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[Model] Support PP-ShiTuV2 models for PaddleClas (PaddlePaddle#1900)
* [cmake] add faiss.cmake -> pp-shituv2 * [PP-ShiTuV2] Support PP-ShituV2-Det model * [PP-ShiTuV2] Support PP-ShiTuV2-Det model * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [Bug Fix] fix ppshitu_pybind error * [benchmark] Add ppshituv2-det c++ benchmark * [examples] Add PP-ShiTuV2 det & rec examples * [vision] Update vision classification result * [Bug Fix] fix trt shapes setting errors
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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" | ||
|
|
||
| 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}); | ||
| option.trt_option.SetShape("im_shape", {1, 2}, {1, 2}, {1, 2}); | ||
| } | ||
| auto model = vision::classification::PPShiTuV2Detector( | ||
| model_file, params_file, config_file, option, model_format); | ||
| if (FLAGS_no_nms) { | ||
| model.GetPostprocessor().ApplyNMS(); | ||
| } | ||
| vision::DetectionResult res; | ||
| if (config_info["precision_compare"] == "true") { | ||
| // Run once at least | ||
| model.Predict(im, &res); | ||
| // 1. Test result diff | ||
| std::cout << "=============== Test result diff =================\n"; | ||
| // Save result to -> disk. | ||
| std::string det_result_path = "ppshituv2_det_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; | ||
| } | ||
| // Run profiling | ||
| BENCHMARK_MODEL(model, model.Predict(im, &res)) | ||
| auto vis_im = vision::VisDetection(im, res, 0.5f); | ||
| 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 +1,93 @@ | ||
| // 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_string(trt_shape, "1,3,224,224:1,3,224,224:1,3,224,224", | ||
| "Set min/opt/max shape for trt/paddle_trt backend." | ||
| "eg:--trt_shape 1,3,224,224:1,3,224,224:1,3,224,224"); | ||
|
|
||
| DEFINE_string(input_name, "x", | ||
| "Set input name for trt/paddle_trt backend." | ||
| "eg:--input_names x"); | ||
|
|
||
| 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); | ||
| // Set max_batch_size 1 for best performance | ||
| if (config_info["backend"] == "paddle_trt") { | ||
| option.trt_option.max_batch_size = 1; | ||
| } | ||
| 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") { | ||
| std::vector<std::vector<int32_t>> trt_shapes = | ||
| benchmark::ResultManager::GetInputShapes(FLAGS_trt_shape); | ||
| option.trt_option.SetShape(FLAGS_input_name, trt_shapes[0], trt_shapes[1], | ||
| trt_shapes[2]); | ||
| } | ||
|
|
||
| auto model = vision::classification::PPShiTuV2Recognizer( | ||
| model_file, params_file, config_file, option, model_format); | ||
| vision::ClassifyResult res; | ||
| if (config_info["precision_compare"] == "true") { | ||
| // Run once at least | ||
| model.Predict(im, &res); | ||
| // 1. Test result diff | ||
| std::cout << "=============== Test result diff =================\n"; | ||
| // Save result to -> disk. | ||
| std::string cls_result_path = "ppcls_result.txt"; | ||
| benchmark::ResultManager::SaveClassifyResult(res, cls_result_path); | ||
| // Load result from <- disk. | ||
| vision::ClassifyResult res_loaded; | ||
| benchmark::ResultManager::LoadClassifyResult(&res_loaded, cls_result_path); | ||
| // Calculate diff between two results. | ||
| auto cls_diff = | ||
| benchmark::ResultManager::CalculateDiffStatis(res, res_loaded); | ||
| std::cout << "Labels diff: mean=" << cls_diff.labels.mean | ||
| << ", max=" << cls_diff.labels.max | ||
| << ", min=" << cls_diff.labels.min << std::endl; | ||
| std::cout << "Scores diff: mean=" << cls_diff.scores.mean | ||
| << ", max=" << cls_diff.scores.max | ||
| << ", min=" << cls_diff.scores.min << std::endl; | ||
| } | ||
| BENCHMARK_MODEL(model, model.Predict(im, &res)) | ||
| #endif | ||
| return 0; | ||
| } |
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