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[Model] Support DINO & DETR and add PaddleDetectionModel class (Paddl…
…ePaddle#1837) * 添加paddleclas模型 * 更新README_CN * 更新README_CN * 更新README * update get_model.sh * update get_models.sh * update paddleseg models * update paddle_seg models * update paddle_seg models * modified test resources * update benchmark_gpu_trt.sh * add paddle detection * add paddledetection to benchmark * modified benchmark cmakelists * update benchmark scripts * modified benchmark function calling * modified paddledetection documents * add PaddleDetectonModel * reset examples/paddledetection * resolve conflict * update pybind * resolve conflict * fix bug * delete debug mode * update checkarch log * update trt inputs example * Update README.md --------- Co-authored-by: DefTruth <[email protected]>
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| // Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
||
| #include "flags.h" | ||
| #include "macros.h" | ||
| #include "option.h" | ||
|
|
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| namespace vision = fastdeploy::vision; | ||
| namespace benchmark = fastdeploy::benchmark; | ||
|
|
||
| DEFINE_bool(no_nms, false, "Whether the model contains nms."); | ||
|
|
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| int main(int argc, char* argv[]) { | ||
| #if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION) | ||
| // Initialization | ||
| auto option = fastdeploy::RuntimeOption(); | ||
| if (!CreateRuntimeOption(&option, argc, argv, true)) { | ||
| return -1; | ||
| } | ||
| auto im = cv::imread(FLAGS_image); | ||
| std::unordered_map<std::string, std::string> config_info; | ||
| benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path, | ||
| &config_info); | ||
| std::string model_name, params_name, config_name; | ||
| auto model_format = fastdeploy::ModelFormat::PADDLE; | ||
| if (!UpdateModelResourceName(&model_name, ¶ms_name, &config_name, | ||
| &model_format, config_info)) { | ||
| return -1; | ||
| } | ||
| auto model_file = FLAGS_model + sep + model_name; | ||
| auto params_file = FLAGS_model + sep + params_name; | ||
| auto config_file = FLAGS_model + sep + config_name; | ||
| if (config_info["backend"] == "paddle_trt") { | ||
| option.paddle_infer_option.collect_trt_shape = true; | ||
| } | ||
| if (config_info["backend"] == "paddle_trt" || | ||
| config_info["backend"] == "trt") { | ||
| option.trt_option.SetShape("im_shape",{1,2},{1,2},{1,2}); | ||
| option.trt_option.SetShape("image", {1, 3, 320,320},{1, 3, 640, 640}, | ||
| {1, 3, 1280, 1280}); | ||
| option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, | ||
| {1, 2}); | ||
| } | ||
| auto model_ppdet = vision::detection::PaddleDetectionModel( | ||
| model_file, params_file, config_file, option, model_format); | ||
| vision::DetectionResult res; | ||
| if (config_info["precision_compare"] == "true") { | ||
| // Run once at least | ||
| model_ppdet.Predict(im, &res); | ||
| // 1. Test result diff | ||
| std::cout << "=============== Test result diff =================\n"; | ||
| // Save result to -> disk. | ||
| std::string det_result_path = "ppdet_result.txt"; | ||
| benchmark::ResultManager::SaveDetectionResult(res, det_result_path); | ||
| // Load result from <- disk. | ||
| vision::DetectionResult res_loaded; | ||
| benchmark::ResultManager::LoadDetectionResult(&res_loaded, det_result_path); | ||
| // Calculate diff between two results. | ||
| auto det_diff = | ||
| benchmark::ResultManager::CalculateDiffStatis(res, res_loaded); | ||
| std::cout << "Boxes diff: mean=" << det_diff.boxes.mean | ||
| << ", max=" << det_diff.boxes.max | ||
| << ", min=" << det_diff.boxes.min << std::endl; | ||
| std::cout << "Label_ids diff: mean=" << det_diff.labels.mean | ||
| << ", max=" << det_diff.labels.max | ||
| << ", min=" << det_diff.labels.min << std::endl; | ||
| // 2. Test tensor diff | ||
| std::cout << "=============== Test tensor diff =================\n"; | ||
| std::vector<vision::DetectionResult> batch_res; | ||
| std::vector<fastdeploy::FDTensor> input_tensors, output_tensors; | ||
| std::vector<cv::Mat> imgs; | ||
| imgs.push_back(im); | ||
| std::vector<vision::FDMat> fd_images = vision::WrapMat(imgs); | ||
|
|
||
| model_ppdet.GetPreprocessor().Run(&fd_images, &input_tensors); | ||
| input_tensors[0].name = "image"; | ||
| input_tensors[1].name = "scale_factor"; | ||
| input_tensors[2].name = "im_shape"; | ||
| input_tensors.pop_back(); | ||
| model_ppdet.Infer(input_tensors, &output_tensors); | ||
| model_ppdet.GetPostprocessor().Run(output_tensors, &batch_res); | ||
| // Save tensor to -> disk. | ||
| auto& tensor_dump = output_tensors[0]; | ||
| std::string det_tensor_path = "ppdet_tensor.txt"; | ||
| benchmark::ResultManager::SaveFDTensor(tensor_dump, det_tensor_path); | ||
| // Load tensor from <- disk. | ||
| fastdeploy::FDTensor tensor_loaded; | ||
| benchmark::ResultManager::LoadFDTensor(&tensor_loaded, det_tensor_path); | ||
| // Calculate diff between two tensors. | ||
| auto det_tensor_diff = benchmark::ResultManager::CalculateDiffStatis( | ||
| tensor_dump, tensor_loaded); | ||
| std::cout << "Tensor diff: mean=" << det_tensor_diff.data.mean | ||
| << ", max=" << det_tensor_diff.data.max | ||
| << ", min=" << det_tensor_diff.data.min << std::endl; | ||
| } | ||
| // Run profiling | ||
| if (FLAGS_no_nms) { | ||
| model_ppdet.GetPostprocessor().ApplyNMS(); | ||
| } | ||
| BENCHMARK_MODEL(model_ppdet, model_ppdet.Predict(im, &res)) | ||
| auto vis_im = vision::VisDetection(im, res,0.3); | ||
| cv::imwrite("vis_result.jpg", vis_im); | ||
| std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl; | ||
| #endif | ||
|
|
||
| return 0; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,117 @@ | ||
| // Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
||
| #include "flags.h" | ||
| #include "macros.h" | ||
| #include "option.h" | ||
|
|
||
| namespace vision = fastdeploy::vision; | ||
| namespace benchmark = fastdeploy::benchmark; | ||
|
|
||
| DEFINE_bool(no_nms, false, "Whether the model contains nms."); | ||
|
|
||
| int main(int argc, char* argv[]) { | ||
| #if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION) | ||
| // Initialization | ||
| auto option = fastdeploy::RuntimeOption(); | ||
| if (!CreateRuntimeOption(&option, argc, argv, true)) { | ||
| return -1; | ||
| } | ||
| auto im = cv::imread(FLAGS_image); | ||
| std::unordered_map<std::string, std::string> config_info; | ||
| benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path, | ||
| &config_info); | ||
| std::string model_name, params_name, config_name; | ||
| auto model_format = fastdeploy::ModelFormat::PADDLE; | ||
| if (!UpdateModelResourceName(&model_name, ¶ms_name, &config_name, | ||
| &model_format, config_info)) { | ||
| return -1; | ||
| } | ||
| auto model_file = FLAGS_model + sep + model_name; | ||
| auto params_file = FLAGS_model + sep + params_name; | ||
| auto config_file = FLAGS_model + sep + config_name; | ||
| if (config_info["backend"] == "paddle_trt") { | ||
| option.paddle_infer_option.collect_trt_shape = true; | ||
| } | ||
| if (config_info["backend"] == "paddle_trt" || | ||
| config_info["backend"] == "trt") { | ||
| option.trt_option.SetShape("image", {1, 3, 640, 640}, {1, 3, 640, 640}, | ||
| {1, 3, 640, 640}); | ||
| option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, | ||
| {1, 2}); | ||
| } | ||
| auto model_ppdet = vision::detection::PaddleDetectionModel( | ||
| model_file, params_file, config_file, option, model_format); | ||
| vision::DetectionResult res; | ||
| if (config_info["precision_compare"] == "true") { | ||
| // Run once at least | ||
| model_ppdet.Predict(im, &res); | ||
| // 1. Test result diff | ||
| std::cout << "=============== Test result diff =================\n"; | ||
| // Save result to -> disk. | ||
| std::string det_result_path = "ppdet_result.txt"; | ||
| benchmark::ResultManager::SaveDetectionResult(res, det_result_path); | ||
| // Load result from <- disk. | ||
| vision::DetectionResult res_loaded; | ||
| benchmark::ResultManager::LoadDetectionResult(&res_loaded, det_result_path); | ||
| // Calculate diff between two results. | ||
| auto det_diff = | ||
| benchmark::ResultManager::CalculateDiffStatis(res, res_loaded); | ||
| std::cout << "Boxes diff: mean=" << det_diff.boxes.mean | ||
| << ", max=" << det_diff.boxes.max | ||
| << ", min=" << det_diff.boxes.min << std::endl; | ||
| std::cout << "Label_ids diff: mean=" << det_diff.labels.mean | ||
| << ", max=" << det_diff.labels.max | ||
| << ", min=" << det_diff.labels.min << std::endl; | ||
| // 2. Test tensor diff | ||
| std::cout << "=============== Test tensor diff =================\n"; | ||
| std::vector<vision::DetectionResult> batch_res; | ||
| std::vector<fastdeploy::FDTensor> input_tensors, output_tensors; | ||
| std::vector<cv::Mat> imgs; | ||
| imgs.push_back(im); | ||
| std::vector<vision::FDMat> fd_images = vision::WrapMat(imgs); | ||
|
|
||
| model_ppdet.GetPreprocessor().Run(&fd_images, &input_tensors); | ||
| input_tensors[0].name = "image"; | ||
| input_tensors[1].name = "scale_factor"; | ||
| input_tensors[2].name = "im_shape"; | ||
| input_tensors.pop_back(); | ||
| model_ppdet.Infer(input_tensors, &output_tensors); | ||
| model_ppdet.GetPostprocessor().Run(output_tensors, &batch_res); | ||
| // Save tensor to -> disk. | ||
| auto& tensor_dump = output_tensors[0]; | ||
| std::string det_tensor_path = "ppdet_tensor.txt"; | ||
| benchmark::ResultManager::SaveFDTensor(tensor_dump, det_tensor_path); | ||
| // Load tensor from <- disk. | ||
| fastdeploy::FDTensor tensor_loaded; | ||
| benchmark::ResultManager::LoadFDTensor(&tensor_loaded, det_tensor_path); | ||
| // Calculate diff between two tensors. | ||
| auto det_tensor_diff = benchmark::ResultManager::CalculateDiffStatis( | ||
| tensor_dump, tensor_loaded); | ||
| std::cout << "Tensor diff: mean=" << det_tensor_diff.data.mean | ||
| << ", max=" << det_tensor_diff.data.max | ||
| << ", min=" << det_tensor_diff.data.min << std::endl; | ||
| } | ||
| // Run profiling | ||
| if (FLAGS_no_nms) { | ||
| model_ppdet.GetPostprocessor().ApplyNMS(); | ||
| } | ||
| BENCHMARK_MODEL(model_ppdet, model_ppdet.Predict(im, &res)) | ||
| auto vis_im = vision::VisDetection(im, res,0.3); | ||
| cv::imwrite("vis_result.jpg", vis_im); | ||
| std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl; | ||
| #endif | ||
|
|
||
| return 0; | ||
| } |
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