yolov6 6.0 Modification and optimization (#764)
* Add files via upload * check_file * remove invalid data * remove data * Optimization and modification add all yolo model Co-authored-by: Wang Xinyu <wangxinyu_es@163.com>
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xupengao
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@ -283,6 +283,26 @@ ILayer* SPPF(INetworkDefinition *network, std::map<std::string, Weights>& weight
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// SPPF
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ILayer* SPPF(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, int k, std::string lname) {
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int c_ = c1 / 2;
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auto cv1 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
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auto pool1 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
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pool1->setPaddingNd(DimsHW{ k / 2, k / 2 });
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pool1->setStrideNd(DimsHW{ 1, 1 });
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auto pool2 = network->addPoolingNd(*pool1->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
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pool2->setPaddingNd(DimsHW{ k / 2, k / 2 });
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pool2->setStrideNd(DimsHW{ 1, 1 });
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auto pool3 = network->addPoolingNd(*pool2->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
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pool3->setPaddingNd(DimsHW{ k / 2, k / 2 });
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pool3->setStrideNd(DimsHW{ 1, 1 });
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ITensor* inputTensors[] = { cv1->getOutput(0), pool1->getOutput(0), pool2->getOutput(0), pool3->getOutput(0) };
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auto cat = network->addConcatenation(inputTensors, 4);
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auto cv2 = convBlock(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2");
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return cv2;
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}
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std::vector<std::vector<float>> getAnchors(std::map<std::string, Weights>& weightMap, std::string lname) {
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std::vector<std::vector<float>> anchors;
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Weights wts = weightMap[lname + ".anchor_grid"];
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@ -1,425 +1,444 @@
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#include <iostream>
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#include <chrono>
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#include <cmath>
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#include "cuda_utils.h"
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#include "logging.h"
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#include "common.hpp"
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#include "utils.h"
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#include "calibrator.h"
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#include <typeinfo>
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#define USE_FP32 // set USE_INT8 or USE_FP16 or USE_FP32
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#define DEVICE 0 // GPU id
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#define NMS_THRESH 0.4
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#define CONF_THRESH 0.5
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#define BATCH_SIZE 1
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = Yolo::INPUT_H;
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static const int INPUT_W = Yolo::INPUT_W;
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static const int CLASS_NUM = Yolo::CLASS_NUM;
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static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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static Logger gLogger;
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static int get_width(int x, float gw, int divisor = 8) {
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return int(ceil((x * gw) / divisor)) * divisor;
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}
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static int get_depth(int x, float gd) {
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if (x == 1) return 1;
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int r = round(x * gd);
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if (x * gd - int(x * gd) == 0.5 && (int(x * gd) % 2) == 0) {
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--r;
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}
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return std::max<int>(r, 1);
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}
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ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
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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(wts_name);
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/* ------ yolov5 backbone------ */
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auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
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assert(conv0);
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auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
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auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
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auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
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auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
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auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
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auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
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auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
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auto bottleneck_csp8 = C3(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.8");
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auto spp9 = SPPF(network, weightMap, *bottleneck_csp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.9");
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/* ------ yolov5 head ------ */
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auto conv10 = convBlock(network, weightMap, *spp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
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auto upsample11 = network->addResize(*conv10->getOutput(0));
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assert(upsample11);
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upsample11->setResizeMode(ResizeMode::kNEAREST);
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upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());
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ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
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auto cat12 = network->addConcatenation(inputTensors12, 2);
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auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");
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auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");
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auto upsample15 = network->addResize(*conv14->getOutput(0));
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assert(upsample15);
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upsample15->setResizeMode(ResizeMode::kNEAREST);
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upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());
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ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
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auto cat16 = network->addConcatenation(inputTensors16, 2);
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auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");
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/* ------ detect ------ */
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IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
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auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
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ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };
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auto cat19 = network->addConcatenation(inputTensors19, 2);
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auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");
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IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);
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auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");
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ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };
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auto cat22 = network->addConcatenation(inputTensors22, 2);
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auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
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IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);
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auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
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yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*yolo->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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#if defined(USE_FP16)
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config->setFlag(BuilderFlag::kFP16);
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#elif defined(USE_INT8)
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std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
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assert(builder->platformHasFastInt8());
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config->setFlag(BuilderFlag::kINT8);
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Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
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config->setInt8Calibrator(calibrator);
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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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//v6.0
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ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
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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(wts_name);
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/* ------ yolov5 backbone------ */
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auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
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auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
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auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
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auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
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auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
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auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
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auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
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auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
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auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
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auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
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auto c3_10 = C3(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.10");
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auto sppf11 = SPPF(network, weightMap, *c3_10->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.11");
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/* ------ yolov5 head ------ */
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auto conv12 = convBlock(network, weightMap, *sppf11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
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auto upsample13 = network->addResize(*conv12->getOutput(0));
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assert(upsample13);
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upsample13->setResizeMode(ResizeMode::kNEAREST);
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upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
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ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
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auto cat14 = network->addConcatenation(inputTensors14, 2);
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auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");
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auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
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auto upsample17 = network->addResize(*conv16->getOutput(0));
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assert(upsample17);
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upsample17->setResizeMode(ResizeMode::kNEAREST);
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upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());
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ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
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auto cat18 = network->addConcatenation(inputTensors18, 2);
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auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");
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auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
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auto upsample21 = network->addResize(*conv20->getOutput(0));
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assert(upsample21);
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upsample21->setResizeMode(ResizeMode::kNEAREST);
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upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());
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ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
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auto cat22 = network->addConcatenation(inputTensors21, 2);
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auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
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auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
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ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
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auto cat25 = network->addConcatenation(inputTensors25, 2);
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auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");
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auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
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ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
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auto cat28 = network->addConcatenation(inputTensors28, 2);
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auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");
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auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
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ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
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auto cat31 = network->addConcatenation(inputTensors31, 2);
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auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");
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/* ------ detect ------ */
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IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
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IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
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IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);
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IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);
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auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});
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yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*yolo->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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#if defined(USE_FP16)
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config->setFlag(BuilderFlag::kFP16);
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#elif defined(USE_INT8)
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std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
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assert(builder->platformHasFastInt8());
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config->setFlag(BuilderFlag::kINT8);
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Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
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config->setInt8Calibrator(calibrator);
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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, bool& is_p6, float& gd, float& gw, std::string& wts_name) {
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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 = nullptr;
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if (is_p6) {
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engine = build_engine_p6(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
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} else {
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engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
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}
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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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config->destroy();
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}
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||||
|
||||
void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
}
|
||||
|
||||
bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw, std::string& img_dir) {
|
||||
if (argc < 4) return false;
|
||||
if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
|
||||
wts = std::string(argv[2]);
|
||||
engine = std::string(argv[3]);
|
||||
auto net = std::string(argv[4]);
|
||||
if (net[0] == 's') {
|
||||
gd = 0.33;
|
||||
gw = 0.50;
|
||||
} else if (net[0] == 'm') {
|
||||
gd = 0.67;
|
||||
gw = 0.75;
|
||||
} else if (net[0] == 'l') {
|
||||
gd = 1.0;
|
||||
gw = 1.0;
|
||||
} else if (net[0] == 'x') {
|
||||
gd = 1.33;
|
||||
gw = 1.25;
|
||||
} else if (net[0] == 'c' && argc == 7) {
|
||||
gd = atof(argv[5]);
|
||||
gw = atof(argv[6]);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
if (net.size() == 2 && net[1] == '6') {
|
||||
is_p6 = true;
|
||||
}
|
||||
} else if (std::string(argv[1]) == "-d" && argc == 4) {
|
||||
engine = std::string(argv[2]);
|
||||
img_dir = std::string(argv[3]);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
cudaSetDevice(DEVICE);
|
||||
|
||||
std::string wts_name = "";
|
||||
std::string engine_name = "";
|
||||
bool is_p6 = false;
|
||||
float gd = 0.0f, gw = 0.0f;
|
||||
std::string img_dir;
|
||||
if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./yolov5 -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
if (!wts_name.empty()) {
|
||||
IHostMemory* modelStream{ nullptr };
|
||||
APIToModel(BATCH_SIZE, &modelStream, is_p6, gd, gw, wts_name);
|
||||
assert(modelStream != nullptr);
|
||||
std::ofstream p(engine_name, std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 0;
|
||||
}
|
||||
|
||||
// deserialize the .engine and run inference
|
||||
std::ifstream file(engine_name, std::ios::binary);
|
||||
if (!file.good()) {
|
||||
std::cerr << "read " << engine_name << " error!" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
char *trtModelStream = nullptr;
|
||||
size_t size = 0;
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
|
||||
std::vector<std::string> file_names;
|
||||
if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
|
||||
std::cerr << "read_files_in_dir failed." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// prepare input data ---------------------------
|
||||
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
|
||||
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
||||
// data[i] = 1.0;
|
||||
static float prob[BATCH_SIZE * OUTPUT_SIZE];
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
assert(engine->getNbBindings() == 2);
|
||||
void* buffers[2];
|
||||
// In order to bind the buffers, we need to know the names of the input and output tensors.
|
||||
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
|
||||
const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
assert(inputIndex == 0);
|
||||
assert(outputIndex == 1);
|
||||
// Create GPU buffers on device
|
||||
CUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CUDA_CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CUDA_CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
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(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
if (img.empty()) continue;
|
||||
cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox BGR to RGB
|
||||
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] = (float)uc_pixel[2] / 255.0;
|
||||
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
|
||||
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
|
||||
uc_pixel += 3;
|
||||
++i;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, stream, buffers, 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;
|
||||
std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
auto& res = batch_res[b];
|
||||
nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);
|
||||
}
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
auto& res = batch_res[b];
|
||||
//std::cout << res.size() << std::endl;
|
||||
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
for (size_t j = 0; j < res.size(); j++) {
|
||||
cv::Rect r = get_rect(img, res[j].bbox);
|
||||
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
|
||||
cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
|
||||
}
|
||||
cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
|
||||
}
|
||||
fcount = 0;
|
||||
}
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CUDA_CHECK(cudaFree(buffers[inputIndex]));
|
||||
CUDA_CHECK(cudaFree(buffers[outputIndex]));
|
||||
// Destroy the engine
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
// Print histogram of the output distribution
|
||||
//std::cout << "\nOutput:\n\n";
|
||||
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
//{
|
||||
// std::cout << prob[i] << ", ";
|
||||
// if (i % 10 == 0) std::cout << std::endl;
|
||||
//}
|
||||
//std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
#include <iostream>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include "cuda_utils.h"
|
||||
#include "logging.h"
|
||||
#include "common.hpp"
|
||||
#include "utils.h"
|
||||
#include "calibrator.h"
|
||||
|
||||
#define USE_FP32 // set USE_INT8 or USE_FP16 or USE_FP32
|
||||
#define DEVICE 0 // GPU id
|
||||
#define NMS_THRESH 0.4
|
||||
#define CONF_THRESH 0.5
|
||||
#define BATCH_SIZE 1
|
||||
|
||||
// stuff we know about the network and the input/output blobs
|
||||
static const int INPUT_H = Yolo::INPUT_H;
|
||||
static const int INPUT_W = Yolo::INPUT_W;
|
||||
static const int CLASS_NUM = Yolo::CLASS_NUM;
|
||||
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
|
||||
const char* INPUT_BLOB_NAME = "data";
|
||||
const char* OUTPUT_BLOB_NAME = "prob";
|
||||
static Logger gLogger;
|
||||
|
||||
static int get_width(int x, float gw, int divisor = 8) {
|
||||
return int(ceil((x * gw) / divisor)) * divisor;
|
||||
}
|
||||
|
||||
static int get_depth(int x, float gd) {
|
||||
if (x == 1) return 1;
|
||||
int r = round(x * gd);
|
||||
if (x * gd - int(x * gd) == 0.5 && (int(x * gd) % 2) == 0) {
|
||||
--r;
|
||||
}
|
||||
return std::max<int>(r, 1);
|
||||
}
|
||||
|
||||
|
||||
|
||||
ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
|
||||
assert(data);
|
||||
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
|
||||
/* ------ yolov5 backbone------ */
|
||||
auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
|
||||
assert(conv0);
|
||||
auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
|
||||
auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
|
||||
auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
|
||||
auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
|
||||
auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
|
||||
auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
|
||||
auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
|
||||
auto bottleneck_csp8 = C3(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.8");
|
||||
auto spp9 = SPPF(network, weightMap, *bottleneck_csp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.9");
|
||||
/* ------ yolov5 head ------ */
|
||||
auto conv10 = convBlock(network, weightMap, *spp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
|
||||
|
||||
auto upsample11 = network->addResize(*conv10->getOutput(0));
|
||||
assert(upsample11);
|
||||
upsample11->setResizeMode(ResizeMode::kNEAREST);
|
||||
upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());
|
||||
|
||||
ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
|
||||
auto cat12 = network->addConcatenation(inputTensors12, 2);
|
||||
auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");
|
||||
auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");
|
||||
|
||||
auto upsample15 = network->addResize(*conv14->getOutput(0));
|
||||
assert(upsample15);
|
||||
upsample15->setResizeMode(ResizeMode::kNEAREST);
|
||||
upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());
|
||||
|
||||
ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
|
||||
auto cat16 = network->addConcatenation(inputTensors16, 2);
|
||||
|
||||
auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");
|
||||
|
||||
/* ------ detect ------ */
|
||||
IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
|
||||
auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
|
||||
ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };
|
||||
auto cat19 = network->addConcatenation(inputTensors19, 2);
|
||||
auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");
|
||||
IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);
|
||||
auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");
|
||||
ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };
|
||||
auto cat22 = network->addConcatenation(inputTensors22, 2);
|
||||
auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
|
||||
IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);
|
||||
|
||||
auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
|
||||
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*yolo->getOutput(0));
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
|
||||
#if defined(USE_FP16)
|
||||
config->setFlag(BuilderFlag::kFP16);
|
||||
#elif defined(USE_INT8)
|
||||
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
|
||||
assert(builder->platformHasFastInt8());
|
||||
config->setFlag(BuilderFlag::kINT8);
|
||||
Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
|
||||
config->setInt8Calibrator(calibrator);
|
||||
#endif
|
||||
|
||||
std::cout << "Building engine, please wait for a while..." << std::endl;
|
||||
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
|
||||
std::cout << "Build engine successfully!" << std::endl;
|
||||
|
||||
// Don't need the network any more
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
|
||||
assert(data);
|
||||
|
||||
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
|
||||
|
||||
/* ------ yolov5 backbone------ */
|
||||
auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
|
||||
auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
|
||||
auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
|
||||
auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
|
||||
auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
|
||||
auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
|
||||
auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
|
||||
auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
|
||||
auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
|
||||
auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
|
||||
auto c3_10 = C3(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.10");
|
||||
auto sppf11 = SPPF(network, weightMap, *c3_10->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.11");
|
||||
|
||||
/* ------ yolov5 head ------ */
|
||||
auto conv12 = convBlock(network, weightMap, *sppf11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
|
||||
auto upsample13 = network->addResize(*conv12->getOutput(0));
|
||||
assert(upsample13);
|
||||
upsample13->setResizeMode(ResizeMode::kNEAREST);
|
||||
upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
|
||||
ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
|
||||
auto cat14 = network->addConcatenation(inputTensors14, 2);
|
||||
auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");
|
||||
|
||||
auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
|
||||
auto upsample17 = network->addResize(*conv16->getOutput(0));
|
||||
assert(upsample17);
|
||||
upsample17->setResizeMode(ResizeMode::kNEAREST);
|
||||
upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());
|
||||
ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
|
||||
auto cat18 = network->addConcatenation(inputTensors18, 2);
|
||||
auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");
|
||||
|
||||
auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
|
||||
auto upsample21 = network->addResize(*conv20->getOutput(0));
|
||||
assert(upsample21);
|
||||
upsample21->setResizeMode(ResizeMode::kNEAREST);
|
||||
upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());
|
||||
ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
|
||||
auto cat22 = network->addConcatenation(inputTensors21, 2);
|
||||
auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
|
||||
|
||||
auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
|
||||
ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
|
||||
auto cat25 = network->addConcatenation(inputTensors25, 2);
|
||||
auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");
|
||||
|
||||
auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
|
||||
ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
|
||||
auto cat28 = network->addConcatenation(inputTensors28, 2);
|
||||
auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");
|
||||
|
||||
auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
|
||||
ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
|
||||
auto cat31 = network->addConcatenation(inputTensors31, 2);
|
||||
auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");
|
||||
|
||||
/* ------ detect ------ */
|
||||
IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
|
||||
IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
|
||||
IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);
|
||||
IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);
|
||||
|
||||
auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});
|
||||
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*yolo->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
|
||||
#if defined(USE_FP16)
|
||||
config->setFlag(BuilderFlag::kFP16);
|
||||
#elif defined(USE_INT8)
|
||||
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
|
||||
assert(builder->platformHasFastInt8());
|
||||
config->setFlag(BuilderFlag::kINT8);
|
||||
Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
|
||||
config->setInt8Calibrator(calibrator);
|
||||
#endif
|
||||
|
||||
std::cout << "Building engine, please wait for a while..." << std::endl;
|
||||
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
|
||||
std::cout << "Build engine successfully!" << std::endl;
|
||||
|
||||
// Don't need the network any more
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*)(mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, bool& is_p6, float& gd, float& gw, std::string& wts_name) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
|
||||
// Create model to populate the network, then set the outputs and create an engine
|
||||
ICudaEngine *engine = nullptr;
|
||||
if (is_p6) {
|
||||
engine = build_engine_p6(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
|
||||
} else {
|
||||
engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
|
||||
}
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
config->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
}
|
||||
|
||||
bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw, std::string& img_dir) {
|
||||
if (argc < 4) return false;
|
||||
if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
|
||||
wts = std::string(argv[2]);
|
||||
engine = std::string(argv[3]);
|
||||
auto net = std::string(argv[4]);
|
||||
if (net[0] == 'n') {
|
||||
gd = 0.33;
|
||||
gw = 0.25;
|
||||
} else if (net[0] == 's') {
|
||||
gd = 0.33;
|
||||
gw = 0.50;
|
||||
} else if (net[0] == 'm') {
|
||||
gd = 0.67;
|
||||
gw = 0.75;
|
||||
} else if (net[0] == 'l') {
|
||||
gd = 1.0;
|
||||
gw = 1.0;
|
||||
} else if (net[0] == 'x') {
|
||||
gd = 1.33;
|
||||
gw = 1.25;
|
||||
} else if (net[0] == 'c' && argc == 7) {
|
||||
gd = atof(argv[5]);
|
||||
gw = atof(argv[6]);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
if (net.size() == 2 && net[1] == '6') {
|
||||
is_p6 = true;
|
||||
}
|
||||
} else if (std::string(argv[1]) == "-d" && argc == 4) {
|
||||
engine = std::string(argv[2]);
|
||||
img_dir = std::string(argv[3]);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
cudaSetDevice(DEVICE);
|
||||
|
||||
std::string wts_name = "";
|
||||
std::string engine_name = "";
|
||||
bool is_p6 = false;
|
||||
float gd = 0.0f, gw = 0.0f;
|
||||
std::string img_dir;
|
||||
if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./yolov5 -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./yolov5 -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
if (!wts_name.empty()) {
|
||||
IHostMemory* modelStream{ nullptr };
|
||||
APIToModel(BATCH_SIZE, &modelStream, is_p6, gd, gw, wts_name);
|
||||
assert(modelStream != nullptr);
|
||||
std::ofstream p(engine_name, std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 0;
|
||||
}
|
||||
|
||||
// deserialize the .engine and run inference
|
||||
std::ifstream file(engine_name, std::ios::binary);
|
||||
if (!file.good()) {
|
||||
std::cerr << "read " << engine_name << " error!" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
char *trtModelStream = nullptr;
|
||||
size_t size = 0;
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
|
||||
std::vector<std::string> file_names;
|
||||
if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
|
||||
std::cerr << "read_files_in_dir failed." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// prepare input data ---------------------------
|
||||
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
|
||||
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
||||
// data[i] = 1.0;
|
||||
static float prob[BATCH_SIZE * OUTPUT_SIZE];
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
assert(engine->getNbBindings() == 2);
|
||||
void* buffers[2];
|
||||
// In order to bind the buffers, we need to know the names of the input and output tensors.
|
||||
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
|
||||
const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
assert(inputIndex == 0);
|
||||
assert(outputIndex == 1);
|
||||
// Create GPU buffers on device
|
||||
CUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CUDA_CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CUDA_CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
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(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
if (img.empty()) continue;
|
||||
cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox BGR to RGB
|
||||
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] = (float)uc_pixel[2] / 255.0;
|
||||
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
|
||||
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
|
||||
uc_pixel += 3;
|
||||
++i;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, stream, buffers, 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;
|
||||
std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
auto& res = batch_res[b];
|
||||
nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);
|
||||
}
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
auto& res = batch_res[b];
|
||||
//std::cout << res.size() << std::endl;
|
||||
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
for (size_t j = 0; j < res.size(); j++) {
|
||||
cv::Rect r = get_rect(img, res[j].bbox);
|
||||
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
|
||||
cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
|
||||
}
|
||||
cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
|
||||
}
|
||||
fcount = 0;
|
||||
}
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CUDA_CHECK(cudaFree(buffers[inputIndex]));
|
||||
CUDA_CHECK(cudaFree(buffers[outputIndex]));
|
||||
// Destroy the engine
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
// Print histogram of the output distribution
|
||||
//std::cout << "\nOutput:\n\n";
|
||||
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
//{
|
||||
// std::cout << prob[i] << ", ";
|
||||
// if (i % 10 == 0) std::cout << std::endl;
|
||||
//}
|
||||
//std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user