code format
dongshuilong
parent
87873447f5
commit
de72640087
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@ -215,9 +215,9 @@ cp ../configs/inference_cls.yaml tools/
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#### 2.3.2 执行
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```shell
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./build/clas_system -c inference_cls.yaml
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./build/clas_system -c tools/inference_cls.yaml
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# or
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./build/clas_system -config inference_cls.yaml
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./build/clas_system -config tools/inference_cls.yaml
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```
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最终屏幕上会输出结果，如下图所示。
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@ -18,102 +18,102 @@
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namespace Feature {
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void FeatureExtracter::LoadModel(const std::string &model_path,
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const std::string ¶ms_path) {
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paddle_infer::Config config;
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config.SetModel(model_path, params_path);
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void FeatureExtracter::LoadModel(const std::string &model_path,
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const std::string ¶ms_path) {
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paddle_infer::Config config;
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config.SetModel(model_path, params_path);
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 1, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 1, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
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std::vector<double> ×) {
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cv::Mat resize_img;
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std::vector<double> time;
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auto preprocess_start = std::chrono::steady_clock::now();
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this->resize_op_.Run(img, resize_img, this->resize_short_,
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this->resize_size_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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auto preprocess_end = std::chrono::steady_clock::now();
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auto infer_start = std::chrono::steady_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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auto infer_end = std::chrono::steady_clock::now();
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auto postprocess_start = std::chrono::steady_clock::now();
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if (this->feature_norm)
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FeatureNorm(out_data);
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auto postprocess_end = std::chrono::steady_clock::now();
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std::chrono::duration<float> preprocess_diff =
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preprocess_end - preprocess_start;
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time.push_back(double(preprocess_diff.count()) * 1000);
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std::chrono::duration<float> inference_diff = infer_end - infer_start;
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double inference_cost_time = double(inference_diff.count() * 1000);
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time.push_back(inference_cost_time);
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// std::chrono::duration<float> postprocess_diff =
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// postprocess_end - postprocess_start;
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time.push_back(0);
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// std::cout << "result: " << std::endl;
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// std::cout << "\tclass id: " << maxPosition << std::endl;
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// std::cout << std::fixed << std::setprecision(10)
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// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
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times[0] += time[0];
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times[1] += time[1];
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times[2] += time[2];
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
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void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
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std::vector<double> ×) {
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cv::Mat resize_img;
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std::vector<double> time;
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auto preprocess_start = std::chrono::steady_clock::now();
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this->resize_op_.Run(img, resize_img, this->resize_short_,
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this->resize_size_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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auto preprocess_end = std::chrono::steady_clock::now();
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auto infer_start = std::chrono::steady_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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auto infer_end = std::chrono::steady_clock::now();
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auto postprocess_start = std::chrono::steady_clock::now();
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if (this->feature_norm)
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FeatureNorm(out_data);
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auto postprocess_end = std::chrono::steady_clock::now();
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std::chrono::duration<float> preprocess_diff =
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preprocess_end - preprocess_start;
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time.push_back(double(preprocess_diff.count()) * 1000);
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std::chrono::duration<float> inference_diff = infer_end - infer_start;
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double inference_cost_time = double(inference_diff.count() * 1000);
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time.push_back(inference_cost_time);
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// std::chrono::duration<float> postprocess_diff =
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// postprocess_end - postprocess_start;
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time.push_back(0);
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// std::cout << "result: " << std::endl;
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// std::cout << "\tclass id: " << maxPosition << std::endl;
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// std::cout << std::fixed << std::setprecision(10)
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// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
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times[0] += time[0];
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times[1] += time[1];
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times[2] += time[2];
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}
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void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
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float featuer_sqrt = std::sqrt(std::inner_product(
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featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
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for (int i = 0; i < featuer.size(); ++i)
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featuer[i] /= featuer_sqrt;
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}
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void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
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float featuer_sqrt = std::sqrt(std::inner_product(
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featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
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for (int i = 0; i < featuer.size(); ++i)
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featuer[i] /= featuer_sqrt;
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}
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} // namespace Feature
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@ -37,304 +37,306 @@
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using namespace std;
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using namespace cv;
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DEFINE_string(config, "", "Path of yaml file");
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DEFINE_string(c, "", "Path of yaml file");
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DEFINE_string(config,
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"", "Path of yaml file");
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DEFINE_string(c,
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"", "Path of yaml file");
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void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
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const std::vector<std::string> &all_img_paths,
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void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
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const std::vector <std::string> &all_img_paths,
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const int batch_size, Detection::ObjectDetector *det,
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std::vector<Detection::ObjectResult> &im_result,
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std::vector <Detection::ObjectResult> &im_result,
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std::vector<int> &im_bbox_num, std::vector<double> &det_t,
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const bool visual_det = false,
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const bool run_benchmark = false,
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const std::string &output_dir = "output") {
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int steps = ceil(float(all_img_paths.size()) / batch_size);
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// printf("total images = %d, batch_size = %d, total steps = %d\n",
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// all_img_paths.size(), batch_size, steps);
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for (int idx = 0; idx < steps; idx++) {
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int left_image_cnt = all_img_paths.size() - idx * batch_size;
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if (left_image_cnt > batch_size) {
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left_image_cnt = batch_size;
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}
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// for (int bs = 0; bs < left_image_cnt; bs++) {
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// std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
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// cv::Mat im = cv::imread(image_file_path, 1);
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// batch_imgs.insert(batch_imgs.end(), im);
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// }
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int steps = ceil(float(all_img_paths.size()) / batch_size);
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// printf("total images = %d, batch_size = %d, total steps = %d\n",
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// all_img_paths.size(), batch_size, steps);
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for (int idx = 0; idx < steps; idx++) {
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int left_image_cnt = all_img_paths.size() - idx * batch_size;
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if (left_image_cnt > batch_size) {
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left_image_cnt = batch_size;
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}
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// for (int bs = 0; bs < left_image_cnt; bs++) {
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// std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
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// cv::Mat im = cv::imread(image_file_path, 1);
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// batch_imgs.insert(batch_imgs.end(), im);
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// }
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// Store all detected result
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std::vector<Detection::ObjectResult> result;
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std::vector<int> bbox_num;
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std::vector<double> det_times;
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bool is_rbox = false;
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if (run_benchmark) {
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det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
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} else {
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det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
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// get labels and colormap
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auto labels = det->GetLabelList();
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auto colormap = Detection::GenerateColorMap(labels.size());
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// Store all detected result
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std::vector <Detection::ObjectResult> result;
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std::vector<int> bbox_num;
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std::vector<double> det_times;
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bool is_rbox = false;
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if (run_benchmark) {
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det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
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} else {
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det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
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// get labels and colormap
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auto labels = det->GetLabelList();
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auto colormap = Detection::GenerateColorMap(labels.size());
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int item_start_idx = 0;
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for (int i = 0; i < left_image_cnt; i++) {
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cv::Mat im = batch_imgs[i];
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int detect_num = 0;
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int item_start_idx = 0;
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for (int i = 0; i < left_image_cnt; i++) {
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cv::Mat im = batch_imgs[i];
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int detect_num = 0;
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for (int j = 0; j < bbox_num[i]; j++) {
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Detection::ObjectResult item = result[item_start_idx + j];
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if (item.confidence < det->GetThreshold() || item.class_id == -1) {
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continue;
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}
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detect_num += 1;
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im_result.push_back(item);
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if (visual_det) {
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if (item.rect.size() > 6) {
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is_rbox = true;
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printf(
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"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
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item.class_id, item.confidence, item.rect[0], item.rect[1],
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item.rect[2], item.rect[3], item.rect[4], item.rect[5],
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item.rect[6], item.rect[7]);
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} else {
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printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
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item.class_id, item.confidence, item.rect[0], item.rect[1],
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item.rect[2], item.rect[3]);
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for (int j = 0; j < bbox_num[i]; j++) {
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Detection::ObjectResult item = result[item_start_idx + j];
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if (item.confidence < det->GetThreshold() || item.class_id == -1) {
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continue;
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}
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detect_num += 1;
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im_result.push_back(item);
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if (visual_det) {
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if (item.rect.size() > 6) {
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is_rbox = true;
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printf(
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"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
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item.class_id, item.confidence, item.rect[0], item.rect[1],
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item.rect[2], item.rect[3], item.rect[4], item.rect[5],
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item.rect[6], item.rect[7]);
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} else {
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printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
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item.class_id, item.confidence, item.rect[0], item.rect[1],
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item.rect[2], item.rect[3]);
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}
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}
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}
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im_bbox_num.push_back(detect_num);
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item_start_idx = item_start_idx + bbox_num[i];
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// Visualization result
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if (visual_det) {
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std::cout << all_img_paths.at(idx * batch_size + i)
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<< " The number of detected box: " << detect_num
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<< std::endl;
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cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
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colormap, is_rbox);
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std::vector<int> compression_params;
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compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
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compression_params.push_back(95);
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std::string output_path(output_dir);
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if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
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output_path += OS_PATH_SEP;
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}
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std::string image_file_path = all_img_paths.at(idx * batch_size + i);
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output_path +=
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image_file_path.substr(image_file_path.find_last_of('/') + 1);
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cv::imwrite(output_path, vis_img, compression_params);
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printf("Visualized output saved as %s\n", output_path.c_str());
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}
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}
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}
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}
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im_bbox_num.push_back(detect_num);
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item_start_idx = item_start_idx + bbox_num[i];
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// Visualization result
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if (visual_det) {
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std::cout << all_img_paths.at(idx * batch_size + i)
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<< " The number of detected box: " << detect_num
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<< std::endl;
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cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
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colormap, is_rbox);
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std::vector<int> compression_params;
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compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
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compression_params.push_back(95);
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std::string output_path(output_dir);
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if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
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output_path += OS_PATH_SEP;
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}
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std::string image_file_path = all_img_paths.at(idx * batch_size + i);
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output_path +=
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image_file_path.substr(image_file_path.find_last_of('/') + 1);
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cv::imwrite(output_path, vis_img, compression_params);
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printf("Visualized output saved as %s\n", output_path.c_str());
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}
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}
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det_t[0] += det_times[0];
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det_t[1] += det_times[1];
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det_t[2] += det_times[2];
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}
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det_t[0] += det_times[0];
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det_t[1] += det_times[1];
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det_t[2] += det_times[2];
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}
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}
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void PrintResult(std::string &img_path,
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std::vector<Detection::ObjectResult> &det_result,
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std::vector <Detection::ObjectResult> &det_result,
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std::vector<int> &indeices, VectorSearch &vector_search,
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SearchResult &search_result) {
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printf("%s:\n", img_path.c_str());
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for (int i = 0; i < indeices.size(); ++i) {
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int t = indeices[i];
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printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
|
||||
det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2],
|
||||
det_result[t].rect[3], det_result[t].confidence,
|
||||
vector_search.GetLabel(search_result.I[search_result.return_k * t])
|
||||
.c_str());
|
||||
}
|
||||
printf("%s:\n", img_path.c_str());
|
||||
for (int i = 0; i < indeices.size(); ++i) {
|
||||
int t = indeices[i];
|
||||
printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
|
||||
det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2],
|
||||
det_result[t].rect[3], det_result[t].confidence,
|
||||
vector_search.GetLabel(search_result.I[search_result.return_k * t])
|
||||
.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
std::string yaml_path = "";
|
||||
if (FLAGS_config == "" && FLAGS_c == "") {
|
||||
std::cerr << "[ERROR] usage: " << std::endl
|
||||
<< argv[0] << " -c $yaml_path" << std::endl
|
||||
<< "or:" << std::endl
|
||||
<< argv[0] << " -config $yaml_path" << std::endl;
|
||||
exit(1);
|
||||
} else if (FLAGS_config != "") {
|
||||
yaml_path = FLAGS_config;
|
||||
} else {
|
||||
yaml_path = FLAGS_c;
|
||||
}
|
||||
|
||||
YamlConfig config(yaml_path);
|
||||
config.PrintConfigInfo();
|
||||
|
||||
// initialize detector, rec_Model, vector_search
|
||||
Feature::FeatureExtracter feature_extracter(config.config_file);
|
||||
Detection::ObjectDetector detector(config.config_file);
|
||||
VectorSearch searcher(config.config_file);
|
||||
|
||||
// config
|
||||
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
|
||||
bool visual_det = false;
|
||||
if (config.config_file["Global"]["visual_det"].IsDefined()) {
|
||||
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
|
||||
}
|
||||
bool benchmark = false;
|
||||
if (config.config_file["Global"]["benchmark"].IsDefined()) {
|
||||
benchmark = config.config_file["Global"]["benchmark"].as<bool>();
|
||||
}
|
||||
int max_det_results = 5;
|
||||
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
|
||||
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
|
||||
}
|
||||
float rec_nms_thresold = 0.05;
|
||||
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
|
||||
rec_nms_thresold =
|
||||
config.config_file["Global"]["rec_nms_thresold"].as<float>();
|
||||
}
|
||||
|
||||
// load image_file_path
|
||||
std::string path =
|
||||
config.config_file["Global"]["infer_imgs"].as<std::string>();
|
||||
std::vector<std::string> img_files_list;
|
||||
if (cv::utils::fs::isDirectory(path)) {
|
||||
std::vector<cv::String> filenames;
|
||||
cv::glob(path, filenames);
|
||||
for (auto f : filenames) {
|
||||
img_files_list.push_back(f);
|
||||
}
|
||||
} else {
|
||||
img_files_list.push_back(path);
|
||||
}
|
||||
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
|
||||
// for time log
|
||||
std::vector<double> cls_times = {0, 0, 0};
|
||||
std::vector<double> det_times = {0, 0, 0};
|
||||
std::vector<double> search_times = {0, 0, 0};
|
||||
int instance_num = 0;
|
||||
// for read images
|
||||
std::vector<cv::Mat> batch_imgs;
|
||||
std::vector<std::string> img_paths;
|
||||
// for detection
|
||||
std::vector<Detection::ObjectResult> det_result;
|
||||
std::vector<int> det_bbox_num;
|
||||
// for vector search
|
||||
std::vector<float> features;
|
||||
std::vector<float> feature;
|
||||
// for nms
|
||||
std::vector<int> indeices;
|
||||
|
||||
int warmup_iter = img_files_list.size() > 5 ? 5 : img_files_list.size();
|
||||
if (benchmark) {
|
||||
img_files_list.insert(img_files_list.begin(), img_files_list.begin(),
|
||||
img_files_list.begin() + warmup_iter);
|
||||
}
|
||||
|
||||
for (int idx = 0; idx < img_files_list.size(); ++idx) {
|
||||
std::string img_path = img_files_list[idx];
|
||||
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
|
||||
if (!srcimg.data) {
|
||||
std::cerr << "[ERROR] image read failed! image path: " << img_path
|
||||
<< "\n";
|
||||
exit(-1);
|
||||
}
|
||||
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
|
||||
|
||||
batch_imgs.push_back(srcimg);
|
||||
img_paths.push_back(img_path);
|
||||
|
||||
// step1: get all detection results
|
||||
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
|
||||
det_bbox_num, det_times, visual_det, false);
|
||||
|
||||
// select max_det_results bbox
|
||||
if (det_result.size() > max_det_results) {
|
||||
det_result.resize(max_det_results);
|
||||
}
|
||||
instance_num += det_result.size();
|
||||
|
||||
// step2: add the whole image for recognition to improve recall
|
||||
Detection::ObjectResult result_whole_img = {
|
||||
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
|
||||
det_result.push_back(result_whole_img);
|
||||
det_bbox_num[0] = det_result.size() + 1;
|
||||
|
||||
// step3: extract feature for all boxes in an inmage
|
||||
SearchResult search_result;
|
||||
for (int j = 0; j < det_result.size(); ++j) {
|
||||
int w = det_result[j].rect[2] - det_result[j].rect[0];
|
||||
int h = det_result[j].rect[3] - det_result[j].rect[1];
|
||||
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h);
|
||||
cv::Mat crop_img = srcimg(rect);
|
||||
feature_extracter.Run(crop_img, feature, cls_times);
|
||||
features.insert(features.end(), feature.begin(), feature.end());
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
std::string yaml_path = "";
|
||||
if (FLAGS_config == "" && FLAGS_c == "") {
|
||||
std::cerr << "[ERROR] usage: " << std::endl
|
||||
<< argv[0] << " -c $yaml_path" << std::endl
|
||||
<< "or:" << std::endl
|
||||
<< argv[0] << " -config $yaml_path" << std::endl;
|
||||
exit(1);
|
||||
} else if (FLAGS_config != "") {
|
||||
yaml_path = FLAGS_config;
|
||||
} else {
|
||||
yaml_path = FLAGS_c;
|
||||
}
|
||||
|
||||
// step4: get search result
|
||||
auto search_start = std::chrono::steady_clock::now();
|
||||
search_result = searcher.Search(features.data(), det_result.size());
|
||||
auto search_end = std::chrono::steady_clock::now();
|
||||
YamlConfig config(yaml_path);
|
||||
config.PrintConfigInfo();
|
||||
|
||||
// nms for search result
|
||||
for (int i = 0; i < det_result.size(); ++i) {
|
||||
det_result[i].confidence = search_result.D[search_result.return_k * i];
|
||||
// initialize detector, rec_Model, vector_search
|
||||
Feature::FeatureExtracter feature_extracter(config.config_file);
|
||||
Detection::ObjectDetector detector(config.config_file);
|
||||
VectorSearch searcher(config.config_file);
|
||||
|
||||
// config
|
||||
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
|
||||
bool visual_det = false;
|
||||
if (config.config_file["Global"]["visual_det"].IsDefined()) {
|
||||
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
|
||||
}
|
||||
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
|
||||
auto nms_end = std::chrono::steady_clock::now();
|
||||
std::chrono::duration<float> search_diff = search_end - search_start;
|
||||
search_times[1] += double(search_diff.count() * 1000);
|
||||
|
||||
std::chrono::duration<float> nms_diff = nms_end - search_end;
|
||||
search_times[2] += double(nms_diff.count() * 1000);
|
||||
|
||||
// print result
|
||||
if (not benchmark or (benchmark and idx >= warmup_iter))
|
||||
PrintResult(img_path, det_result, indeices, searcher, search_result);
|
||||
|
||||
// for postprocess
|
||||
batch_imgs.clear();
|
||||
img_paths.clear();
|
||||
det_bbox_num.clear();
|
||||
det_result.clear();
|
||||
feature.clear();
|
||||
features.clear();
|
||||
indeices.clear();
|
||||
if (benchmark and warmup_iter == idx + 1) {
|
||||
det_times = {0, 0, 0};
|
||||
cls_times = {0, 0, 0};
|
||||
search_times = {0, 0, 0};
|
||||
instance_num = 0;
|
||||
bool benchmark = false;
|
||||
if (config.config_file["Global"]["benchmark"].IsDefined()) {
|
||||
benchmark = config.config_file["Global"]["benchmark"].as<bool>();
|
||||
}
|
||||
int max_det_results = 5;
|
||||
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
|
||||
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
|
||||
}
|
||||
float rec_nms_thresold = 0.05;
|
||||
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
|
||||
rec_nms_thresold =
|
||||
config.config_file["Global"]["rec_nms_thresold"].as<float>();
|
||||
}
|
||||
}
|
||||
|
||||
if (benchmark) {
|
||||
std::string presion = "fp32";
|
||||
if (config.config_file["Global"]["use_fp16"].IsDefined() and
|
||||
config.config_file["Global"]["use_fp16"].as<bool>())
|
||||
presion = "fp16";
|
||||
bool use_gpu = config.config_file["Global"]["use_gpu"].as<bool>();
|
||||
bool use_tensorrt = config.config_file["Global"]["use_tensorrt"].as<bool>();
|
||||
bool enable_mkldnn =
|
||||
config.config_file["Global"]["enable_mkldnn"].as<bool>();
|
||||
int cpu_num_threads =
|
||||
config.config_file["Global"]["cpu_num_threads"].as<int>();
|
||||
int batch_size = config.config_file["Global"]["batch_size"].as<int>();
|
||||
std::vector<int> shape =
|
||||
config.config_file["Global"]["image_shape"].as<std::vector<int>>();
|
||||
std::string det_shape = std::to_string(shape[0]);
|
||||
for (int i = 1; i < shape.size(); ++i)
|
||||
det_shape = det_shape + ", " + std::to_string(shape[i]);
|
||||
// load image_file_path
|
||||
std::string path =
|
||||
config.config_file["Global"]["infer_imgs"].as<std::string>();
|
||||
std::vector <std::string> img_files_list;
|
||||
if (cv::utils::fs::isDirectory(path)) {
|
||||
std::vector <cv::String> filenames;
|
||||
cv::glob(path, filenames);
|
||||
for (auto f : filenames) {
|
||||
img_files_list.push_back(f);
|
||||
}
|
||||
} else {
|
||||
img_files_list.push_back(path);
|
||||
}
|
||||
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
|
||||
// for time log
|
||||
std::vector<double> cls_times = {0, 0, 0};
|
||||
std::vector<double> det_times = {0, 0, 0};
|
||||
std::vector<double> search_times = {0, 0, 0};
|
||||
int instance_num = 0;
|
||||
// for read images
|
||||
std::vector <cv::Mat> batch_imgs;
|
||||
std::vector <std::string> img_paths;
|
||||
// for detection
|
||||
std::vector <Detection::ObjectResult> det_result;
|
||||
std::vector<int> det_bbox_num;
|
||||
// for vector search
|
||||
std::vector<float> features;
|
||||
std::vector<float> feature;
|
||||
// for nms
|
||||
std::vector<int> indeices;
|
||||
|
||||
AutoLogger autolog_det("Det", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, det_shape, presion,
|
||||
det_times, img_files_list.size() - warmup_iter);
|
||||
autolog_det.report();
|
||||
AutoLogger autolog_rec("Rec", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "3, 224, 224", presion,
|
||||
cls_times, instance_num);
|
||||
autolog_rec.report();
|
||||
AutoLogger autolog_search("Search", false, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "dynamic", presion,
|
||||
search_times, instance_num);
|
||||
autolog_search.report();
|
||||
}
|
||||
return 0;
|
||||
int warmup_iter = img_files_list.size() > 5 ? 5 : img_files_list.size();
|
||||
if (benchmark) {
|
||||
img_files_list.insert(img_files_list.begin(), img_files_list.begin(),
|
||||
img_files_list.begin() + warmup_iter);
|
||||
}
|
||||
|
||||
for (int idx = 0; idx < img_files_list.size(); ++idx) {
|
||||
std::string img_path = img_files_list[idx];
|
||||
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
|
||||
if (!srcimg.data) {
|
||||
std::cerr << "[ERROR] image read failed! image path: " << img_path
|
||||
<< "\n";
|
||||
exit(-1);
|
||||
}
|
||||
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
|
||||
|
||||
batch_imgs.push_back(srcimg);
|
||||
img_paths.push_back(img_path);
|
||||
|
||||
// step1: get all detection results
|
||||
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
|
||||
det_bbox_num, det_times, visual_det, false);
|
||||
|
||||
// select max_det_results bbox
|
||||
if (det_result.size() > max_det_results) {
|
||||
det_result.resize(max_det_results);
|
||||
}
|
||||
instance_num += det_result.size();
|
||||
|
||||
// step2: add the whole image for recognition to improve recall
|
||||
Detection::ObjectResult result_whole_img = {
|
||||
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
|
||||
det_result.push_back(result_whole_img);
|
||||
det_bbox_num[0] = det_result.size() + 1;
|
||||
|
||||
// step3: extract feature for all boxes in an inmage
|
||||
SearchResult search_result;
|
||||
for (int j = 0; j < det_result.size(); ++j) {
|
||||
int w = det_result[j].rect[2] - det_result[j].rect[0];
|
||||
int h = det_result[j].rect[3] - det_result[j].rect[1];
|
||||
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h);
|
||||
cv::Mat crop_img = srcimg(rect);
|
||||
feature_extracter.Run(crop_img, feature, cls_times);
|
||||
features.insert(features.end(), feature.begin(), feature.end());
|
||||
}
|
||||
|
||||
// step4: get search result
|
||||
auto search_start = std::chrono::steady_clock::now();
|
||||
search_result = searcher.Search(features.data(), det_result.size());
|
||||
auto search_end = std::chrono::steady_clock::now();
|
||||
|
||||
// nms for search result
|
||||
for (int i = 0; i < det_result.size(); ++i) {
|
||||
det_result[i].confidence = search_result.D[search_result.return_k * i];
|
||||
}
|
||||
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
|
||||
auto nms_end = std::chrono::steady_clock::now();
|
||||
std::chrono::duration<float> search_diff = search_end - search_start;
|
||||
search_times[1] += double(search_diff.count() * 1000);
|
||||
|
||||
std::chrono::duration<float> nms_diff = nms_end - search_end;
|
||||
search_times[2] += double(nms_diff.count() * 1000);
|
||||
|
||||
// print result
|
||||
if (not benchmark or (benchmark and idx >= warmup_iter))
|
||||
PrintResult(img_path, det_result, indeices, searcher, search_result);
|
||||
|
||||
// for postprocess
|
||||
batch_imgs.clear();
|
||||
img_paths.clear();
|
||||
det_bbox_num.clear();
|
||||
det_result.clear();
|
||||
feature.clear();
|
||||
features.clear();
|
||||
indeices.clear();
|
||||
if (benchmark and warmup_iter == idx + 1) {
|
||||
det_times = {0, 0, 0};
|
||||
cls_times = {0, 0, 0};
|
||||
search_times = {0, 0, 0};
|
||||
instance_num = 0;
|
||||
}
|
||||
}
|
||||
|
||||
if (benchmark) {
|
||||
std::string presion = "fp32";
|
||||
if (config.config_file["Global"]["use_fp16"].IsDefined() and
|
||||
config.config_file["Global"]["use_fp16"].as<bool>())
|
||||
presion = "fp16";
|
||||
bool use_gpu = config.config_file["Global"]["use_gpu"].as<bool>();
|
||||
bool use_tensorrt = config.config_file["Global"]["use_tensorrt"].as<bool>();
|
||||
bool enable_mkldnn =
|
||||
config.config_file["Global"]["enable_mkldnn"].as<bool>();
|
||||
int cpu_num_threads =
|
||||
config.config_file["Global"]["cpu_num_threads"].as<int>();
|
||||
int batch_size = config.config_file["Global"]["batch_size"].as<int>();
|
||||
std::vector<int> shape =
|
||||
config.config_file["Global"]["image_shape"].as < std::vector < int >> ();
|
||||
std::string det_shape = std::to_string(shape[0]);
|
||||
for (int i = 1; i < shape.size(); ++i)
|
||||
det_shape = det_shape + ", " + std::to_string(shape[i]);
|
||||
|
||||
AutoLogger autolog_det("Det", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, det_shape, presion,
|
||||
det_times, img_files_list.size() - warmup_iter);
|
||||
autolog_det.report();
|
||||
AutoLogger autolog_rec("Rec", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "3, 224, 224", presion,
|
||||
cls_times, instance_num);
|
||||
autolog_rec.report();
|
||||
AutoLogger autolog_search("Search", false, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "dynamic", presion,
|
||||
search_times, instance_num);
|
||||
autolog_search.report();
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue