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duan8/csrnet/csrnet.cpp
AadeIT aa64535e1d
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>
2024-03-07 19:00:37 +08:00

536 lines
19 KiB
C++
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#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include <chrono>
#include <config.h>
#include <cstring>
#include <dirent.h>
#include <fstream>
#include <iostream>
#include <logging.h>
#include <map>
#include <numeric>
#include <opencv2/opencv.hpp>
#include <vector>
using namespace nvinfer1;
#define CHECK(status) \
do { \
auto ret = (status); \
if (ret != 0) { \
std::cerr << "Cuda failure: " << ret << std::endl; \
abort(); \
} \
} while (0)
static Logger gLogger;
static char *kWTSFile = "";
std::map<std::string, Weights> loadWeights(const std::string file) {
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--) {
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t *val = reinterpret_cast<uint32_t *>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x) {
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
// clang-format off
/*
CSRNet(
(frontend): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace=True)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace=True)
)
(backend): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2),
dilation=(2, 2)) (1): ReLU(inplace=True) (2): Conv2d(512, 512,
kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) (3):
ReLU(inplace=True) (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1,
1), padding=(2, 2), dilation=(2, 2)) (5): ReLU(inplace=True) (6):
Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2),
dilation=(2, 2)) (7): ReLU(inplace=True) (8): Conv2d(256, 128,
kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) (9):
ReLU(inplace=True) (10): Conv2d(128, 64, kernel_size=(3, 3), stride=(1,
1), padding=(2, 2), dilation=(2, 2)) (11): ReLU(inplace=True)
)
(output_layer): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))
)
*/
// clang-format on
void doInference(IExecutionContext &context, float *input, float *output,
int input_h, int input_w) {
const ICudaEngine &engine = context.getEngine();
uint64_t input_size = 3 * input_h * input_w * sizeof(float);
uint64_t output_size = ((input_h * input_w) >> 6) * sizeof(float);
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void *buffers[2];
// In order to bind the buffers, we need to know the names of the input and
// output tensors. Note that indices are guaranteed to be less than
// IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(kInputTensorName);
const int outputIndex = engine.getBindingIndex(kOutputTensorName);
context.setBindingDimensions(inputIndex, Dims4(1, 3, input_h, input_w));
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], input_size));
CHECK(cudaMalloc(&buffers[outputIndex], output_size));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA
// output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, input_size,
cudaMemcpyHostToDevice, stream));
auto t1 = std::chrono::high_resolution_clock::now();
context.enqueueV2(buffers, stream, nullptr);
std::cout << "enqueueV2 time: "
<< std::chrono::duration<float>(
std::chrono::high_resolution_clock::now() - t1)
.count()
<< "s" << std::endl;
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size,
cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
ICudaEngine *createEngine(unsigned int maxBatchSize, IBuilder *builder,
IBuilderConfig *config, DataType dt) {
// INetworkDefinition *network = builder->createNetworkV2(0U);
const auto explicitBatch =
1U << static_cast<uint32_t>(
NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
INetworkDefinition *network = builder->createNetworkV2(explicitBatch);
ITensor *data = network->addInput(kInputTensorName, dt, Dims4{1, 3, -1, -1});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(kWTSFile);
IConvolutionLayer *conv1 = network->addConvolutionNd(
*data, 64, DimsHW{3, 3}, weightMap["frontend.0.weight"],
weightMap["frontend.0.bias"]);
assert(conv1);
conv1->setStrideNd(DimsHW{1, 1});
conv1->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu1 =
network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
assert(relu1);
auto conv2 = network->addConvolutionNd(*relu1->getOutput(0), 64, DimsHW{3, 3},
weightMap["frontend.2.weight"],
weightMap["frontend.2.bias"]);
assert(conv2);
conv2->setStrideNd(DimsHW{1, 1});
conv2->setPaddingNd(DimsHW{1, 1});
auto relu2 =
network->addActivation(*conv2->getOutput(0), ActivationType::kRELU);
assert(relu2);
auto pool1 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kMAX,
DimsHW{2, 2});
assert(pool1);
pool1->setStrideNd(DimsHW{2, 2});
auto conv3 = network->addConvolutionNd(
*pool1->getOutput(0), 128, DimsHW{3, 3}, weightMap["frontend.5.weight"],
weightMap["frontend.5.bias"]);
assert(conv3);
conv3->setStrideNd(DimsHW{1, 1});
conv3->setPaddingNd(DimsHW{1, 1});
auto relu3 =
network->addActivation(*conv3->getOutput(0), ActivationType::kRELU);
assert(relu3);
auto conv4 = network->addConvolutionNd(
*relu3->getOutput(0), 128, DimsHW{3, 3}, weightMap["frontend.7.weight"],
weightMap["frontend.7.bias"]);
assert(conv4);
conv4->setStrideNd(DimsHW{1, 1});
conv4->setPaddingNd(DimsHW{1, 1});
auto relu4 =
network->addActivation(*conv4->getOutput(0), ActivationType::kRELU);
assert(relu4);
auto pool2 = network->addPoolingNd(*relu4->getOutput(0), PoolingType::kMAX,
DimsHW{2, 2});
assert(pool2);
pool2->setStrideNd(DimsHW{2, 2});
auto conv5 = network->addConvolutionNd(
*pool2->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.10.weight"],
weightMap["frontend.10.bias"]);
assert(conv5);
conv5->setStrideNd(DimsHW{1, 1});
conv5->setPaddingNd(DimsHW{1, 1});
auto relu5 =
network->addActivation(*conv5->getOutput(0), ActivationType::kRELU);
assert(relu5);
auto conv6 = network->addConvolutionNd(
*relu5->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.12.weight"],
weightMap["frontend.12.bias"]);
assert(conv6);
conv6->setStrideNd(DimsHW{1, 1});
conv6->setPaddingNd(DimsHW{1, 1});
auto relu6 =
network->addActivation(*conv6->getOutput(0), ActivationType::kRELU);
assert(relu6);
auto conv7 = network->addConvolutionNd(
*relu6->getOutput(0), 256, DimsHW{3, 3}, weightMap["frontend.14.weight"],
weightMap["frontend.14.bias"]);
assert(conv7);
conv7->setStrideNd(DimsHW{1, 1});
conv7->setPaddingNd(DimsHW{1, 1});
auto relu7 =
network->addActivation(*conv7->getOutput(0), ActivationType::kRELU);
assert(relu7);
auto pool3 = network->addPoolingNd(*relu7->getOutput(0), PoolingType::kMAX,
DimsHW{2, 2});
assert(pool3);
pool3->setStrideNd(DimsHW{2, 2});
auto conv8 = network->addConvolutionNd(
*pool3->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.17.weight"],
weightMap["frontend.17.bias"]);
assert(conv8);
conv8->setStrideNd(DimsHW{1, 1});
conv8->setPaddingNd(DimsHW{1, 1});
auto relu8 =
network->addActivation(*conv8->getOutput(0), ActivationType::kRELU);
assert(relu8);
auto conv9 = network->addConvolutionNd(
*relu8->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.19.weight"],
weightMap["frontend.19.bias"]);
assert(conv9);
conv9->setStrideNd(DimsHW{1, 1});
conv9->setPaddingNd(DimsHW{1, 1});
auto relu9 =
network->addActivation(*conv9->getOutput(0), ActivationType::kRELU);
assert(relu9);
auto conv10 = network->addConvolutionNd(
*relu9->getOutput(0), 512, DimsHW{3, 3}, weightMap["frontend.21.weight"],
weightMap["frontend.21.bias"]);
assert(conv10);
conv10->setStrideNd(DimsHW{1, 1});
conv10->setPaddingNd(DimsHW{1, 1});
auto relu10 =
network->addActivation(*conv10->getOutput(0), ActivationType::kRELU);
assert(relu10);
// backend
auto conv11 = network->addConvolutionNd(
*relu10->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.0.weight"],
weightMap["backend.0.bias"]);
assert(conv11);
conv11->setPaddingNd(DimsHW{2, 2});
conv11->setStrideNd(DimsHW{1, 1});
conv11->setDilationNd(DimsHW{2, 2});
auto relu11 =
network->addActivation(*conv11->getOutput(0), ActivationType::kRELU);
assert(relu11);
auto conv12 = network->addConvolutionNd(
*relu11->getOutput(0), 512, DimsHW{3, 3}, weightMap["backend.2.weight"],
weightMap["backend.2.bias"]);
assert(conv12);
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;
}
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