aa64535e1d
* 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>
536 lines
19 KiB
C++
536 lines
19 KiB
C++
#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});
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conv12->setStrideNd(DimsHW{1, 1});
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conv12->setDilationNd(DimsHW{2, 2});
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auto relu12 =
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network->addActivation(*conv12->getOutput(0), ActivationType::kRELU);
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assert(relu12);
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auto conv13 = network->addConvolutionNd(
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*relu12->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.4.weight"],
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weightMap["backend.4.bias"]);
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assert(conv13);
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conv13->setPaddingNd(DimsHW{2, 2});
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conv13->setStrideNd(DimsHW{1, 1});
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conv13->setDilationNd(DimsHW{2, 2});
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auto relu13 =
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network->addActivation(*conv13->getOutput(0), ActivationType::kRELU);
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assert(relu13);
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auto conv14 = network->addConvolutionNd(
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*relu13->getOutput(0), 256, DimsHW{3, 3}, weightMap["backend.6.weight"],
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weightMap["backend.6.bias"]);
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assert(conv14);
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conv14->setPaddingNd(DimsHW{2, 2});
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conv14->setStrideNd(DimsHW{1, 1});
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conv14->setDilationNd(DimsHW{2, 2});
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auto relu14 =
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network->addActivation(*conv14->getOutput(0), ActivationType::kRELU);
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assert(relu14);
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auto conv15 = network->addConvolutionNd(
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*relu14->getOutput(0), 128, DimsHW{3, 3}, weightMap["backend.8.weight"],
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weightMap["backend.8.bias"]);
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assert(conv15);
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conv15->setPaddingNd(DimsHW{2, 2});
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conv15->setStrideNd(DimsHW{1, 1});
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conv15->setDilationNd(DimsHW{2, 2});
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auto relu15 =
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network->addActivation(*conv15->getOutput(0), ActivationType::kRELU);
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assert(relu15);
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auto conv16 = network->addConvolutionNd(
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*relu15->getOutput(0), 64, DimsHW{3, 3}, weightMap["backend.10.weight"],
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weightMap["backend.10.bias"]);
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assert(conv16);
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conv16->setPaddingNd(DimsHW{2, 2});
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conv16->setStrideNd(DimsHW{1, 1});
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conv16->setDilationNd(DimsHW{2, 2});
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auto relu16 =
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network->addActivation(*conv16->getOutput(0), ActivationType::kRELU);
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assert(relu16);
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auto conv17 = network->addConvolutionNd(
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*relu16->getOutput(0), 1, DimsHW{1, 1}, weightMap["output_layer.weight"],
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weightMap["output_layer.bias"]);
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assert(conv17);
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conv17->setStrideNd(DimsHW{1, 1});
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conv17->getOutput(0)->setName(kOutputTensorName);
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network->markOutput(*conv17->getOutput(0));
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IOptimizationProfile *profile = builder->createOptimizationProfile();
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profile->setDimensions(kInputTensorName, OptProfileSelector::kMIN,
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Dims4(1, 3, MIN_INPUT_SIZE, MIN_INPUT_SIZE));
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profile->setDimensions(kInputTensorName, OptProfileSelector::kOPT,
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Dims4(1, 3, OPT_INPUT_H, OPT_INPUT_W));
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profile->setDimensions(kInputTensorName, OptProfileSelector::kMAX,
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Dims4(1, 3, MAX_INPUT_SIZE, MAX_INPUT_SIZE));
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config->addOptimizationProfile(profile);
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builder->setMaxBatchSize(kBatchSize);
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config->setMaxWorkspaceSize(16 << 20);
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#endif
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ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
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printf("build engine successfully : %s\n", kEngineFile);
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto &mem : weightMap) {
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free((void *)(mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory **modelStream) {
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// Create builder
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IBuilder *builder = createInferBuilder(gLogger);
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IBuilderConfig *config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an
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// engine
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ICudaEngine *engine =
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createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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config->destroy();
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builder->destroy();
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}
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int read_files_in_dir(const char *p_dir_name,
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std::vector<std::string> &file_names) {
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DIR *p_dir = opendir(p_dir_name);
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if (p_dir == nullptr) {
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return -1;
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}
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struct dirent *p_file = nullptr;
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while ((p_file = readdir(p_dir)) != nullptr) {
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if (strcmp(p_file->d_name, ".") != 0 && strcmp(p_file->d_name, "..") != 0) {
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std::string cur_file_name(p_file->d_name);
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file_names.push_back(cur_file_name);
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}
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}
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closedir(p_dir);
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return 0;
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}
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int main(int argc, char **argv) {
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if (argc != 3) {
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std::cerr << "arguments not right!" << std::endl;
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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;
|
|
} |