refactor yolov5_det.cpp (#1209)
This commit is contained in:
Wang Xinyu
GitHub
parent
f334a1aa38
commit
57ba75f9a4
@ -1,6 +1,3 @@
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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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@ -8,20 +5,24 @@
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#include "calibrator.h"
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#include "preprocess.h"
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#include <iostream>
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#include <chrono>
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#include <cmath>
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#define USE_FP16 // 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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#define MAX_IMAGE_INPUT_SIZE_THRESH 3000 * 3000 // ensure it exceed the maximum size in the input images !
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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 const int kBatchSize = 1;
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static const int kInputH = Yolo::INPUT_H;
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static const int kInputW = Yolo::INPUT_W;
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static const int kNumClass = Yolo::CLASS_NUM;
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static const int kOutputSize = 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* kInputTensorName = "data";
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const char* kOutputTensorName = "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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@ -37,11 +38,11 @@ static int get_depth(int x, float gd) {
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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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static 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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// Create input tensor of shape {3, kInputH, kInputW} with name kInputTensorName
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ITensor* data = network->addInput(kInputTensorName, dt, Dims3{ 3, kInputH, kInputW });
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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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@ -80,20 +81,20 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
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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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IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (kNumClass + 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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IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (kNumClass + 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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IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (kNumClass + 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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yolo->getOutput(0)->setName(kOutputTensorName);
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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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@ -104,7 +105,7 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
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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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Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, kInputW, kInputH, "./coco_calib/", "int8calib.table", kInputTensorName);
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config->setInt8Calibrator(calibrator);
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#endif
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@ -123,10 +124,10 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
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return engine;
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}
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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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static 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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// Create input tensor of shape {3, kInputH, kInputW} with name kInputTensorName
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ITensor* data = network->addInput(kInputTensorName, dt, Dims3{ 3, kInputH, kInputW });
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights(wts_name);
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@ -189,13 +190,13 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
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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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IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (kNumClass + 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 * (kNumClass + 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 * (kNumClass + 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 * (kNumClass + 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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yolo->getOutput(0)->setName(kOutputTensorName);
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network->markOutput(*yolo->getOutput(0));
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// Build engine
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@ -207,7 +208,7 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
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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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Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, kInputW, kInputH, "./coco_calib/", "int8calib.table", kInputTensorName);
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config->setInt8Calibrator(calibrator);
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#endif
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@ -226,220 +227,238 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
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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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bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw, std::string& img_dir) {
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if (argc < 4) return false;
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if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
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wts = std::string(argv[2]);
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engine = std::string(argv[3]);
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auto net = std::string(argv[4]);
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if (net[0] == 'n') {
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gd = 0.33;
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gw = 0.25;
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} else if (net[0] == 's') {
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gd = 0.33;
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gw = 0.50;
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} else if (net[0] == 'm') {
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gd = 0.67;
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gw = 0.75;
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} else if (net[0] == 'l') {
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gd = 1.0;
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gw = 1.0;
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} else if (net[0] == 'x') {
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gd = 1.33;
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gw = 1.25;
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} else if (net[0] == 'c' && argc == 7) {
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gd = atof(argv[5]);
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gw = atof(argv[6]);
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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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return false;
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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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if (net.size() == 2 && net[1] == '6') {
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is_p6 = true;
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}
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} else if (std::string(argv[1]) == "-d" && argc == 4) {
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engine = std::string(argv[2]);
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img_dir = std::string(argv[3]);
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} else {
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return false;
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}
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return true;
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}
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void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* output, int batchSize) {
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// infer on the batch asynchronously, and DMA output back to host
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context.enqueue(batchSize, buffers, stream, nullptr);
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CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
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void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_output_buffer) {
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assert(engine->getNbBindings() == 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(kInputTensorName);
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const int outputIndex = engine->getBindingIndex(kOutputTensorName);
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assert(inputIndex == 0);
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assert(outputIndex == 1);
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// Create GPU buffers on device
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CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
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CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
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*cpu_output_buffer = new float[kBatchSize * kOutputSize];
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}
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void infer(IExecutionContext& context, cudaStream_t& stream, void** gpu_buffers, float* output, int batchsize) {
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context.enqueue(batchsize, gpu_buffers, stream, nullptr);
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CUDA_CHECK(cudaMemcpyAsync(output, gpu_buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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}
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bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw, std::string& img_dir) {
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if (argc < 4) return false;
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if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
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wts = std::string(argv[2]);
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engine = std::string(argv[3]);
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auto net = std::string(argv[4]);
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if (net[0] == 'n') {
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gd = 0.33;
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gw = 0.25;
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} else if (net[0] == 's') {
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gd = 0.33;
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gw = 0.50;
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} else if (net[0] == 'm') {
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gd = 0.67;
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gw = 0.75;
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} else if (net[0] == 'l') {
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gd = 1.0;
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gw = 1.0;
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} else if (net[0] == 'x') {
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gd = 1.33;
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gw = 1.25;
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} else if (net[0] == 'c' && argc == 7) {
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gd = atof(argv[5]);
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gw = atof(argv[6]);
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} else {
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return false;
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}
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if (net.size() == 2 && net[1] == '6') {
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is_p6 = true;
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}
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} else if (std::string(argv[1]) == "-d" && argc == 4) {
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engine = std::string(argv[2]);
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img_dir = std::string(argv[3]);
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} else {
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return false;
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}
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return true;
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void serialize_engine(unsigned int max_batchsize, bool& is_p6, float& gd, float& gw, std::string& wts_name, std::string& engine_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(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
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} else {
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engine = build_engine(max_batchsize, 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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IHostMemory* serialized_engine = engine->serialize();
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assert(serialized_engine != nullptr);
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// Save engine to file
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std::ofstream p(engine_name, 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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assert(false);
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}
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p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
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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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serialized_engine->destroy();
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}
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void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, IExecutionContext** context) {
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std::ifstream file(engine_name, std::ios::binary);
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if (!file.good()) {
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std::cerr << "read " << engine_name << " error!" << std::endl;
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assert(false);
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}
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size_t size = 0;
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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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char* serialized_engine = new char[size];
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assert(serialized_engine);
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file.read(serialized_engine, size);
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file.close();
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*runtime = createInferRuntime(gLogger);
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assert(*runtime);
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*engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
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assert(*engine);
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*context = (*engine)->createExecutionContext();
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assert(*context);
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delete[] serialized_engine;
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}
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int main(int argc, char** argv) {
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cudaSetDevice(DEVICE);
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cudaSetDevice(DEVICE);
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std::string wts_name = "";
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std::string engine_name = "";
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bool is_p6 = false;
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float gd = 0.0f, gw = 0.0f;
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std::string img_dir;
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if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
|
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std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./yolov5_det -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_det -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
std::string wts_name = "";
|
||||
std::string engine_name = "";
|
||||
bool is_p6 = false;
|
||||
float gd = 0.0f, gw = 0.0f;
|
||||
std::string img_dir;
|
||||
|
||||
// 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;
|
||||
}
|
||||
|
||||
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);
|
||||
float* 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((void**)&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CUDA_CHECK(cudaMalloc((void**)&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
|
||||
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CUDA_CHECK(cudaStreamCreate(&stream));
|
||||
uint8_t* img_host = nullptr;
|
||||
uint8_t* img_device = nullptr;
|
||||
// prepare input data cache in pinned memory
|
||||
CUDA_CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
|
||||
// prepare input data cache in device memory
|
||||
CUDA_CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
|
||||
int fcount = 0;
|
||||
std::vector<cv::Mat> imgs_buffer(BATCH_SIZE);
|
||||
for (int f = 0; f < (int)file_names.size(); f++) {
|
||||
fcount++;
|
||||
if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
|
||||
//auto start = std::chrono::system_clock::now();
|
||||
float *buffer_idx = (float*)buffers[inputIndex];
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
if (img.empty()) continue;
|
||||
imgs_buffer[b] = img;
|
||||
size_t size_image = img.cols * img.rows * 3;
|
||||
size_t size_image_dst = INPUT_H * INPUT_W * 3;
|
||||
//copy data to pinned memory
|
||||
memcpy(img_host, img.data, size_image);
|
||||
//copy data to device memory
|
||||
CUDA_CHECK(cudaMemcpyAsync(img_device, img_host, size_image, cudaMemcpyHostToDevice, stream));
|
||||
preprocess_kernel_img(img_device, img.cols, img.rows, buffer_idx, INPUT_W, INPUT_H, stream);
|
||||
buffer_idx += size_image_dst;
|
||||
cudaStreamSynchronize(stream);
|
||||
}
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, stream, (void**)buffers, prob, BATCH_SIZE);
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << "inference time: " << 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];
|
||||
cv::Mat img = imgs_buffer[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(img_device));
|
||||
CUDA_CHECK(cudaFreeHost(img_host));
|
||||
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;
|
||||
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_det -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_det -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 file
|
||||
if (!wts_name.empty()) {
|
||||
serialize_engine(kBatchSize, is_p6, gd, gw, wts_name, engine_name);
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
// Deserialize the engine from file
|
||||
IRuntime* runtime = nullptr;
|
||||
ICudaEngine* engine = nullptr;
|
||||
IExecutionContext* context = nullptr;
|
||||
deserialize_engine(engine_name, &runtime, &engine, &context);
|
||||
cudaStream_t stream;
|
||||
CUDA_CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
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 cpu and gpu buffers
|
||||
float* gpu_buffers[2];
|
||||
float* cpu_output_buffer = nullptr;
|
||||
prepare_buffers(engine, &gpu_buffers[0], &gpu_buffers[1], &cpu_output_buffer);
|
||||
|
||||
uint8_t* img_host = nullptr;
|
||||
uint8_t* img_device = nullptr;
|
||||
// prepare input data cache in pinned memory
|
||||
CUDA_CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
|
||||
// prepare input data cache in device memory
|
||||
CUDA_CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
|
||||
|
||||
int fcount = 0;
|
||||
std::vector<cv::Mat> imgs_buffer(kBatchSize);
|
||||
for (int f = 0; f < (int)file_names.size(); f++) {
|
||||
fcount++;
|
||||
if (fcount < kBatchSize && f + 1 != (int)file_names.size()) continue;
|
||||
//auto start = std::chrono::system_clock::now();
|
||||
float *buffer_idx = (float*)gpu_buffers[0];
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
|
||||
if (img.empty()) continue;
|
||||
imgs_buffer[b] = img;
|
||||
size_t size_image = img.cols * img.rows * 3;
|
||||
size_t size_image_dst = kInputH * kInputW * 3;
|
||||
//copy data to pinned memory
|
||||
memcpy(img_host, img.data, size_image);
|
||||
//copy data to device memory
|
||||
CUDA_CHECK(cudaMemcpyAsync(img_device, img_host, size_image, cudaMemcpyHostToDevice, stream));
|
||||
preprocess_kernel_img(img_device, img.cols, img.rows, buffer_idx, kInputW, kInputH, stream);
|
||||
buffer_idx += size_image_dst;
|
||||
cudaStreamSynchronize(stream);
|
||||
}
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
infer(*context, stream, (void**)gpu_buffers, cpu_output_buffer, kBatchSize);
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << "inference time: " << 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, &cpu_output_buffer[b * kOutputSize], CONF_THRESH, NMS_THRESH);
|
||||
}
|
||||
for (int b = 0; b < fcount; b++) {
|
||||
auto& res = batch_res[b];
|
||||
cv::Mat img = imgs_buffer[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(img_device));
|
||||
CUDA_CHECK(cudaFreeHost(img_host));
|
||||
CUDA_CHECK(cudaFree(gpu_buffers[0]));
|
||||
CUDA_CHECK(cudaFree(gpu_buffers[1]));
|
||||
delete[] cpu_output_buffer;
|
||||
// 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 < kOutputSize; 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