize3->setPadding(DimsHW{ 1, 1 });
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IActivationLayer* binarize4 = network->addActivation(*binarize3->getOutput(0), ActivationType::kSIGMOID);
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assert(binarize4);
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//threshold_maps = self.thresh(x)
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auto thresh1 = convBnLeaky2(network, weightMap, *neck_out->getOutput(0), 64, 3, 1, 1, "head.thresh", false); //
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Weights deconvwts9{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* threshup = network->addDeconvolutionNd(*thresh1->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts9, emptywts);
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threshup->setStrideNd(DimsHW{ 2, 2 });
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threshup->setNbGroups(64);
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IConvolutionLayer* thresh2 = network->addConvolution(*threshup->getOutput(0), 64, DimsHW{ 3, 3 }, weightMap["head.thresh.3.1.weight"], weightMap["head.thresh.3.1.bias"]);
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assert(thresh2);
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thresh2->setStride(DimsHW{ 1, 1 });
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thresh2->setPadding(DimsHW{ 1, 1 });
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IScaleLayer* threshbn1 = addBatchNorm2d(network, weightMap, *thresh2->getOutput(0), "head.thresh.4", 1e-5);
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IActivationLayer* threshrelu1 = network->addActivation(*threshbn1->getOutput(0), ActivationType::kRELU);
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assert(threshrelu1);
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Weights deconvwts10{ DataType::kFLOAT, deval, 64 * 2 * 2 };
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IDeconvolutionLayer* threshup2 = network->addDeconvolutionNd(*threshrelu1->getOutput(0), 64, DimsHW{ 2, 2 }, deconvwts10, emptywts);
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threshup2->setStrideNd(DimsHW{ 2, 2 });
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threshup2->setNbGroups(64);
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IConvolutionLayer* thresh3 = network->addConvolution(*threshup2->getOutput(0), 1, DimsHW{ 3, 3 }, weightMap["head.thresh.6.1.weight"], weightMap["head.thresh.6.1.bias"]);
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assert(thresh3);
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thresh3->setStride(DimsHW{ 1, 1 });
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thresh3->setPadding(DimsHW{ 1, 1 });
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IActivationLayer* thresh4 = network->addActivation(*thresh3->getOutput(0), ActivationType::kSIGMOID);
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assert(thresh4);
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//y = torch.cat((shrink_maps, threshold_maps), dim=1)
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// binarize4 thresh4
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//Dims binarize4dim = binarize4->getOutput(0)->getDimensions();
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//Dims thresh4dim = thresh4->getOutput(0)->getDimensions();
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ITensor* inputTensors2[] = { binarize4->getOutput(0), thresh4->getOutput(0)};
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auto head_out = network->addConcatenation(inputTensors2, 2);
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// y = F.interpolate(y, size=(H, W)) # 使用最近邻训练的可以用TRTAPI实现
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// 最后大小为图片大小
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head_out->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*head_out->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#endif
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std::cout << "Building engine, please wait for a while..." << std::endl;
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ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "Build engine successfully!" << std::endl;
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto& mem : weightMap)
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{
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free((void*)(mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
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// Create builder
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
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//ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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builder->destroy();
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}
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void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
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const ICudaEngine& engine = context.getEngine();
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// Pointers to input and output device buffers to pass to engine.
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// Engine requires exactly IEngine::getNbBindings() number of buffers.
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assert(engine.getNbBindings() == 2);
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void* buffers[2];
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// In order to bind the buffers, we need to know the names of the input and output tensors.
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// Note that indices are guaranteed to be less than IEngine::getNbBindings()
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const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
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// Create GPU buffers on device
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CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
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CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
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// Create stream
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cudaStream_t stream;
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CHECK(cudaStreamCreate(&stream));
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// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
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CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
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context.enqueue(batchSize, buffers, stream, nullptr);
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CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CHECK(cudaFree(buffers[inputIndex]));
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CHECK(cudaFree(buffers[outputIndex]));
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}
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int main(int argc, char** argv)
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{
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cudaSetDevice(DEVICE);
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// create a model using the API directly and serialize it to a stream
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char *trtModelStream{ nullptr };
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size_t size{ 0 };
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if (argc == 2 && std::string(argv[1]) == "-s")
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{
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IHostMemory* modelStream{ nullptr };
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APIToModel(BATCH_SIZE, &modelStream);
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assert(modelStream != nullptr);
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std::ofstream p("DBNet.engine", std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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return 0;
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}
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else if (argc == 3 && std::string(argv[1]) == "-d")
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{
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std::ifstream file("DBNet.engine", std::ios::binary);
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if (file.good()) {
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file.seekg(0, file.end);
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size = file.tellg();
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file.seekg(0, file.beg);
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trtModelStream = new char[size];
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assert(trtModelStream);
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file.read(trtModelStream, size);
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file.close();
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}
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}
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else
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{
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std::cerr << "arguments not right!" << std::endl;
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std::cerr << "./debnet -s // serialize model to plan file" << std::endl;
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std::cerr << "./debnet -d ../samples // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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// prepare input data ---------------------------
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static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
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//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
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// data[i] = 1.0;
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static float prob[BATCH_SIZE * OUTPUT_SIZE];
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IRuntime* runtime = createInferRuntime(gLogger);
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assert(runtime != nullptr);
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ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
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assert(engine != nullptr);
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IExecutionContext* context = engine->createExecutionContext();
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assert(context != nullptr);
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delete[] trtModelStream;
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std::vector<std::string> file_names;
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if (read_files_in_dir(argv[2], file_names) < 0) {
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std::cout << "read_files_in_dir failed." << std::endl;
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return -1;
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}
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/*
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std::vector<float> mean_value{0.406, 0.456, 0.485};
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std::vector<float> std_value{0.225, 0.224, 0.229};
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cv::Mat src, dst;
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std::vector<cv::Mat> bgrChannels(3);
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cv::split(src, bgrChannels);
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for (auto i = 0; i < bgrChannels.size(); i++)
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{
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bgrChannels[i].convertTo(bgrChannels[i], CV_32FC1, 1.0 / std_value[i], (0.0 - mean_value[i]) / std_value[i]);
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}
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cv::meger(bgrChannels, dst);
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*/
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std::vector<float> mean_value{ 0.406, 0.456, 0.485 }; // BGR
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||
std::vector<float> std_value{ 0.225, 0.224, 0.229 };
|
||
int fcount = 0;
|
||
for (int f = 0; f < (int)file_names.size(); f++) {
|
||
fcount++;
|
||
if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
|
||
for (int b = 0; b < fcount; b++) {
|
||
//cv::Mat img = cv::imread(file_names[f - fcount + 1 + b]);
|
||
cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
|
||
if (img.empty()) continue;
|
||
cv::Mat pr_img; // letterbox BGR to RGB
|
||
cv::resize(img, pr_img, cv::Size(INPUT_W, INPUT_H), cv::INTER_LINEAR);
|
||
int i = 0;
|
||
for (int row = 0; row < INPUT_H; ++row) {
|
||
uchar* uc_pixel = pr_img.data + row * pr_img.step;
|
||
for (int col = 0; col < INPUT_W; ++col) {
|
||
data[b * 3 * INPUT_H * INPUT_W + i] = (uc_pixel[2]/255.0 - mean_value[2]) / std_value[2];
|
||
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (uc_pixel[1]/255.0 - mean_value[1]) / std_value[1];
|
||
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (uc_pixel[0]/255.0 - mean_value[0]) / std_value[0];
|
||
uc_pixel += 3;
|
||
++i;
|
||
}
|
||
}
|
||
}
|
||
|
||
// Run inference
|
||
auto start = std::chrono::system_clock::now();
|
||
doInference(*context, data, prob, BATCH_SIZE);
|
||
auto end = std::chrono::system_clock::now();
|
||
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
||
|
||
// prob 为 2* 640*640 拿出第一个
|
||
cv::Mat map = cv::Mat::zeros(cv::Size(640, 640), CV_8UC1);
|
||
for (int b = 0; b < fcount; b++)
|
||
{
|
||
cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
|
||
cv::resize(img, img, cv::Size(INPUT_W, INPUT_H), cv::INTER_LINEAR);
|
||
for (int h = 0; h < INPUT_H; ++h)
|
||
{
|
||
uchar *ptr = map.ptr(h);
|
||
for (int w = 0; w < INPUT_W; ++w)
|
||
{
|
||
ptr[w] = (prob[b*OUTPUT_SIZE + h*INPUT_W + w] > 0.3) ? 255 : 0;
|
||
}
|
||
}
|
||
// 提取最小外接矩形
|
||
std::vector<std::vector<cv::Point>> contours;
|
||
std::vector<cv::Vec4i> hierarcy;
|
||
cv::findContours(map, contours, hierarcy, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
|
||
|
||
std::vector<cv::Rect> boundRect(contours.size());
|
||
std::vector<cv::RotatedRect> box(contours.size());
|
||
cv::Point2f rect[4];
|
||
for (int i = 0; i < contours.size(); i++)
|
||
{
|
||
box[i] = cv::minAreaRect(cv::Mat(contours[i]));
|
||
//boundRect[i] = cv::boundingRect(cv::Mat(contours[i]));
|
||
//绘制外接矩形和 最小外接矩形（for循环）
|
||
//cv::rectangle(img, cv::Point(boundRect[i].x, boundRect[i].y), cv::Point(boundRect[i].x + boundRect[i].width, boundRect[i].y + boundRect[i].height), cv::Scalar(0, 255, 0), 2, 8);
|
||
cv::RotatedRect expandbox = expandBox(box[i], EXPANDRATIO);
|
||
expandbox.points(rect);//把最小外接矩形四个端点复制给rect数组
|
||
for (int j = 0; j < 4; j++)
|
||
{
|
||
cv::line(img, rect[j], rect[(j + 1) % 4], cv::Scalar(0, 0, 255), 2, 8);
|
||
}
|
||
}
|
||
|
||
cv::imshow("result", img);
|
||
cv::waitKey(0);
|
||
}
|
||
return 0;
|
||
}
|
||
} |