add csrnet (#1450)
* add csrnet * Add update result jpg CSRNet Inference result * fix pr format * add density plot code and update README.md fix img src --------- Co-authored-by: liulf <liulf@nncsys.com>
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26
csrnet/CMakeLists.txt
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26
csrnet/CMakeLists.txt
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cmake_minimum_required(VERSION 3.10)
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project(csrnet)
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add_definitions(-std=c++11)
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add_definitions(-DAPI_EXPORTS)
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_BUILD_TYPE Debug)
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# cuda
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include_directories(/usr/local/cuda/targets/x86_64-linux/include )
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link_directories(/usr/local/cuda/targets/x86_64-linux/lib)
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# tensorrt
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include_directories(/usr/include/x86_64-linux-gnu/)
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link_directories(/usr/lib/x86_64-linux-gnu/)
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# opencv
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find_package(OpenCV)
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include_directories(${OpenCV_INCLUDE_DIRS})
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include_directories(${PROJECT_SOURCE_DIR}/)
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add_executable(csrnet csrnet.cpp)
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target_link_libraries(csrnet nvinfer cudart ${OpenCV_LIBS})
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58
csrnet/README.md
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csrnet/README.md
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# csrnet
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The Pytorch implementation is [leeyeehoo/CSRNet-pytorch](https://github.com/leeyeehoo/CSRNet-pytorch).
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This repo is a TensorRT implementation of CSRNet.
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paper : [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://arxiv.org/abs/1802.10062)
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Dev environment:
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- Ubuntu 22.04
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- TensorRT 8.6
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- OpenCV 4.5.4
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- CMake 3.24
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- GPU Driver 535.113.01
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- CUDA 12.2
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- RTX3080
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# how to run
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```bash
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1. generate csrnet engine
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git clone https://github.com/leeyeehoo/CSRNet-pytorch.git
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git clone https://github.com/wang-xinyu/tensorrtx.git
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// copy gen_wts.py to CSRNet-pytorch
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// generate wts file
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python gen_wts.py
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// csrnet wts will be generated in CSRNet-pytorch
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2. build csrnet.engine
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// mv CSRNet-pytorch/csrnet.engine to tensorrtx/csrnet
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mv CSRNet-pytorch/csrnet.wts tensorrtx/csrnet
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// build
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mkdir build
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cmake ..
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make
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sudo ./csrnet -s ./csrnet.wts
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Loading weights: ./csrnet.wts
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build engine successfully : ./csrnet.engine
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// download images https://github.com/wang-xinyu/tensorrtx/assets/46584679/46bc4def-e573-44ae-996d-5d68927c78ff and copy to images
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sudo ./csrnet -d ./images
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// output e.g
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// enqueueV2 time: 0.0323869s
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// detect time:44ms
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// people num :22.9101 write_path: ../images/data.jpg
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```
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# result
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inference people num: 22.9101
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<p align="center">
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<img src= https://raw.githubusercontent.com/wang-xinyu/tensorrtx/dbf857d25f77bf64113fc99a745ccf4973bdd44e/Density_Plot.jpg>
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</p>
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16
csrnet/config.h
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csrnet/config.h
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#pragma once
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const static char *kInputTensorName = "data";
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const static char *kOutputTensorName = "prob";
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const static char *kEngineFile = "./csrnet.engine";
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const static int kBatchSize = 1;
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const static int MAX_INPUT_SIZE = 1440; // 32x
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const static int MIN_INPUT_SIZE = 608;
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const static int OPT_INPUT_W = 1152;
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const static int OPT_INPUT_H = 640;
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constexpr static int kMaxInputImageSize = MAX_INPUT_SIZE * MAX_INPUT_SIZE * 3;
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constexpr static int kMaxOutputProbSize =
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(MAX_INPUT_SIZE * MAX_INPUT_SIZE) >> 6;
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536
csrnet/csrnet.cpp
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csrnet/csrnet.cpp
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include <chrono>
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#include <config.h>
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#include <cstring>
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#include <dirent.h>
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#include <fstream>
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#include <iostream>
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#include <logging.h>
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#include <map>
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#include <numeric>
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#include <opencv2/opencv.hpp>
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#include <vector>
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using namespace nvinfer1;
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#define CHECK(status) \
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do { \
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auto ret = (status); \
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if (ret != 0) { \
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std::cerr << "Cuda failure: " << ret << std::endl; \
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abort(); \
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} \
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} while (0)
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static Logger gLogger;
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static char *kWTSFile = "";
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std::map<std::string, Weights> loadWeights(const std::string file) {
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std::cout << "Loading weights: " << file << std::endl;
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std::map<std::string, Weights> weightMap;
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// Open weights file
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std::ifstream input(file);
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assert(input.is_open() && "Unable to load weight file.");
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// Read number of weight blobs
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int32_t count;
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input >> count;
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assert(count > 0 && "Invalid weight map file.");
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while (count--) {
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Weights wt{DataType::kFLOAT, nullptr, 0};
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uint32_t size;
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// Read name and type of blob
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std::string name;
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input >> name >> std::dec >> size;
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wt.type = DataType::kFLOAT;
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// Load blob
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uint32_t *val = reinterpret_cast<uint32_t *>(malloc(sizeof(val) * size));
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for (uint32_t x = 0, y = size; x < y; ++x) {
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input >> std::hex >> val[x];
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}
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wt.values = val;
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wt.count = size;
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weightMap[name] = wt;
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}
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return weightMap;
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}
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// clang-format off
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/*
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CSRNet(
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(frontend): Sequential(
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(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(3): ReLU(inplace=True)
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(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
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(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(6): ReLU(inplace=True)
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(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(8): ReLU(inplace=True)
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(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
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(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(11): ReLU(inplace=True)
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(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(13): ReLU(inplace=True)
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(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(15): ReLU(inplace=True)
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(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
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(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(18): ReLU(inplace=True)
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(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(20): ReLU(inplace=True)
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(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(22): ReLU(inplace=True)
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)
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(backend): Sequential(
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(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2),
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dilation=(2, 2)) (1): ReLU(inplace=True) (2): Conv2d(512, 512,
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kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) (3):
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ReLU(inplace=True) (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1,
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1), padding=(2, 2), dilation=(2, 2)) (5): ReLU(inplace=True) (6):
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Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2),
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dilation=(2, 2)) (7): ReLU(inplace=True) (8): Conv2d(256, 128,
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kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) (9):
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ReLU(inplace=True) (10): Conv2d(128, 64, kernel_size=(3, 3), stride=(1,
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1), padding=(2, 2), dilation=(2, 2)) (11): ReLU(inplace=True)
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)
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(output_layer): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))
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)
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*/
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// clang-format on
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void doInference(IExecutionContext &context, float *input, float *output,
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int input_h, int input_w) {
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const ICudaEngine &engine = context.getEngine();
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uint64_t input_size = 3 * input_h * input_w * sizeof(float);
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uint64_t output_size = ((input_h * input_w) >> 6) * sizeof(float);
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// Pointers to input and output device buffers to pass to engine.
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// Engine requires exactly IEngine::getNbBindings() number of buffers.
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assert(engine.getNbBindings() == 2);
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void *buffers[2];
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// In order to bind the buffers, we need to know the names of the input and
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// output tensors. Note that indices are guaranteed to be less than
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// 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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context.setBindingDimensions(inputIndex, Dims4(1, 3, input_h, input_w));
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// Create GPU buffers on device
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CHECK(cudaMalloc(&buffers[inputIndex], input_size));
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CHECK(cudaMalloc(&buffers[outputIndex], output_size));
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// Create stream
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cudaStream_t stream;
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CHECK(cudaStreamCreate(&stream));
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// DMA input batch data to device, infer on the batch asynchronously, and DMA
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// output back to host
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CHECK(cudaMemcpyAsync(buffers[inputIndex], input, input_size,
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cudaMemcpyHostToDevice, stream));
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auto t1 = std::chrono::high_resolution_clock::now();
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context.enqueueV2(buffers, stream, nullptr);
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std::cout << "enqueueV2 time: "
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<< std::chrono::duration<float>(
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std::chrono::high_resolution_clock::now() - t1)
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.count()
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<< "s" << std::endl;
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CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size,
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cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CHECK(cudaFree(buffers[inputIndex]));
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CHECK(cudaFree(buffers[outputIndex]));
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}
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ICudaEngine *createEngine(unsigned int maxBatchSize, IBuilder *builder,
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IBuilderConfig *config, DataType dt) {
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// INetworkDefinition *network = builder->createNetworkV2(0U);
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const auto explicitBatch =
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1U << static_cast<uint32_t>(
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NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
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INetworkDefinition *network = builder->createNetworkV2(explicitBatch);
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ITensor *data = network->addInput(kInputTensorName, dt, Dims4{1, 3, -1, -1});
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights(kWTSFile);
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IConvolutionLayer *conv1 = network->addConvolutionNd(
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*data, 64, DimsHW{3, 3}, weightMap["frontend.0.weight"],
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weightMap["frontend.0.bias"]);
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assert(conv1);
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conv1->setStrideNd(DimsHW{1, 1});
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conv1->setPaddingNd(DimsHW{1, 1});
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IActivationLayer *relu1 =
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network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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auto conv2 = network->addConvolutionNd(*relu1->getOutput(0), 64, DimsHW{3, 3},
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weightMap["frontend.2.weight"],
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weightMap["frontend.2.bias"]);
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assert(conv2);
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conv2->setStrideNd(DimsHW{1, 1});
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conv2->setPaddingNd(DimsHW{1, 1});
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auto relu2 =
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network->addActivation(*conv2->getOutput(0), ActivationType::kRELU);
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assert(relu2);
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auto pool1 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kMAX,
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DimsHW{2, 2});
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assert(pool1);
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pool1->setStrideNd(DimsHW{2, 2});
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auto conv3 = network->addConvolutionNd(
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*pool1->getOutput(0), 128, DimsHW{3, 3}, weightMap["frontend.5.weight"],
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weightMap["frontend.5.bias"]);
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assert(conv3);
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conv3->setStrideNd(DimsHW{1, 1});
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conv3->setPaddingNd(DimsHW{1, 1});
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auto relu3 =
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network->addActivation(*conv3->getOutput(0), ActivationType::kRELU);
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assert(relu3);
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auto conv4 = network->addConvolutionNd(
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*relu3->getOutput(0), 128, DimsHW{3, 3}, weightMap["frontend.7.weight"],
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weightMap["frontend.7.bias"]);
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assert(conv4);
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conv4->setStrideNd(DimsHW{1, 1});
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conv4->setPaddingNd(DimsHW{1, 1});
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auto relu4 =
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network->addActivation(*conv4->getOutput(0), ActivationType::kRELU);
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assert(relu4);
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auto pool2 = network->addPoolingNd(*relu4->getOutput(0), PoolingType::kMAX,
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DimsHW{2, 2});
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assert(pool2);
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pool2->setStrideNd(DimsHW{2, 2});
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auto conv5 = network->addConvolutionNd(
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*pool2->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.10.weight"],
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weightMap["frontend.10.bias"]);
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assert(conv5);
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conv5->setStrideNd(DimsHW{1, 1});
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conv5->setPaddingNd(DimsHW{1, 1});
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auto relu5 =
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network->addActivation(*conv5->getOutput(0), ActivationType::kRELU);
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assert(relu5);
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auto conv6 = network->addConvolutionNd(
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*relu5->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.12.weight"],
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weightMap["frontend.12.bias"]);
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assert(conv6);
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conv6->setStrideNd(DimsHW{1, 1});
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conv6->setPaddingNd(DimsHW{1, 1});
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auto relu6 =
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network->addActivation(*conv6->getOutput(0), ActivationType::kRELU);
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assert(relu6);
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auto conv7 = network->addConvolutionNd(
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*relu6->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.14.weight"],
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weightMap["frontend.14.bias"]);
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assert(conv7);
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conv7->setStrideNd(DimsHW{1, 1});
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conv7->setPaddingNd(DimsHW{1, 1});
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auto relu7 =
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network->addActivation(*conv7->getOutput(0), ActivationType::kRELU);
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assert(relu7);
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auto pool3 = network->addPoolingNd(*relu7->getOutput(0), PoolingType::kMAX,
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DimsHW{2, 2});
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assert(pool3);
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pool3->setStrideNd(DimsHW{2, 2});
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auto conv8 = network->addConvolutionNd(
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*pool3->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.17.weight"],
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weightMap["frontend.17.bias"]);
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assert(conv8);
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conv8->setStrideNd(DimsHW{1, 1});
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conv8->setPaddingNd(DimsHW{1, 1});
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auto relu8 =
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network->addActivation(*conv8->getOutput(0), ActivationType::kRELU);
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assert(relu8);
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auto conv9 = network->addConvolutionNd(
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*relu8->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.19.weight"],
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weightMap["frontend.19.bias"]);
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assert(conv9);
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conv9->setStrideNd(DimsHW{1, 1});
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conv9->setPaddingNd(DimsHW{1, 1});
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auto relu9 =
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network->addActivation(*conv9->getOutput(0), ActivationType::kRELU);
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assert(relu9);
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auto conv10 = network->addConvolutionNd(
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*relu9->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.21.weight"],
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weightMap["frontend.21.bias"]);
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assert(conv10);
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conv10->setStrideNd(DimsHW{1, 1});
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conv10->setPaddingNd(DimsHW{1, 1});
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auto relu10 =
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network->addActivation(*conv10->getOutput(0), ActivationType::kRELU);
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assert(relu10);
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// backend
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auto conv11 = network->addConvolutionNd(
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*relu10->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.0.weight"],
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weightMap["backend.0.bias"]);
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assert(conv11);
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conv11->setPaddingNd(DimsHW{2, 2});
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conv11->setStrideNd(DimsHW{1, 1});
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conv11->setDilationNd(DimsHW{2, 2});
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auto relu11 =
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network->addActivation(*conv11->getOutput(0), ActivationType::kRELU);
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assert(relu11);
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auto conv12 = network->addConvolutionNd(
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*relu11->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.2.weight"],
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weightMap["backend.2.bias"]);
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assert(conv12);
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conv12->setPaddingNd(DimsHW{2, 2});
|
||||
conv12->setStrideNd(DimsHW{1, 1});
|
||||
conv12->setDilationNd(DimsHW{2, 2});
|
||||
auto relu12 =
|
||||
network->addActivation(*conv12->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu12);
|
||||
|
||||
auto conv13 = network->addConvolutionNd(
|
||||
*relu12->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.4.weight"],
|
||||
weightMap["backend.4.bias"]);
|
||||
assert(conv13);
|
||||
conv13->setPaddingNd(DimsHW{2, 2});
|
||||
conv13->setStrideNd(DimsHW{1, 1});
|
||||
conv13->setDilationNd(DimsHW{2, 2});
|
||||
auto relu13 =
|
||||
network->addActivation(*conv13->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu13);
|
||||
|
||||
auto conv14 = network->addConvolutionNd(
|
||||
*relu13->getOutput(0), 256, DimsHW{3, 3}, weightMap["backend.6.weight"],
|
||||
weightMap["backend.6.bias"]);
|
||||
assert(conv14);
|
||||
conv14->setPaddingNd(DimsHW{2, 2});
|
||||
conv14->setStrideNd(DimsHW{1, 1});
|
||||
conv14->setDilationNd(DimsHW{2, 2});
|
||||
auto relu14 =
|
||||
network->addActivation(*conv14->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu14);
|
||||
auto conv15 = network->addConvolutionNd(
|
||||
*relu14->getOutput(0), 128, DimsHW{3, 3}, weightMap["backend.8.weight"],
|
||||
weightMap["backend.8.bias"]);
|
||||
assert(conv15);
|
||||
conv15->setPaddingNd(DimsHW{2, 2});
|
||||
conv15->setStrideNd(DimsHW{1, 1});
|
||||
conv15->setDilationNd(DimsHW{2, 2});
|
||||
auto relu15 =
|
||||
network->addActivation(*conv15->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu15);
|
||||
auto conv16 = network->addConvolutionNd(
|
||||
*relu15->getOutput(0), 64, DimsHW{3, 3}, weightMap["backend.10.weight"],
|
||||
weightMap["backend.10.bias"]);
|
||||
assert(conv16);
|
||||
conv16->setPaddingNd(DimsHW{2, 2});
|
||||
conv16->setStrideNd(DimsHW{1, 1});
|
||||
conv16->setDilationNd(DimsHW{2, 2});
|
||||
auto relu16 =
|
||||
network->addActivation(*conv16->getOutput(0), ActivationType::kRELU);
|
||||
|
||||
assert(relu16);
|
||||
|
||||
auto conv17 = network->addConvolutionNd(
|
||||
*relu16->getOutput(0), 1, DimsHW{1, 1}, weightMap["output_layer.weight"],
|
||||
weightMap["output_layer.bias"]);
|
||||
assert(conv17);
|
||||
|
||||
conv17->setStrideNd(DimsHW{1, 1});
|
||||
conv17->getOutput(0)->setName(kOutputTensorName);
|
||||
network->markOutput(*conv17->getOutput(0));
|
||||
|
||||
IOptimizationProfile *profile = builder->createOptimizationProfile();
|
||||
profile->setDimensions(kInputTensorName, OptProfileSelector::kMIN,
|
||||
Dims4(1, 3, MIN_INPUT_SIZE, MIN_INPUT_SIZE));
|
||||
profile->setDimensions(kInputTensorName, OptProfileSelector::kOPT,
|
||||
Dims4(1, 3, OPT_INPUT_H, OPT_INPUT_W));
|
||||
profile->setDimensions(kInputTensorName, OptProfileSelector::kMAX,
|
||||
Dims4(1, 3, MAX_INPUT_SIZE, MAX_INPUT_SIZE));
|
||||
config->addOptimizationProfile(profile);
|
||||
|
||||
builder->setMaxBatchSize(kBatchSize);
|
||||
config->setMaxWorkspaceSize(16 << 20);
|
||||
#ifdef USE_FP16
|
||||
config->setFlag(BuilderFlag::kFP16);
|
||||
#endif
|
||||
ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
|
||||
|
||||
printf("build engine successfully : %s\n", kEngineFile);
|
||||
// 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) {
|
||||
// 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 =
|
||||
createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
config->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
int read_files_in_dir(const char *p_dir_name,
|
||||
std::vector<std::string> &file_names) {
|
||||
DIR *p_dir = opendir(p_dir_name);
|
||||
if (p_dir == nullptr) {
|
||||
return -1;
|
||||
}
|
||||
|
||||
struct dirent *p_file = nullptr;
|
||||
while ((p_file = readdir(p_dir)) != nullptr) {
|
||||
if (strcmp(p_file->d_name, ".") != 0 && strcmp(p_file->d_name, "..") != 0) {
|
||||
std::string cur_file_name(p_file->d_name);
|
||||
file_names.push_back(cur_file_name);
|
||||
}
|
||||
}
|
||||
closedir(p_dir);
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
|
||||
if (argc != 3) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./csrnet -s ./csrnet.wts // serialize model to plan file"
|
||||
<< std::endl;
|
||||
std::cerr
|
||||
<< "./csrnet -d ../images // deserialize plan file and run inference"
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory *modelStream{nullptr};
|
||||
kWTSFile = argv[2];
|
||||
APIToModel(kBatchSize, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p(kEngineFile, 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 1;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file(kEngineFile, std::ios::binary);
|
||||
if (file.good()) {
|
||||
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();
|
||||
}
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
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;
|
||||
|
||||
std::vector<std::string> file_names;
|
||||
if (read_files_in_dir(argv[2], file_names) < 0) {
|
||||
std::cout << "read_files_in_dir failed." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
std::vector<float> mean_value{0.406, 0.456, 0.485}; // BGR
|
||||
std::vector<float> std_value{0.225, 0.224, 0.229};
|
||||
|
||||
int fcount = 0;
|
||||
|
||||
float *data = new float[kMaxInputImageSize];
|
||||
float *prob = new float[kMaxOutputProbSize];
|
||||
|
||||
for (auto f : file_names) {
|
||||
fcount++;
|
||||
cv::Mat src_img = cv::imread(std::string(argv[2]) + "/" + f);
|
||||
if (src_img.empty())
|
||||
continue;
|
||||
|
||||
int i = 0;
|
||||
for (int row = 0; row < src_img.rows; ++row) {
|
||||
uchar *uc_pixel = src_img.data + row * src_img.step;
|
||||
for (int col = 0; col < src_img.cols; ++col) {
|
||||
data[i] = (uc_pixel[2] / 255.0 - mean_value[2]) / std_value[2];
|
||||
data[i + src_img.rows * src_img.cols] =
|
||||
(uc_pixel[1] / 255.0 - mean_value[1]) / std_value[1];
|
||||
data[i + 2 * src_img.rows * src_img.cols] =
|
||||
(uc_pixel[0] / 255.0 - mean_value[0]) / std_value[0];
|
||||
uc_pixel += 3;
|
||||
++i;
|
||||
}
|
||||
}
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, data, prob, src_img.rows, src_img.cols);
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << "detect time:"
|
||||
<< std::chrono::duration_cast<std::chrono::milliseconds>(end -
|
||||
start)
|
||||
.count()
|
||||
<< "ms" << std::endl;
|
||||
float num = std::accumulate(
|
||||
prob, prob + ((src_img.rows * src_img.cols) >> 6), 0.0f);
|
||||
|
||||
cv::Mat densityMap(src_img.rows >> 3, src_img.cols >> 3, CV_32FC1,
|
||||
(void *)prob);
|
||||
|
||||
cv::Mat densityMapScaled;
|
||||
cv::normalize(densityMap, densityMapScaled, 0, 255, cv::NORM_MINMAX,
|
||||
CV_8UC1);
|
||||
cv::Mat densityColorMap;
|
||||
cv::applyColorMap(densityMapScaled, densityColorMap, cv::COLORMAP_VIRIDIS);
|
||||
|
||||
cv::resize(densityColorMap, densityColorMap, src_img.size());
|
||||
cv::addWeighted(densityColorMap, 0.5, src_img, 0.5, 0, src_img);
|
||||
|
||||
// write to jpg
|
||||
cv::putText(src_img, std::string("people num: ") + std::to_string(num),
|
||||
cv::Point(10, 50), cv::FONT_HERSHEY_SIMPLEX, 0.5,
|
||||
cv::Scalar(255, 255, 255), 1);
|
||||
std::string write_path = std::string(argv[2]) + "result_" + f;
|
||||
std::cout << "people num :" << num << " write_path: " << write_path
|
||||
<< std::endl;
|
||||
cv::imwrite(write_path, src_img);
|
||||
}
|
||||
delete[] data;
|
||||
delete[] prob;
|
||||
|
||||
return 0;
|
||||
}
|
||||
31
csrnet/gen_wts.py
Normal file
31
csrnet/gen_wts.py
Normal file
@ -0,0 +1,31 @@
|
||||
from torch.nn.modules import module
|
||||
from model import CSRNet
|
||||
import torch
|
||||
import os
|
||||
import struct
|
||||
|
||||
|
||||
save_path = os.path.join(os.path.dirname(
|
||||
__file__), "output", os.path.basename(__file__).split('.')[0])
|
||||
os.makedirs(save_path, exist_ok=True)
|
||||
wts_file = os.path.join(save_path, "csrnet.wts")
|
||||
|
||||
|
||||
# load model
|
||||
model_path = "partBmodel_best.pth.tar"
|
||||
model = CSRNet()
|
||||
checkpoint = torch.load(model_path)
|
||||
model.load_state_dict(checkpoint['state_dict'])
|
||||
|
||||
|
||||
# save to wts
|
||||
print(f'Writing into {wts_file}')
|
||||
with open(wts_file, 'w') as f:
|
||||
f.write('{}\n'.format(len(model.state_dict().keys())))
|
||||
for k, v in model.state_dict().items():
|
||||
vr = v.reshape(-1).cpu().numpy()
|
||||
f.write('{} {} '.format(k, len(vr)))
|
||||
for vv in vr:
|
||||
f.write(' ')
|
||||
f.write(struct.pack('>f', float(vv)).hex())
|
||||
f.write('\n')
|
||||
502
csrnet/logging.h
Normal file
502
csrnet/logging.h
Normal file
@ -0,0 +1,502 @@
|
||||
/*
|
||||
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
#include "macros.h"
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf {
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream &stream, const std::string &prefix,
|
||||
bool shouldLog)
|
||||
: mOutput(stream), mPrefix(prefix), mShouldLog(shouldLog) {}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer &&other)
|
||||
: mOutput(other.mOutput) {}
|
||||
|
||||
~LogStreamConsumerBuffer() {
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered
|
||||
// part of the output sequence std::streambuf::pptr() gives a pointer to the
|
||||
// current position of the output sequence if the pointer to the beginning
|
||||
// is not equal to the pointer to the current position, call putOutput() to
|
||||
// log the output to the stream
|
||||
if (pbase() != pptr()) {
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents
|
||||
// into the stream, resetting the buffer and flushing the stream
|
||||
virtual int sync() {
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput() {
|
||||
if (mShouldLog) {
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm *tm_local = std::localtime(×tamp);
|
||||
std::cout << "[";
|
||||
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon
|
||||
<< "/";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday
|
||||
<< "/";
|
||||
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year
|
||||
<< "-";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour
|
||||
<< ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec
|
||||
<< "] ";
|
||||
// std::stringbuf::str() gets the string contents of the buffer
|
||||
// insert the buffer contents pre-appended by the appropriate prefix into
|
||||
// the stream
|
||||
mOutput << mPrefix << str();
|
||||
// set the buffer to empty
|
||||
str("");
|
||||
// flush the stream
|
||||
mOutput.flush();
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog) { mShouldLog = shouldLog; }
|
||||
|
||||
private:
|
||||
std::ostream &mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before
|
||||
//! std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase {
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream &stream, const std::string &prefix,
|
||||
bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog) {}
|
||||
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumer
|
||||
//! \brief Convenience object used to facilitate use of C++ stream syntax when
|
||||
//! logging messages.
|
||||
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
|
||||
//! This is because the LogStreamConsumerBase class is used to initialize the
|
||||
//! LogStreamConsumerBuffer member field in LogStreamConsumer and then the
|
||||
//! address of the buffer is passed to std::ostream. This is necessary to
|
||||
//! prevent the address of an uninitialized buffer from being passed to
|
||||
//! std::ostream. Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream {
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level
|
||||
//! severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be
|
||||
//! logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity),
|
||||
severityPrefix(severity),
|
||||
severity <= reportableSeverity),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(severity <= reportableSeverity), mSeverity(severity) {}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer &&other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity),
|
||||
severityPrefix(other.mSeverity),
|
||||
other.mShouldLog),
|
||||
std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
,
|
||||
mShouldLog(other.mShouldLog), mSeverity(other.mSeverity) {}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity) {
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream &severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
bool mShouldLog;
|
||||
Severity mSeverity;
|
||||
};
|
||||
|
||||
//! \class Logger
|
||||
//!
|
||||
//! \brief Class which manages logging of TensorRT tools and samples
|
||||
//!
|
||||
//! \details This class provides a common interface for TensorRT tools and
|
||||
//! samples to log information to the console, and supports logging two types of
|
||||
//! messages:
|
||||
//!
|
||||
//! - Debugging messages with an associated severity (info, warning, error, or
|
||||
//! internal error/fatal)
|
||||
//! - Test pass/fail messages
|
||||
//!
|
||||
//! The advantage of having all samples use this class for logging as opposed to
|
||||
//! emitting directly to stdout/stderr is that the logic for controlling the
|
||||
//! verbosity and formatting of sample output is centralized in one location.
|
||||
//!
|
||||
//! In the future, this class could be extended to support dumping test results
|
||||
//! to a file in some standard format (for example, JUnit XML), and providing
|
||||
//! additional metadata (e.g. timing the duration of a test run).
|
||||
//!
|
||||
//! TODO: For backwards compatibility with existing samples, this class inherits
|
||||
//! directly from the nvinfer1::ILogger interface, which is problematic since
|
||||
//! there isn't a clean separation between messages coming from the TensorRT
|
||||
//! library and messages coming from the sample.
|
||||
//!
|
||||
//! In the future (once all samples are updated to use Logger::getTRTLogger() to
|
||||
//! access the ILogger) we can refactor the class to eliminate the inheritance
|
||||
//! and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger {
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING)
|
||||
: mReportableSeverity(severity) {}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult {
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger
|
||||
//! associated with this Logger \return The nvinfer1::ILogger associated with
|
||||
//! this Logger
|
||||
//!
|
||||
//! TODO Once all samples are updated to use this method to register the
|
||||
//! logger with TensorRT, we can eliminate the inheritance of Logger from
|
||||
//! ILogger
|
||||
//!
|
||||
nvinfer1::ILogger &getTRTLogger() { return *this; }
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
//!
|
||||
//! Note samples should not be calling this function directly; it will
|
||||
//! eventually go away once we eliminate the inheritance from
|
||||
//! nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char *msg) TRT_NOEXCEPT override {
|
||||
LogStreamConsumer(mReportableSeverity, severity)
|
||||
<< "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Method for controlling the verbosity of logging output
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of
|
||||
//! this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity) {
|
||||
mReportableSeverity = severity;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
//!
|
||||
//! This object is an opaque handle to information used by the Logger to print
|
||||
//! test results. The sample must call Logger::defineTest() in order to obtain
|
||||
//! a TestAtom that can be used with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom {
|
||||
public:
|
||||
TestAtom(TestAtom &&) = default;
|
||||
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string &name, const std::string &cmdline)
|
||||
: mStarted(started), mName(name), mCmdline(cmdline) {}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
std::string mCmdline;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Define a test for logging
|
||||
//!
|
||||
//! \param[in] name The name of the test. This should be a string starting
|
||||
//! with
|
||||
//! "TensorRT" and containing dot-separated strings
|
||||
//! containing the characters [A-Za-z0-9_]. For example,
|
||||
//! "TensorRT.sample_googlenet"
|
||||
//! \param[in] cmdline The command line used to reproduce the test
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string &name,
|
||||
const std::string &cmdline) {
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief A convenience overloaded version of defineTest() that accepts an
|
||||
//! array of command-line arguments
|
||||
//! as input
|
||||
//!
|
||||
//! \param[in] name The name of the test
|
||||
//! \param[in] argc The number of command-line arguments
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string &name, int argc,
|
||||
char const *const *argv) {
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom &testAtom) {
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of
|
||||
//! TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom &testAtom, TestResult result) {
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom &testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom &testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom &testAtom) {
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom &testAtom, bool pass) {
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const { return mReportableSeverity; }
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the
|
||||
//! given severity
|
||||
//!
|
||||
static const char *severityPrefix(Severity severity) {
|
||||
switch (severity) {
|
||||
case Severity::kINTERNAL_ERROR:
|
||||
return "[F] ";
|
||||
case Severity::kERROR:
|
||||
return "[E] ";
|
||||
case Severity::kWARNING:
|
||||
return "[W] ";
|
||||
case Severity::kINFO:
|
||||
return "[I] ";
|
||||
case Severity::kVERBOSE:
|
||||
return "[V] ";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message
|
||||
//! with the given result
|
||||
//!
|
||||
static const char *testResultString(TestResult result) {
|
||||
switch (result) {
|
||||
case TestResult::kRUNNING:
|
||||
return "RUNNING";
|
||||
case TestResult::kPASSED:
|
||||
return "PASSED";
|
||||
case TestResult::kFAILED:
|
||||
return "FAILED";
|
||||
case TestResult::kWAIVED:
|
||||
return "WAIVED";
|
||||
default:
|
||||
assert(0);
|
||||
return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the
|
||||
//! given severity
|
||||
//!
|
||||
static std::ostream &severityOstream(Severity severity) {
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom &testAtom, TestResult result) {
|
||||
severityOstream(Severity::kINFO)
|
||||
<< "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const *const *argv) {
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++) {
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace {
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages
|
||||
//! of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger &logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages
|
||||
//! of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger &logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages
|
||||
//! of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger &logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages
|
||||
//! of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger &logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages
|
||||
//! of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger &logger) {
|
||||
return LogStreamConsumer(logger.getReportableSeverity(),
|
||||
Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
12
csrnet/macros.h
Normal file
12
csrnet/macros.h
Normal file
@ -0,0 +1,12 @@
|
||||
#ifndef __MACROS_H
|
||||
#define __MACROS_H
|
||||
|
||||
#if NV_TENSORRT_MAJOR >= 8
|
||||
#define TRT_NOEXCEPT noexcept
|
||||
#define TRT_CONST_ENQUEUE const
|
||||
#else
|
||||
#define TRT_NOEXCEPT
|
||||
#define TRT_CONST_ENQUEUE
|
||||
#endif
|
||||
|
||||
#endif // __MACROS_H
|
||||
Loading…
Reference in New Issue
Block a user