mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
[Model] Support PP-ShiTuV2 models for PaddleClas (#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
This commit is contained in:
DefTruth
@@ -40,6 +40,8 @@ add_executable(benchmark_ttfnet ${PROJECT_SOURCE_DIR}/benchmark_ttfnet.cc)
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add_executable(benchmark ${PROJECT_SOURCE_DIR}/benchmark.cc)
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add_executable(benchmark_ppdet ${PROJECT_SOURCE_DIR}/benchmark_ppdet.cc)
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add_executable(benchmark_dino ${PROJECT_SOURCE_DIR}/benchmark_dino.cc)
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add_executable(benchmark_ppshituv2_rec ${PROJECT_SOURCE_DIR}/benchmark_ppshituv2_rec.cc)
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add_executable(benchmark_ppshituv2_det ${PROJECT_SOURCE_DIR}/benchmark_ppshituv2_det.cc)
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if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
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target_link_libraries(benchmark_yolov5 ${FASTDEPLOY_LIBS} gflags pthread)
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@@ -75,6 +77,8 @@ if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
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target_link_libraries(benchmark ${FASTDEPLOY_LIBS} gflags pthread)
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target_link_libraries(benchmark_ppdet ${FASTDEPLOY_LIBS} gflags pthread)
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target_link_libraries(benchmark_dino ${FASTDEPLOY_LIBS} gflags pthread)
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target_link_libraries(benchmark_ppshituv2_rec ${FASTDEPLOY_LIBS} gflags pthread)
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target_link_libraries(benchmark_ppshituv2_det ${FASTDEPLOY_LIBS} gflags pthread)
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else()
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target_link_libraries(benchmark_yolov5 ${FASTDEPLOY_LIBS} gflags)
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target_link_libraries(benchmark_ppyolov5 ${FASTDEPLOY_LIBS} gflags)
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@@ -109,6 +113,8 @@ else()
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target_link_libraries(benchmark ${FASTDEPLOY_LIBS} gflags)
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target_link_libraries(benchmark_ppdet ${FASTDEPLOY_LIBS} gflags)
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target_link_libraries(benchmark_dino ${FASTDEPLOY_LIBS} gflags)
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target_link_libraries(benchmark_ppshituv2_rec ${FASTDEPLOY_LIBS} gflags)
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target_link_libraries(benchmark_ppshituv2_det ${FASTDEPLOY_LIBS} gflags)
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endif()
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# only for Android ADB test
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if(ANDROID)
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20
benchmark/cpp/benchmark.cc
Executable file → Normal file
20
benchmark/cpp/benchmark.cc
Executable file → Normal file
@@ -33,11 +33,14 @@ DEFINE_string(tensors, "tensor_a.txt:tensor_b.txt",
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"The paths to dumped tensors.");
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DEFINE_bool(mem, false, "Whether to force to collect memory info.");
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DEFINE_int32(interval, -1, "Sampling interval for collect memory info.");
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DEFINE_string(model_file, "UNKNOWN", "Optional, set specific model file,"
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DEFINE_string(model_file, "UNKNOWN",
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"Optional, set specific model file,"
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"eg, model.pdmodel, model.onnx");
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DEFINE_string(params_file, "", "Optional, set specific params file,"
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DEFINE_string(params_file, "",
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"Optional, set specific params file,"
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"eg, model.pdiparams.");
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DEFINE_string(model_format, "PADDLE", "Optional, set specific model format,"
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DEFINE_string(model_format, "PADDLE",
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"Optional, set specific model format,"
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"eg, PADDLE/ONNX/RKNN/TORCHSCRIPT/SOPHGO");
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DEFINE_bool(disable_mkldnn, false, "disable mkldnn for paddle backend");
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@@ -117,8 +120,8 @@ static void RuntimeProfiling(int argc, char* argv[]) {
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model_file = FLAGS_model + sep + FLAGS_model_file;
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params_file = FLAGS_model + sep + FLAGS_params_file;
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model_format = GetModelFormat(FLAGS_model_format);
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if (model_format == fastdeploy::ModelFormat::PADDLE
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&& FLAGS_params_file == "") {
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if (model_format == fastdeploy::ModelFormat::PADDLE &&
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FLAGS_params_file == "") {
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std::cout << "[ERROR] params_file can not be empty for PADDLE"
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<< " format, Please, set your custom params_file manually."
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<< std::endl;
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@@ -256,6 +259,9 @@ static void showInputInfos(int argc, char* argv[]) {
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if (!CreateRuntimeOption(&option, argc, argv, true)) {
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return;
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}
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if (FLAGS_disable_mkldnn) {
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option.paddle_infer_option.enable_mkldnn = false;
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}
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std::unordered_map<std::string, std::string> config_info;
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benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
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&config_info);
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@@ -286,7 +292,9 @@ static void showInputInfos(int argc, char* argv[]) {
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int main(int argc, char* argv[]) {
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#if defined(ENABLE_BENCHMARK)
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google::SetVersionString("0.0.0");
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google::SetUsageMessage("./benchmark -[info|diff|check|dump|mem] -model xxx -config_path xxx -[shapes|dtypes|names|tensors] -[model_file|params_file|model_format]");
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google::SetUsageMessage(
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"./benchmark -[info|diff|check|dump|mem] -model xxx -config_path xxx "
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"-[shapes|dtypes|names|tensors] -[model_file|params_file|model_format]");
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google::ParseCommandLineFlags(&argc, &argv, true);
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if (FLAGS_diff) {
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CheckTensorDiff(argc, argv);
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5
benchmark/cpp/benchmark_picodet.cc
Executable file → Normal file
5
benchmark/cpp/benchmark_picodet.cc
Executable file → Normal file
@@ -49,8 +49,7 @@ int main(int argc, char* argv[]) {
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config_info["backend"] == "trt") {
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option.trt_option.SetShape("image", {1, 3, 640, 640}, {1, 3, 640, 640},
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{1, 3, 640, 640});
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option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2},
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{1, 2});
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option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, {1, 2});
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}
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auto model_picodet = vision::detection::PicoDet(
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model_file, params_file, config_file, option, model_format);
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@@ -81,7 +80,7 @@ int main(int argc, char* argv[]) {
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}
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// Run profiling
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BENCHMARK_MODEL(model_picodet, model_picodet.Predict(im, &res))
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auto vis_im = vision::VisDetection(im, res);
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auto vis_im = vision::VisDetection(im, res, 0.5f);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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#endif
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89
benchmark/cpp/benchmark_ppshituv2_det.cc
Normal file
89
benchmark/cpp/benchmark_ppshituv2_det.cc
Normal file
@@ -0,0 +1,89 @@
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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "flags.h"
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#include "macros.h"
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#include "option.h"
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namespace vision = fastdeploy::vision;
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namespace benchmark = fastdeploy::benchmark;
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DEFINE_bool(no_nms, false, "Whether the model contains nms.");
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int main(int argc, char* argv[]) {
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#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
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// Initialization
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auto option = fastdeploy::RuntimeOption();
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if (!CreateRuntimeOption(&option, argc, argv, true)) {
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return -1;
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}
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auto im = cv::imread(FLAGS_image);
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std::unordered_map<std::string, std::string> config_info;
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benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
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&config_info);
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std::string model_name, params_name, config_name;
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auto model_format = fastdeploy::ModelFormat::PADDLE;
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if (!UpdateModelResourceName(&model_name, ¶ms_name, &config_name,
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&model_format, config_info)) {
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return -1;
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}
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auto model_file = FLAGS_model + sep + model_name;
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auto params_file = FLAGS_model + sep + params_name;
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auto config_file = FLAGS_model + sep + config_name;
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if (config_info["backend"] == "paddle_trt") {
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option.paddle_infer_option.collect_trt_shape = true;
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}
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if (config_info["backend"] == "paddle_trt" ||
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config_info["backend"] == "trt") {
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option.trt_option.SetShape("image", {1, 3, 640, 640}, {1, 3, 640, 640},
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{1, 3, 640, 640});
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option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, {1, 2});
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option.trt_option.SetShape("im_shape", {1, 2}, {1, 2}, {1, 2});
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}
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auto model = vision::classification::PPShiTuV2Detector(
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model_file, params_file, config_file, option, model_format);
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if (FLAGS_no_nms) {
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model.GetPostprocessor().ApplyNMS();
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}
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vision::DetectionResult res;
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if (config_info["precision_compare"] == "true") {
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// Run once at least
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model.Predict(im, &res);
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// 1. Test result diff
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std::cout << "=============== Test result diff =================\n";
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// Save result to -> disk.
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std::string det_result_path = "ppshituv2_det_result.txt";
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benchmark::ResultManager::SaveDetectionResult(res, det_result_path);
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// Load result from <- disk.
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vision::DetectionResult res_loaded;
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benchmark::ResultManager::LoadDetectionResult(&res_loaded, det_result_path);
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// Calculate diff between two results.
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auto det_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Boxes diff: mean=" << det_diff.boxes.mean
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<< ", max=" << det_diff.boxes.max
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<< ", min=" << det_diff.boxes.min << std::endl;
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std::cout << "Label_ids diff: mean=" << det_diff.labels.mean
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<< ", max=" << det_diff.labels.max
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<< ", min=" << det_diff.labels.min << std::endl;
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}
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// Run profiling
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BENCHMARK_MODEL(model, model.Predict(im, &res))
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auto vis_im = vision::VisDetection(im, res, 0.5f);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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#endif
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return 0;
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}
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93
benchmark/cpp/benchmark_ppshituv2_rec.cc
Normal file
93
benchmark/cpp/benchmark_ppshituv2_rec.cc
Normal file
@@ -0,0 +1,93 @@
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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "flags.h"
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#include "macros.h"
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#include "option.h"
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namespace vision = fastdeploy::vision;
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namespace benchmark = fastdeploy::benchmark;
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DEFINE_string(trt_shape, "1,3,224,224:1,3,224,224:1,3,224,224",
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"Set min/opt/max shape for trt/paddle_trt backend."
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"eg:--trt_shape 1,3,224,224:1,3,224,224:1,3,224,224");
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DEFINE_string(input_name, "x",
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"Set input name for trt/paddle_trt backend."
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"eg:--input_names x");
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int main(int argc, char* argv[]) {
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#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
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// Initialization
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auto option = fastdeploy::RuntimeOption();
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if (!CreateRuntimeOption(&option, argc, argv, true)) {
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return -1;
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}
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auto im = cv::imread(FLAGS_image);
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std::unordered_map<std::string, std::string> config_info;
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benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
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&config_info);
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// Set max_batch_size 1 for best performance
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if (config_info["backend"] == "paddle_trt") {
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option.trt_option.max_batch_size = 1;
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}
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std::string model_name, params_name, config_name;
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auto model_format = fastdeploy::ModelFormat::PADDLE;
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if (!UpdateModelResourceName(&model_name, ¶ms_name, &config_name,
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&model_format, config_info)) {
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return -1;
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}
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auto model_file = FLAGS_model + sep + model_name;
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auto params_file = FLAGS_model + sep + params_name;
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auto config_file = FLAGS_model + sep + config_name;
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if (config_info["backend"] == "paddle_trt") {
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option.paddle_infer_option.collect_trt_shape = true;
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}
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if (config_info["backend"] == "paddle_trt" ||
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config_info["backend"] == "trt") {
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std::vector<std::vector<int32_t>> trt_shapes =
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benchmark::ResultManager::GetInputShapes(FLAGS_trt_shape);
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option.trt_option.SetShape(FLAGS_input_name, trt_shapes[0], trt_shapes[1],
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trt_shapes[2]);
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}
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auto model = vision::classification::PPShiTuV2Recognizer(
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model_file, params_file, config_file, option, model_format);
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vision::ClassifyResult res;
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if (config_info["precision_compare"] == "true") {
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// Run once at least
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model.Predict(im, &res);
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// 1. Test result diff
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std::cout << "=============== Test result diff =================\n";
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// Save result to -> disk.
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std::string cls_result_path = "ppcls_result.txt";
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benchmark::ResultManager::SaveClassifyResult(res, cls_result_path);
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// Load result from <- disk.
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vision::ClassifyResult res_loaded;
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benchmark::ResultManager::LoadClassifyResult(&res_loaded, cls_result_path);
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// Calculate diff between two results.
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auto cls_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Labels diff: mean=" << cls_diff.labels.mean
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<< ", max=" << cls_diff.labels.max
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<< ", min=" << cls_diff.labels.min << std::endl;
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std::cout << "Scores diff: mean=" << cls_diff.scores.mean
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<< ", max=" << cls_diff.scores.max
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<< ", min=" << cls_diff.scores.min << std::endl;
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}
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BENCHMARK_MODEL(model, model.Predict(im, &res))
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#endif
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return 0;
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}
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163
cmake/faiss.cmake
Normal file
163
cmake/faiss.cmake
Normal file
@@ -0,0 +1,163 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
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#
|
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# 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
|
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#
|
||||
# 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(ExternalProject)
|
||||
|
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set(FAISS_PROJECT external_faiss_download)
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set(FAISS_FILENAME faiss)
|
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set(FAISS_PREFIX_DIR ${THIRD_LIBS_PATH}/${FAISS_FILENAME})
|
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set(FAISS_SOURCE_DIR ${THIRD_LIBS_PATH}/${FAISS_FILENAME}/src/${FAISS_PROJECT})
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set(FAISS_INSTALL_DIR ${THIRD_LIBS_PATH}/install/${FAISS_FILENAME})
|
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set(FAISS_INC_DIR ${FAISS_INSTALL_DIR}/include CACHE PATH "faiss include directory." FORCE)
|
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if(ANDROID)
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set(FAISS_LIB_DIR ${FAISS_INSTALL_DIR}/lib/${ANDROID_ABI} CACHE PATH "faiss lib directory." FORCE)
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else()
|
||||
set(FAISS_LIB_DIR ${FAISS_INSTALL_DIR}/lib CACHE PATH "faiss lib directory." FORCE)
|
||||
endif()
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||||
|
||||
if(NOT WITH_FAISS_STATIC)
|
||||
message(FATAL_ERROR "Not support WITH_FAISS_STATIC=OFF now!")
|
||||
endif()
|
||||
|
||||
set(FAISS_URL_PREFIX "https://bj.bcebos.com/fastdeploy/test")
|
||||
if(ANDROID)
|
||||
# check ABI, toolchain
|
||||
if((NOT ANDROID_ABI MATCHES "armeabi-v7a") AND (NOT ANDROID_ABI MATCHES "arm64-v8a"))
|
||||
message(FATAL_ERROR "FAISS only support armeabi-v7a, arm64-v8a now.")
|
||||
endif()
|
||||
if(NOT ANDROID_TOOLCHAIN MATCHES "clang")
|
||||
message(FATAL_ERROR "Currently, only support clang toolchain but found ${ANDROID_TOOLCHAIN}.")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
set(FAISS_VERSION 1.7.3)
|
||||
# URL
|
||||
if(NOT FAISS_URL)
|
||||
if(WIN32)
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-win-x64-${FAISS_VERSION}.zip")
|
||||
elseif(APPLE)
|
||||
if(CURRENT_OSX_ARCH MATCHES "arm64")
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-osx-arm64-${FAISS_VERSION}.tgz")
|
||||
else()
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-osx-x64-${FAISS_VERSION}.tgz")
|
||||
endif()
|
||||
elseif(ANDROID)
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-android-${ANDROID_ABI}-${FAISS_VERSION}.tgz")
|
||||
else() # Linux
|
||||
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
|
||||
message(FATAL_ERROR "Not support for Linux aarch64 now!")
|
||||
else()
|
||||
if(WITH_FAISS_GPU)
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-linux-x64-gpu-${FAISS_VERSION}.tgz")
|
||||
else()
|
||||
set(FAISS_URL "${FAISS_URL_PREFIX}/faiss-linux-x64-${FAISS_VERSION}.tgz")
|
||||
endif()
|
||||
endif()
|
||||
endif()
|
||||
endif()
|
||||
|
||||
# FAISS Headers
|
||||
include_directories(${FAISS_INC_DIR})
|
||||
|
||||
# FAISS Libs paths
|
||||
if(WIN32)
|
||||
set(FAISS_LIB "${FAISS_LIB_DIR}/faiss.lib")
|
||||
elseif(APPLE)
|
||||
set(FAISS_LIB "${FAISS_LIB_DIR}/libfaiss.a")
|
||||
elseif(ANDROID)
|
||||
set(FAISS_LIB "${FAISS_LIB_DIR}/libfaiss.a")
|
||||
else() # Linux
|
||||
set(FAISS_LIB "${FAISS_LIB_DIR}/libfaiss.a")
|
||||
endif()
|
||||
|
||||
# Download FAISS
|
||||
if(ANDROID)
|
||||
ExternalProject_Add(
|
||||
${FAISS_PROJECT}
|
||||
${EXTERNAL_PROJECT_LOG_ARGS}
|
||||
URL ${FAISS_URL}
|
||||
PREFIX ${FAISS_PREFIX_DIR}
|
||||
DOWNLOAD_NO_PROGRESS 1
|
||||
CONFIGURE_COMMAND ""
|
||||
BUILD_COMMAND ""
|
||||
UPDATE_COMMAND ""
|
||||
INSTALL_COMMAND
|
||||
${CMAKE_COMMAND} -E remove_directory ${FAISS_INSTALL_DIR} &&
|
||||
${CMAKE_COMMAND} -E make_directory ${FAISS_INSTALL_DIR} &&
|
||||
${CMAKE_COMMAND} -E make_directory ${FAISS_INSTALL_DIR}/lib &&
|
||||
${CMAKE_COMMAND} -E rename ${FAISS_SOURCE_DIR}/lib/ ${FAISS_INSTALL_DIR}/lib/${ANDROID_ABI} &&
|
||||
${CMAKE_COMMAND} -E copy_directory ${FAISS_SOURCE_DIR}/include ${FAISS_INC_DIR}
|
||||
BUILD_BYPRODUCTS ${FAISS_LIB})
|
||||
else()
|
||||
ExternalProject_Add(
|
||||
${FAISS_PROJECT}
|
||||
${EXTERNAL_PROJECT_LOG_ARGS}
|
||||
URL ${FAISS_URL}
|
||||
PREFIX ${FAISS_PREFIX_DIR}
|
||||
DOWNLOAD_NO_PROGRESS 1
|
||||
CONFIGURE_COMMAND ""
|
||||
BUILD_COMMAND ""
|
||||
UPDATE_COMMAND ""
|
||||
INSTALL_COMMAND
|
||||
${CMAKE_COMMAND} -E remove_directory ${FAISS_INSTALL_DIR} &&
|
||||
${CMAKE_COMMAND} -E make_directory ${FAISS_INSTALL_DIR} &&
|
||||
${CMAKE_COMMAND} -E rename ${FAISS_SOURCE_DIR}/lib/ ${FAISS_INSTALL_DIR}/lib &&
|
||||
${CMAKE_COMMAND} -E copy_directory ${FAISS_SOURCE_DIR}/include ${FAISS_INC_DIR}
|
||||
BUILD_BYPRODUCTS ${FAISS_LIB})
|
||||
endif()
|
||||
|
||||
set(FAISS_LIBRARIES)
|
||||
|
||||
add_library(external_faiss STATIC IMPORTED GLOBAL)
|
||||
set_property(TARGET external_faiss PROPERTY IMPORTED_LOCATION ${FAISS_LIB})
|
||||
add_dependencies(external_faiss ${FAISS_PROJECT})
|
||||
|
||||
list(APPEND FAISS_LIBRARIES external_faiss)
|
||||
|
||||
# Add BLAS/LAPACK/OpenBLAS (needed by FAISS)
|
||||
if(WIN32)
|
||||
add_library(external_blas STATIC IMPORTED GLOBAL)
|
||||
set_property(TARGET external_blas PROPERTY IMPORTED_LOCATION ${FAISS_LIB_DIR}/BLAS.lib)
|
||||
add_dependencies(external_blas ${FAISS_PROJECT})
|
||||
list(APPEND FAISS_LIBRARIES external_blas)
|
||||
|
||||
add_library(external_lapack STATIC IMPORTED GLOBAL)
|
||||
set_property(TARGET external_lapack PROPERTY IMPORTED_LOCATION ${FAISS_LIB_DIR}/LAPACK.lib)
|
||||
add_dependencies(external_lapack ${FAISS_PROJECT})
|
||||
list(APPEND FAISS_LIBRARIES external_lapack)
|
||||
elseif(APPLE)
|
||||
find_package(BLAS REQUIRED)
|
||||
list(APPEND FAISS_LIBRARIES ${BLAS_LIBRARIES})
|
||||
|
||||
find_package(LAPACK REQUIRED)
|
||||
list(APPEND FAISS_LIBRARIES ${LAPACK_LIBRARIES})
|
||||
elseif(ANDROID)
|
||||
# OpenBLAS static lib already merged into libfaiss.a
|
||||
message(STATUS "For Android, OpenBLAS static lib was already merged into libfaiss.a")
|
||||
else() # Linux
|
||||
find_package(BLAS REQUIRED)
|
||||
list(APPEND FAISS_LIBRARIES ${BLAS_LIBRARIES})
|
||||
|
||||
find_package(LAPACK REQUIRED)
|
||||
list(APPEND FAISS_LIBRARIES ${LAPACK_LIBRARIES})
|
||||
endif()
|
||||
|
||||
# Add OpenMP (REQUIRED), OpenMP must be avaliable.
|
||||
find_package(OpenMP REQUIRED)
|
||||
list(APPEND FAISS_LIBRARIES OpenMP::OpenMP_CXX)
|
||||
|
||||
set(FAISS_INCLUDE_DIRS ${FAISS_INC_DIR})
|
||||
set(FAISS_LIBS ${FAISS_LIBRARIES})
|
||||
set(FAISS_FOUND TRUE)
|
||||
|
||||
|
||||
5
examples/vision/classification/ppshitu/cpu-gpu/README.md
Normal file
5
examples/vision/classification/ppshitu/cpu-gpu/README.md
Normal file
@@ -0,0 +1,5 @@
|
||||
[English](README.md) | 简体中文
|
||||
|
||||
## 详细部署的部署示例
|
||||
- [Python部署](python)
|
||||
- [C++部署](cpp)
|
||||
@@ -0,0 +1,14 @@
|
||||
PROJECT(infer_demo C CXX)
|
||||
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||||
|
||||
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
||||
|
||||
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
||||
|
||||
include_directories(${FASTDEPLOY_INCS})
|
||||
|
||||
add_executable(infer_ppshituv2_det_demo ${PROJECT_SOURCE_DIR}/infer_ppshituv2_det.cc)
|
||||
add_executable(infer_ppshituv2_rec_demo ${PROJECT_SOURCE_DIR}/infer_ppshituv2_rec.cc)
|
||||
|
||||
target_link_libraries(infer_ppshituv2_det_demo ${FASTDEPLOY_LIBS})
|
||||
target_link_libraries(infer_ppshituv2_rec_demo ${FASTDEPLOY_LIBS})
|
||||
89
examples/vision/classification/ppshitu/cpu-gpu/cpp/README.md
Executable file
89
examples/vision/classification/ppshitu/cpu-gpu/cpp/README.md
Executable file
@@ -0,0 +1,89 @@
|
||||
# PaddleClas CPU-GPU C++部署示例
|
||||
|
||||
本目录下提供`infer_shituv2_xxx.cc`快速完成PP-ShiTuV2系列模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
||||
|
||||
## 1. 说明
|
||||
PP-ShiTuV2支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型.
|
||||
|
||||
## 2. 部署环境准备
|
||||
在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
|
||||
|
||||
## 3. 运行部署示例
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
|
||||
|
||||
```bash
|
||||
# 下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/classification/ppshitu/cpu-gpu/cpp
|
||||
|
||||
mkdir build
|
||||
cd build
|
||||
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
||||
make -j
|
||||
|
||||
# 下载模型文件和测试图片
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
|
||||
|
||||
# 在CPU上使用Paddle Inference推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 0
|
||||
# 在CPU上使用OenVINO推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 1
|
||||
# 在CPU上使用ONNX Runtime推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 2
|
||||
# 在CPU上使用Paddle Lite推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 3
|
||||
# 在GPU上使用Paddle Inference推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 4
|
||||
# 在GPU上使用Paddle TensorRT推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 5
|
||||
# 在GPU上使用ONNX Runtime推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 6
|
||||
# 在GPU上使用Nvidia TensorRT推理
|
||||
./infer_ppshituv2_det_demo picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer drink_dataset_v2.0/test_images/100.jpeg 7
|
||||
```
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
|
||||
## 4. 部署示例选项说明
|
||||
在我们使用`infer_ppshituv2_det_demo`时, 输入了3个参数, 分别为分类模型, 预测图片, 与最后一位的数字选项.
|
||||
现在下表将解释最后一位数字选项的含义.
|
||||
|数字选项|含义|
|
||||
|:---:|:---:|
|
||||
|0| 在CPU上使用Paddle Inference推理 |
|
||||
|1| 在CPU上使用OenVINO推理 |
|
||||
|2| 在CPU上使用ONNX Runtime推理 |
|
||||
|3| 在CPU上使用Paddle Lite推理 |
|
||||
|4| 在GPU上使用Paddle Inference推理 |
|
||||
|5| 在GPU上使用Paddle TensorRT推理 |
|
||||
|6| 在GPU上使用ONNX Runtime推理 |
|
||||
|7| 在GPU上使用Nvidia TensorRT推理 |
|
||||
|
||||
- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
## 5. 更多指南
|
||||
- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
|
||||
- [PaddleClas Python部署](../python)
|
||||
- [PaddleClas C 部署](../c)
|
||||
- [PaddleClas C# 部署](../csharp)
|
||||
|
||||
## 6. 常见问题
|
||||
- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
|硬件类型|支持的后端|
|
||||
|:---:|:---:|
|
||||
|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
|
||||
|ARM CPU| Paddle Lite |
|
||||
|飞腾 CPU| ONNX Runtime |
|
||||
|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
|
||||
|
||||
- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
|
||||
- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
|
||||
- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
|
||||
- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
|
||||
@@ -0,0 +1,96 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision.h"
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void InitAndInfer(const std::string &model_dir, const std::string &image_file,
|
||||
const fastdeploy::RuntimeOption &option) {
|
||||
auto model_file = model_dir + sep + "inference.pdmodel";
|
||||
auto params_file = model_dir + sep + "inference.pdiparams";
|
||||
auto config_file = model_dir + sep + "infer_cfg.yml";
|
||||
|
||||
auto model = fastdeploy::vision::classification::PPShiTuV2Detector(
|
||||
model_file, params_file, config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
// print res
|
||||
std::cout << res.Str() << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
if (argc < 4) {
|
||||
std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
|
||||
"e.g ./infer_demo "
|
||||
"./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer ./test.jpeg 0"
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[3]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
option.UsePaddleBackend(); // Paddle Inference
|
||||
} else if (flag == 1) {
|
||||
option.UseCpu();
|
||||
option.UseOpenVINOBackend(); // OpenVINO
|
||||
} else if (flag == 2) {
|
||||
option.UseCpu();
|
||||
option.UseOrtBackend(); // ONNX Runtime
|
||||
} else if (flag == 3) {
|
||||
option.UseCpu();
|
||||
option.UseLiteBackend(); // Paddle Lite
|
||||
} else if (flag == 4) {
|
||||
option.UseGpu();
|
||||
option.UsePaddleBackend(); // Paddle Inference
|
||||
} else if (flag == 5) {
|
||||
option.UseGpu();
|
||||
option.UsePaddleInferBackend();
|
||||
option.paddle_infer_option.enable_trt = true;
|
||||
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});
|
||||
} else if (flag == 6) {
|
||||
option.UseGpu();
|
||||
option.UseOrtBackend(); // ONNX Runtime
|
||||
} else if (flag == 7) {
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend(); // TensorRT
|
||||
}
|
||||
|
||||
std::string model_dir = argv[1];
|
||||
std::string image_dir = argv[2];
|
||||
InitAndInfer(model_dir, image_dir, option);
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision.h"
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void InitAndInfer(const std::string &model_dir, const std::string &image_file,
|
||||
const fastdeploy::RuntimeOption &option) {
|
||||
auto model_file = model_dir + sep + "inference.pdmodel";
|
||||
auto params_file = model_dir + sep + "inference.pdiparams";
|
||||
auto config_file = model_dir + sep + "inference_cls.yaml";
|
||||
|
||||
auto model = fastdeploy::vision::classification::PPShiTuV2Recognizer(
|
||||
model_file, params_file, config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::ClassifyResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
// print res
|
||||
std::cout << res.Str() << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
if (argc < 4) {
|
||||
std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
|
||||
"e.g ./infer_demo "
|
||||
"./general_PPLCNetV2_base_pretrained_v1.0_infer ./test.jpeg 0"
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[3]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
option.UsePaddleBackend(); // Paddle Inference
|
||||
} else if (flag == 1) {
|
||||
option.UseCpu();
|
||||
option.UseOpenVINOBackend(); // OpenVINO
|
||||
} else if (flag == 2) {
|
||||
option.UseCpu();
|
||||
option.UseOrtBackend(); // ONNX Runtime
|
||||
} else if (flag == 3) {
|
||||
option.UseCpu();
|
||||
option.UseLiteBackend(); // Paddle Lite
|
||||
} else if (flag == 4) {
|
||||
option.UseGpu();
|
||||
option.UsePaddleBackend(); // Paddle Inference
|
||||
} else if (flag == 5) {
|
||||
option.UseGpu();
|
||||
option.UsePaddleInferBackend();
|
||||
option.paddle_infer_option.enable_trt = true;
|
||||
} else if (flag == 6) {
|
||||
option.UseGpu();
|
||||
option.UseOrtBackend(); // ONNX Runtime
|
||||
} else if (flag == 7) {
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend(); // TensorRT
|
||||
}
|
||||
|
||||
std::string model_dir = argv[1];
|
||||
std::string image_dir = argv[2];
|
||||
InitAndInfer(model_dir, image_dir, option);
|
||||
}
|
||||
75
examples/vision/classification/ppshitu/cpu-gpu/python/README.md
Executable file
75
examples/vision/classification/ppshitu/cpu-gpu/python/README.md
Executable file
@@ -0,0 +1,75 @@
|
||||
# PaddleClas CPU-GPU Python部署示例
|
||||
本目录下提供`infer_ppshituv2_det.py`快速完成PP-ShiTuV2在CPU/GPU上部署的示例.
|
||||
|
||||
## 1. 说明
|
||||
PP-ShiTuV2支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型
|
||||
|
||||
## 2. 部署环境准备
|
||||
在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
|
||||
|
||||
## 3. 运行部署示例
|
||||
```bash
|
||||
# 安装FastDpeloy python包（详细文档请参考`部署环境准备`）
|
||||
pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
|
||||
conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
|
||||
|
||||
# 下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/classification/ppshitu/cpu-gpu/python
|
||||
|
||||
# 下载模型文件和测试图片
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
|
||||
|
||||
# 在CPU上使用Paddle Inference推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend paddle
|
||||
# 在CPU上使用OenVINO推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend openvino
|
||||
# 在CPU上使用ONNX Runtime推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend ort
|
||||
# 在CPU上使用Paddle Lite推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend pplite
|
||||
# 在GPU上使用Paddle Inference推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend paddle
|
||||
# 在GPU上使用Paddle TensorRT推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend pptrt
|
||||
# 在GPU上使用ONNX Runtime推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend ort
|
||||
# 在GPU上使用Nvidia TensorRT推理
|
||||
python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend trt
|
||||
```
|
||||
|
||||
## 4. 部署示例选项说明
|
||||
|
||||
|参数|含义|默认值
|
||||
|---|---|---|
|
||||
|--model|指定模型文件夹所在的路径|None|
|
||||
|--image|指定测试图片所在的路径|None|
|
||||
|--device|指定即将运行的硬件类型，支持的值为`[cpu, gpu]`，当设置为cpu时，可运行在x86 cpu/arm cpu等cpu上|cpu|
|
||||
|--device_id|使用gpu时, 指定设备号|0|
|
||||
|--backend|部署模型时使用的后端, 支持的值为`[paddle,pptrt,pplite,ort,openvino,trt]` |openvino|
|
||||
|--topk|返回的前topk准确率, 支持的为`1,5` |1|
|
||||
|
||||
关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
## 5. 更多指南
|
||||
- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
|
||||
- [PaddleClas C++ 部署](../cpp)
|
||||
- [PaddleClas C 部署](../c)
|
||||
- [PaddleClas C# 部署](../csharp)
|
||||
|
||||
## 6. 常见问题
|
||||
- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
|硬件类型|支持的后端|
|
||||
|:---:|:---:|
|
||||
|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
|
||||
|ARM CPU| Paddle Lite |
|
||||
|飞腾 CPU| ONNX Runtime |
|
||||
|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
|
||||
|
||||
- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
|
||||
- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
|
||||
- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
|
||||
- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
|
||||
96
examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_det.py
Executable file
96
examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_det.py
Executable file
@@ -0,0 +1,96 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", required=True, help="Path of PP-ShiTuV2 detector model.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
parser.add_argument(
|
||||
"--backend",
|
||||
type=str,
|
||||
default="default",
|
||||
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu(args.device_id)
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
|
||||
elif args.backend.lower() == "pptrt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
|
||||
option.use_paddle_infer_backend()
|
||||
option.paddle_infer_option.enable_trt = True
|
||||
option.paddle_infer_option.collect_trt_shape = True
|
||||
option.trt_option.set_shape("image", [1, 3, 640, 640],
|
||||
[1, 3, 640, 640], [1, 3, 640, 640])
|
||||
option.trt_option.set_shape("scale_factor", [1, 2], [1, 2], [1, 2])
|
||||
option.trt_option.set_shape("im_shape", [1, 2], [1, 2], [1, 2])
|
||||
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_infer_backend()
|
||||
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
|
||||
elif args.backend.lower() == "pplite":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "Paddle Lite backend require inference on device CPU."
|
||||
option.use_lite_backend()
|
||||
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# 配置runtime，加载模型
|
||||
runtime_option = build_option(args)
|
||||
|
||||
model_file = os.path.join(args.model, "inference.pdmodel")
|
||||
params_file = os.path.join(args.model, "inference.pdiparams")
|
||||
config_file = os.path.join(args.model, "infer_cfg.yml")
|
||||
model = fd.vision.classification.PPShiTuV2Detector(
|
||||
model_file, params_file, config_file, runtime_option=runtime_option)
|
||||
|
||||
# 预测主体检测结果
|
||||
im = cv2.imread(args.image)
|
||||
result = model.predict(im)
|
||||
|
||||
# 预测结果可视化
|
||||
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
|
||||
print(result)
|
||||
85
examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_rec.py
Executable file
85
examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_rec.py
Executable file
@@ -0,0 +1,85 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", required=True, help="Path of PP-ShiTuV2 recognizer model.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
parser.add_argument(
|
||||
"--backend",
|
||||
type=str,
|
||||
default="default",
|
||||
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu(args.device_id)
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
|
||||
elif args.backend.lower() == "pptrt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
|
||||
option.use_paddle_infer_backend()
|
||||
option.paddle_infer_option.enable_trt = True
|
||||
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_infer_backend()
|
||||
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
|
||||
elif args.backend.lower() == "pplite":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "Paddle Lite backend require inference on device CPU."
|
||||
option.use_lite_backend()
|
||||
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# 配置runtime，加载模型
|
||||
runtime_option = build_option(args)
|
||||
|
||||
model_file = os.path.join(args.model, "inference.pdmodel")
|
||||
params_file = os.path.join(args.model, "inference.pdiparams")
|
||||
config_file = os.path.join(args.model, "inference_cls.yaml")
|
||||
model = fd.vision.classification.PPShiTuV2Recognizer(
|
||||
model_file, params_file, config_file, runtime_option=runtime_option)
|
||||
|
||||
# 预测特征抽取结果
|
||||
im = cv2.imread(args.image)
|
||||
result = model.predict(im)
|
||||
print(result)
|
||||
@@ -18,6 +18,8 @@
|
||||
#include "fastdeploy/vision/classification/contrib/resnet.h"
|
||||
#include "fastdeploy/vision/classification/contrib/yolov5cls/yolov5cls.h"
|
||||
#include "fastdeploy/vision/classification/ppcls/model.h"
|
||||
#include "fastdeploy/vision/classification/ppshitu/ppshituv2_rec.h"
|
||||
#include "fastdeploy/vision/classification/ppshitu/ppshituv2_det.h"
|
||||
#include "fastdeploy/vision/detection/contrib/nanodet_plus.h"
|
||||
#include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
|
||||
#include "fastdeploy/vision/detection/contrib/yolor.h"
|
||||
|
||||
@@ -18,12 +18,17 @@ namespace fastdeploy {
|
||||
|
||||
void BindYOLOv5Cls(pybind11::module& m);
|
||||
void BindPaddleClas(pybind11::module& m);
|
||||
void BindPPShiTuV2(pybind11::module& m);
|
||||
void BindResNet(pybind11::module& m);
|
||||
|
||||
void BindClassification(pybind11::module& m) {
|
||||
auto classification_module =
|
||||
m.def_submodule("classification", "Image classification models.");
|
||||
|
||||
BindYOLOv5Cls(classification_module);
|
||||
BindPaddleClas(classification_module);
|
||||
BindPPShiTuV2(classification_module);
|
||||
BindResNet(classification_module);
|
||||
}
|
||||
|
||||
} // namespace fastdeploy
|
||||
|
||||
101
fastdeploy/vision/classification/ppshitu/ppshitu_pybind.cc
Normal file
101
fastdeploy/vision/classification/ppshitu/ppshitu_pybind.cc
Normal file
@@ -0,0 +1,101 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/pybind/main.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
void BindPPShiTuV2(pybind11::module& m) {
|
||||
pybind11::class_<vision::classification::PPShiTuV2RecognizerPreprocessor,
|
||||
vision::ProcessorManager>(m,
|
||||
"PPShiTuV2RecognizerPreprocessor")
|
||||
.def(pybind11::init<std::string>())
|
||||
.def("disable_normalize",
|
||||
[](vision::classification::PPShiTuV2RecognizerPreprocessor& self) {
|
||||
self.DisableNormalize();
|
||||
})
|
||||
.def("disable_permute",
|
||||
[](vision::classification::PPShiTuV2RecognizerPreprocessor& self) {
|
||||
self.DisablePermute();
|
||||
})
|
||||
.def("initial_resize_on_cpu",
|
||||
[](vision::classification::PPShiTuV2RecognizerPreprocessor& self,
|
||||
bool v) { self.InitialResizeOnCpu(v); });
|
||||
|
||||
pybind11::class_<vision::classification::PPShiTuV2RecognizerPostprocessor>(
|
||||
m, "PPShiTuV2RecognizerPostprocessor")
|
||||
.def(pybind11::init<>())
|
||||
.def("run",
|
||||
[](vision::classification::PPShiTuV2RecognizerPostprocessor& self,
|
||||
std::vector<FDTensor>& inputs) {
|
||||
std::vector<vision::ClassifyResult> results;
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error(
|
||||
"Failed to postprocess the runtime result in "
|
||||
"PPShiTuV2RecognizerPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def("run",
|
||||
[](vision::classification::PPShiTuV2RecognizerPostprocessor& self,
|
||||
std::vector<pybind11::array>& input_array) {
|
||||
std::vector<vision::ClassifyResult> results;
|
||||
std::vector<FDTensor> inputs;
|
||||
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error(
|
||||
"Failed to postprocess the runtime result in "
|
||||
"PPShiTuV2RecognizerPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def_property("feature_norm",
|
||||
&vision::classification::PPShiTuV2RecognizerPostprocessor::
|
||||
GetFeatureNorm,
|
||||
&vision::classification::PPShiTuV2RecognizerPostprocessor::
|
||||
SetFeatureNorm);
|
||||
|
||||
pybind11::class_<vision::classification::PPShiTuV2Recognizer,
|
||||
FastDeployModel>(m, "PPShiTuV2Recognizer")
|
||||
.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
|
||||
ModelFormat>())
|
||||
.def("clone",
|
||||
[](vision::classification::PPShiTuV2Recognizer& self) {
|
||||
return self.Clone();
|
||||
})
|
||||
.def("predict",
|
||||
[](vision::classification::PPShiTuV2Recognizer& self,
|
||||
pybind11::array& data) {
|
||||
cv::Mat im = PyArrayToCvMat(data);
|
||||
vision::ClassifyResult result;
|
||||
self.Predict(im, &result);
|
||||
return result;
|
||||
})
|
||||
.def("batch_predict",
|
||||
[](vision::classification::PPShiTuV2Recognizer& self,
|
||||
std::vector<pybind11::array>& data) {
|
||||
std::vector<cv::Mat> images;
|
||||
for (size_t i = 0; i < data.size(); ++i) {
|
||||
images.push_back(PyArrayToCvMat(data[i]));
|
||||
}
|
||||
std::vector<vision::ClassifyResult> results;
|
||||
self.BatchPredict(images, &results);
|
||||
return results;
|
||||
})
|
||||
.def_property_readonly(
|
||||
"preprocessor",
|
||||
&vision::classification::PPShiTuV2Recognizer::GetPreprocessor)
|
||||
.def_property_readonly(
|
||||
"postprocessor",
|
||||
&vision::classification::PPShiTuV2Recognizer::GetPostprocessor);
|
||||
}
|
||||
} // namespace fastdeploy
|
||||
26
fastdeploy/vision/classification/ppshitu/ppshituv2_det.h
Normal file
26
fastdeploy/vision/classification/ppshitu/ppshituv2_det.h
Normal file
@@ -0,0 +1,26 @@
|
||||
// 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.
|
||||
#pragma once
|
||||
#include "fastdeploy/vision/detection/ppdet/model.h"
|
||||
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
|
||||
typedef detection::PicoDet PPShiTuV2Detector;
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
121
fastdeploy/vision/classification/ppshitu/ppshituv2_rec.cc
Normal file
121
fastdeploy/vision/classification/ppshitu/ppshituv2_rec.cc
Normal file
@@ -0,0 +1,121 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision/classification/ppshitu/ppshituv2_rec.h"
|
||||
|
||||
#include "fastdeploy/utils/unique_ptr.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
|
||||
PPShiTuV2Recognizer::PPShiTuV2Recognizer(const std::string& model_file,
|
||||
const std::string& params_file,
|
||||
const std::string& config_file,
|
||||
const RuntimeOption& custom_option,
|
||||
const ModelFormat& model_format)
|
||||
: preprocessor_(config_file) {
|
||||
if (model_format == ModelFormat::PADDLE) {
|
||||
valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::ORT,
|
||||
Backend::LITE};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
|
||||
valid_timvx_backends = {Backend::LITE};
|
||||
valid_ascend_backends = {Backend::LITE};
|
||||
valid_kunlunxin_backends = {Backend::LITE};
|
||||
valid_ipu_backends = {Backend::PDINFER};
|
||||
valid_directml_backends = {Backend::ORT};
|
||||
} else if (model_format == ModelFormat::SOPHGO) {
|
||||
valid_sophgonpu_backends = {Backend::SOPHGOTPU};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
valid_directml_backends = {Backend::ORT};
|
||||
}
|
||||
|
||||
runtime_option = custom_option;
|
||||
runtime_option.model_format = model_format;
|
||||
runtime_option.model_file = model_file;
|
||||
runtime_option.params_file = params_file;
|
||||
initialized = Initialize();
|
||||
}
|
||||
|
||||
std::unique_ptr<PPShiTuV2Recognizer> PPShiTuV2Recognizer::Clone() const {
|
||||
std::unique_ptr<PPShiTuV2Recognizer> clone_model =
|
||||
utils::make_unique<PPShiTuV2Recognizer>(PPShiTuV2Recognizer(*this));
|
||||
clone_model->SetRuntime(clone_model->CloneRuntime());
|
||||
return clone_model;
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::Initialize() {
|
||||
if (!InitRuntime()) {
|
||||
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::Predict(cv::Mat* im, ClassifyResult* result) {
|
||||
if (!Predict(*im, result)) {
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::Predict(const cv::Mat& im, ClassifyResult* result) {
|
||||
FDMat mat = WrapMat(im);
|
||||
return Predict(mat, result);
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::BatchPredict(const std::vector<cv::Mat>& images,
|
||||
std::vector<ClassifyResult>* results) {
|
||||
std::vector<FDMat> mats = WrapMat(images);
|
||||
return BatchPredict(mats, results);
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::Predict(const FDMat& mat, ClassifyResult* result) {
|
||||
std::vector<ClassifyResult> results;
|
||||
std::vector<FDMat> mats = {mat};
|
||||
if (!BatchPredict(mats, &results)) {
|
||||
return false;
|
||||
}
|
||||
*result = std::move(results[0]);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool PPShiTuV2Recognizer::BatchPredict(const std::vector<FDMat>& mats,
|
||||
std::vector<ClassifyResult>* results) {
|
||||
std::vector<FDMat> fd_mats = mats;
|
||||
if (!preprocessor_.Run(&fd_mats, &reused_input_tensors_)) {
|
||||
FDERROR << "Failed to preprocess the input image." << std::endl;
|
||||
return false;
|
||||
}
|
||||
reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
|
||||
if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
|
||||
FDERROR << "Failed to inference by runtime." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!postprocessor_.Run(reused_output_tensors_, results)) {
|
||||
FDERROR << "Failed to postprocess the inference results by runtime."
|
||||
<< std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
110
fastdeploy/vision/classification/ppshitu/ppshituv2_rec.h
Normal file
110
fastdeploy/vision/classification/ppshitu/ppshituv2_rec.h
Normal file
@@ -0,0 +1,110 @@
|
||||
// Copyright (c) 2022 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.
|
||||
|
||||
#pragma once
|
||||
#include "fastdeploy/fastdeploy_model.h"
|
||||
#include "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_preprocessor.h"
|
||||
#include "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_postprocessor.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
/*! @brief PPShiTuV2Recognizer model object used when to load a PPShiTuV2Recognizer model exported by PP-ShiTuV2 Rec model.
|
||||
*/
|
||||
class FASTDEPLOY_DECL PPShiTuV2Recognizer : public FastDeployModel {
|
||||
public:
|
||||
/** \brief Set path of model file and configuration file, and the configuration of runtime
|
||||
*
|
||||
* \param[in] model_file Path of model file, e.g PPLCNet/inference.pdmodel
|
||||
* \param[in] params_file Path of parameter file, e.g PPLCNet/inference.pdiparams, if the model format is ONNX, this parameter will be ignored
|
||||
* \param[in] config_file Path of configuration file for deployment, e.g PPLCNet/inference_cls.yml
|
||||
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format
|
||||
*/
|
||||
PPShiTuV2Recognizer(const std::string& model_file,
|
||||
const std::string& params_file,
|
||||
const std::string& config_file,
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE);
|
||||
|
||||
/** \brief Clone a new PPShiTuV2Recognizer with less memory usage when multiple instances of the same model are created
|
||||
*
|
||||
* \return new PPShiTuV2Recognizer* type unique pointer
|
||||
*/
|
||||
virtual std::unique_ptr<PPShiTuV2Recognizer> Clone() const;
|
||||
|
||||
/// Get model's name
|
||||
virtual std::string ModelName() const { return "PPShiTuV2Recognizer"; }
|
||||
|
||||
/** \brief DEPRECATED Predict the feature vector result for an input image, remove at 1.0 version
|
||||
*
|
||||
* \param[in] im The input image data, comes from cv::imread()
|
||||
* \param[in] result The output feature vector result will be writen to this structure
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool Predict(cv::Mat* im, ClassifyResult* result);
|
||||
|
||||
/** \brief Predict the classification result for an input image
|
||||
*
|
||||
* \param[in] img The input image data, comes from cv::imread()
|
||||
* \param[in] result The output feature vector result
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool Predict(const cv::Mat& img, ClassifyResult* result);
|
||||
|
||||
/** \brief Predict the feature vector results for a batch of input images
|
||||
*
|
||||
* \param[in] imgs, The input image list, each element comes from cv::imread()
|
||||
* \param[in] results The output feature vector(namely ClassifyResult.feature) result list
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool BatchPredict(const std::vector<cv::Mat>& imgs,
|
||||
std::vector<ClassifyResult>* results);
|
||||
|
||||
/** \brief Predict the feature vector result for an input image
|
||||
*
|
||||
* \param[in] mat The input mat
|
||||
* \param[in] result The output feature vector result
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool Predict(const FDMat& mat, ClassifyResult* result);
|
||||
|
||||
/** \brief Predict the feature vector results for a batch of input images
|
||||
*
|
||||
* \param[in] mats, The input mat list
|
||||
* \param[in] results The output feature vector result list
|
||||
* \return true if the prediction successed, otherwise false
|
||||
*/
|
||||
virtual bool BatchPredict(const std::vector<FDMat>& mats,
|
||||
std::vector<ClassifyResult>* results);
|
||||
|
||||
/// Get preprocessor reference of PPShiTuV2Recognizer
|
||||
virtual PPShiTuV2RecognizerPreprocessor& GetPreprocessor() {
|
||||
return preprocessor_;
|
||||
}
|
||||
|
||||
/// Get postprocessor reference of PPShiTuV2Recognizer
|
||||
virtual PPShiTuV2RecognizerPostprocessor& GetPostprocessor() {
|
||||
return postprocessor_;
|
||||
}
|
||||
|
||||
protected:
|
||||
bool Initialize();
|
||||
PPShiTuV2RecognizerPreprocessor preprocessor_;
|
||||
PPShiTuV2RecognizerPostprocessor postprocessor_;
|
||||
};
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
@@ -0,0 +1,58 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_postprocessor.h"
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include "fastdeploy/vision/utils/utils.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
|
||||
bool PPShiTuV2RecognizerPostprocessor::Run(
|
||||
const std::vector<FDTensor>& tensors,
|
||||
std::vector<ClassifyResult>* results) {
|
||||
int batch = tensors[0].shape[0]; // e.g [batch, 512]
|
||||
int num_feature = tensors[0].shape[1];
|
||||
const float* tensor_data = reinterpret_cast<const float*>(tensors[0].Data());
|
||||
|
||||
results->resize(batch);
|
||||
|
||||
// post processing per batch=1
|
||||
for (int i = 0; i < batch; ++i) {
|
||||
(*results)[i].feature.resize(num_feature);
|
||||
const float* tensor_data_i_start = tensor_data + i * num_feature;
|
||||
std::memcpy((*results)[i].feature.data(), tensor_data_i_start,
|
||||
num_feature * sizeof(float));
|
||||
if (feature_norm_) {
|
||||
FeatureNorm((*results)[i].feature);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void PPShiTuV2RecognizerPostprocessor::FeatureNorm(
|
||||
std::vector<float>& feature) {
|
||||
float feature_sqrt = std::sqrt(std::inner_product(
|
||||
feature.begin(), feature.end(), feature.begin(), 0.0f));
|
||||
for (int i = 0; i < feature.size(); ++i) {
|
||||
feature[i] /= feature_sqrt;
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
@@ -0,0 +1,49 @@
|
||||
// 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.
|
||||
|
||||
#pragma once
|
||||
#include "fastdeploy/vision/common/processors/transform.h"
|
||||
#include "fastdeploy/vision/common/result.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
|
||||
namespace classification {
|
||||
/*! @brief Postprocessor object for PP-ShiTuV2 Recognizer model.
|
||||
*/
|
||||
class FASTDEPLOY_DECL PPShiTuV2RecognizerPostprocessor {
|
||||
public:
|
||||
PPShiTuV2RecognizerPostprocessor() = default;
|
||||
|
||||
/** \brief Process the result of runtime and fill to ClassifyResult structure
|
||||
*
|
||||
* \param[in] tensors The inference result from runtime
|
||||
* \param[in] result The output result of feature vector (see ClassifyResult.feature member)
|
||||
* \return true if the postprocess successed, otherwise false
|
||||
*/
|
||||
bool Run(const std::vector<FDTensor>& tensors,
|
||||
std::vector<ClassifyResult>* results);
|
||||
/// Set the value of feature_norm_ for Postprocessor
|
||||
void SetFeatureNorm(bool feature_norm) { feature_norm_ = feature_norm; }
|
||||
/// Get the value of feature_norm_ from Postprocessor, default to true.
|
||||
bool GetFeatureNorm() { return feature_norm_; }
|
||||
|
||||
private:
|
||||
void FeatureNorm(std::vector<float> &feature);
|
||||
bool feature_norm_ = true;
|
||||
};
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
@@ -0,0 +1,152 @@
|
||||
// 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 "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_preprocessor.h"
|
||||
|
||||
#include "yaml-cpp/yaml.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
|
||||
PPShiTuV2RecognizerPreprocessor::PPShiTuV2RecognizerPreprocessor(
|
||||
const std::string& config_file) {
|
||||
this->config_file_ = config_file;
|
||||
FDASSERT(BuildPreprocessPipelineFromConfig(),
|
||||
"Failed to create PPShiTuV2RecognizerPreprocessor.");
|
||||
initialized_ = true;
|
||||
}
|
||||
|
||||
bool PPShiTuV2RecognizerPreprocessor::BuildPreprocessPipelineFromConfig() {
|
||||
processors_.clear();
|
||||
YAML::Node cfg;
|
||||
try {
|
||||
cfg = YAML::LoadFile(config_file_);
|
||||
} catch (YAML::BadFile& e) {
|
||||
FDERROR << "Failed to load yaml file " << config_file_
|
||||
<< ", maybe you should check this file." << std::endl;
|
||||
return false;
|
||||
}
|
||||
auto preprocess_cfg = cfg["PreProcess"]["transform_ops"];
|
||||
processors_.push_back(std::make_shared<BGR2RGB>());
|
||||
for (const auto& op : preprocess_cfg) {
|
||||
FDASSERT(op.IsMap(),
|
||||
"Require the transform information in yaml be Map type.");
|
||||
auto op_name = op.begin()->first.as<std::string>();
|
||||
if (op_name == "ResizeImage") {
|
||||
if (op.begin()->second["resize_short"]) {
|
||||
int target_size = op.begin()->second["resize_short"].as<int>();
|
||||
bool use_scale = false;
|
||||
int interp = 1;
|
||||
processors_.push_back(
|
||||
std::make_shared<ResizeByShort>(target_size, 1, use_scale));
|
||||
} else if (op.begin()->second["size"]) {
|
||||
int width = 0;
|
||||
int height = 0;
|
||||
if (op.begin()->second["size"].IsScalar()) {
|
||||
auto size = op.begin()->second["size"].as<int>();
|
||||
width = size;
|
||||
height = size;
|
||||
} else {
|
||||
auto size = op.begin()->second["size"].as<std::vector<int>>();
|
||||
width = size[0];
|
||||
height = size[1];
|
||||
}
|
||||
processors_.push_back(
|
||||
std::make_shared<Resize>(width, height, -1.0, -1.0, 1, false));
|
||||
} else {
|
||||
FDERROR << "Invalid params for ResizeImage for both 'size' and "
|
||||
"'resize_short' are None"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
} else if (op_name == "CropImage") {
|
||||
int width = op.begin()->second["size"].as<int>();
|
||||
int height = op.begin()->second["size"].as<int>();
|
||||
processors_.push_back(std::make_shared<CenterCrop>(width, height));
|
||||
} else if (op_name == "NormalizeImage") {
|
||||
if (!disable_normalize_) {
|
||||
auto mean = op.begin()->second["mean"].as<std::vector<float>>();
|
||||
auto std = op.begin()->second["std"].as<std::vector<float>>();
|
||||
auto scale = op.begin()->second["scale"].as<float>();
|
||||
FDASSERT(
|
||||
(scale - 0.00392157) < 1e-06 && (scale - 0.00392157) > -1e-06,
|
||||
"Only support scale in Normalize be 0.00392157, means the pixel "
|
||||
"is in range of [0, 255].");
|
||||
processors_.push_back(std::make_shared<Normalize>(mean, std));
|
||||
}
|
||||
} else if (op_name == "ToCHWImage") {
|
||||
if (!disable_permute_) {
|
||||
processors_.push_back(std::make_shared<HWC2CHW>());
|
||||
}
|
||||
} else {
|
||||
FDERROR << "Unexcepted preprocess operator: " << op_name << "."
|
||||
<< std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
// Fusion will improve performance
|
||||
FuseTransforms(&processors_);
|
||||
return true;
|
||||
}
|
||||
|
||||
void PPShiTuV2RecognizerPreprocessor::DisableNormalize() {
|
||||
this->disable_normalize_ = true;
|
||||
// the DisableNormalize function will be invalid if the configuration file is
|
||||
// loaded during preprocessing
|
||||
if (!BuildPreprocessPipelineFromConfig()) {
|
||||
FDERROR << "Failed to build preprocess pipeline from configuration file."
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
void PPShiTuV2RecognizerPreprocessor::DisablePermute() {
|
||||
this->disable_permute_ = true;
|
||||
// the DisablePermute function will be invalid if the configuration file is
|
||||
// loaded during preprocessing
|
||||
if (!BuildPreprocessPipelineFromConfig()) {
|
||||
FDERROR << "Failed to build preprocess pipeline from configuration file."
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
bool PPShiTuV2RecognizerPreprocessor::Apply(FDMatBatch* image_batch,
|
||||
std::vector<FDTensor>* outputs) {
|
||||
if (!initialized_) {
|
||||
FDERROR << "The preprocessor is not initialized." << std::endl;
|
||||
return false;
|
||||
}
|
||||
for (size_t j = 0; j < processors_.size(); ++j) {
|
||||
image_batch->proc_lib = proc_lib_;
|
||||
if (initial_resize_on_cpu_ && j == 0 &&
|
||||
processors_[j]->Name().find("Resize") == 0) {
|
||||
image_batch->proc_lib = ProcLib::OPENCV;
|
||||
}
|
||||
if (!(*(processors_[j].get()))(image_batch)) {
|
||||
FDERROR << "Failed to processs image in " << processors_[j]->Name() << "."
|
||||
<< std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
outputs->resize(1);
|
||||
FDTensor* tensor = image_batch->Tensor();
|
||||
(*outputs)[0].SetExternalData(tensor->Shape(), tensor->Dtype(),
|
||||
tensor->Data(), tensor->device,
|
||||
tensor->device_id);
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
@@ -0,0 +1,72 @@
|
||||
// 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.
|
||||
#pragma once
|
||||
#include "fastdeploy/vision/common/processors/manager.h"
|
||||
#include "fastdeploy/vision/common/processors/transform.h"
|
||||
#include "fastdeploy/vision/common/result.h"
|
||||
|
||||
namespace fastdeploy {
|
||||
namespace vision {
|
||||
namespace classification {
|
||||
|
||||
/*! @brief Preprocessor object for PP-ShiTuV2 Recognizer model.
|
||||
*/
|
||||
class FASTDEPLOY_DECL PPShiTuV2RecognizerPreprocessor : public ProcessorManager {
|
||||
public:
|
||||
/** \brief Create a preprocessor instance for PP-ShiTuV2 Recognizer model
|
||||
*
|
||||
* \param[in] config_file Path of configuration file for deployment, e.g PPLCNet/infer_cfg.yml
|
||||
*/
|
||||
explicit PPShiTuV2RecognizerPreprocessor(const std::string& config_file);
|
||||
|
||||
/** \brief Implement the virtual function of ProcessorManager, Apply() is the
|
||||
* body of Run(). Apply() contains the main logic of preprocessing, Run() is
|
||||
* called by users to execute preprocessing
|
||||
*
|
||||
* \param[in] image_batch The input image batch
|
||||
* \param[in] outputs The output tensors which will feed in runtime
|
||||
* \return true if the preprocess successed, otherwise false
|
||||
*/
|
||||
virtual bool Apply(FDMatBatch* image_batch,
|
||||
std::vector<FDTensor>* outputs);
|
||||
|
||||
/// This function will disable normalize in preprocessing step.
|
||||
void DisableNormalize();
|
||||
/// This function will disable hwc2chw in preprocessing step.
|
||||
void DisablePermute();
|
||||
|
||||
/** \brief When the initial operator is Resize, and input image size is large,
|
||||
* maybe it's better to run resize on CPU, because the HostToDevice memcpy
|
||||
* is time consuming. Set this true to run the initial resize on CPU.
|
||||
*
|
||||
* \param[in] v ture or false
|
||||
*/
|
||||
void InitialResizeOnCpu(bool v) { initial_resize_on_cpu_ = v; }
|
||||
|
||||
private:
|
||||
bool BuildPreprocessPipelineFromConfig();
|
||||
bool initialized_ = false;
|
||||
std::vector<std::shared_ptr<Processor>> processors_;
|
||||
// for recording the switch of hwc2chw
|
||||
bool disable_permute_ = false;
|
||||
// for recording the switch of normalize
|
||||
bool disable_normalize_ = false;
|
||||
// read config file
|
||||
std::string config_file_;
|
||||
bool initial_resize_on_cpu_ = false;
|
||||
};
|
||||
|
||||
} // namespace classification
|
||||
} // namespace vision
|
||||
} // namespace fastdeploy
|
||||
@@ -19,16 +19,21 @@ namespace vision {
|
||||
void ClassifyResult::Free() {
|
||||
std::vector<int32_t>().swap(label_ids);
|
||||
std::vector<float>().swap(scores);
|
||||
std::vector<float>().swap(feature);
|
||||
}
|
||||
|
||||
void ClassifyResult::Clear() {
|
||||
label_ids.clear();
|
||||
scores.clear();
|
||||
feature.clear();
|
||||
}
|
||||
|
||||
void ClassifyResult::Resize(int size) {
|
||||
label_ids.resize(size);
|
||||
scores.resize(size);
|
||||
// TODO(qiuyanjun): feature not perform resize now.
|
||||
// may need the code below for future.
|
||||
// feature.resize(size);
|
||||
}
|
||||
|
||||
std::string ClassifyResult::Str() {
|
||||
@@ -38,9 +43,25 @@ std::string ClassifyResult::Str() {
|
||||
out = out + std::to_string(label_ids[i]) + ", ";
|
||||
}
|
||||
out += "\nscores: ";
|
||||
for (size_t i = 0; i < label_ids.size(); ++i) {
|
||||
for (size_t i = 0; i < scores.size(); ++i) {
|
||||
out = out + std::to_string(scores[i]) + ", ";
|
||||
}
|
||||
if (!feature.empty()) {
|
||||
out += "\nfeature: size (";
|
||||
out += std::to_string(feature.size()) + "), only show first 100 values.\n";
|
||||
for (size_t i = 0; i < feature.size(); ++i) {
|
||||
// only show first 100 values.
|
||||
if ((i + 1) <= 100) {
|
||||
out = out + std::to_string(feature[i]) + ", ";
|
||||
if ((i + 1) % 10 == 0 && (i + 1) < 100) {
|
||||
out += "\n";
|
||||
}
|
||||
if ((i + 1) == 100) {
|
||||
out += "\n......";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
out += "\n)";
|
||||
return out;
|
||||
}
|
||||
@@ -49,6 +70,7 @@ ClassifyResult& ClassifyResult::operator=(ClassifyResult&& other) {
|
||||
if (&other != this) {
|
||||
label_ids = std::move(other.label_ids);
|
||||
scores = std::move(other.scores);
|
||||
feature = std::move(other.feature);
|
||||
}
|
||||
return *this;
|
||||
}
|
||||
@@ -154,14 +176,18 @@ std::string DetectionResult::Str() {
|
||||
if (!contain_masks) {
|
||||
out = "DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]\n";
|
||||
if (!rotated_boxes.empty()) {
|
||||
out = "DetectionResult: [x1, y1, x2, y2, x3, y3, x4, y4, score, label_id]\n";
|
||||
out =
|
||||
"DetectionResult: [x1, y1, x2, y2, x3, y3, x4, y4, score, "
|
||||
"label_id]\n";
|
||||
}
|
||||
} else {
|
||||
out =
|
||||
"DetectionResult: [xmin, ymin, xmax, ymax, score, label_id, "
|
||||
"mask_shape]\n";
|
||||
if (!rotated_boxes.empty()) {
|
||||
out = "DetectionResult: [x1, y1, x2, y2, x3, y3, x4, y4, score, label_id, mask_shape]\n";
|
||||
out =
|
||||
"DetectionResult: [x1, y1, x2, y2, x3, y3, x4, y4, score, label_id, "
|
||||
"mask_shape]\n";
|
||||
}
|
||||
}
|
||||
for (size_t i = 0; i < boxes.size(); ++i) {
|
||||
@@ -646,8 +672,8 @@ std::string OCRResult::Str() {
|
||||
out = out + "]";
|
||||
|
||||
if (rec_scores.size() > 0) {
|
||||
out = out + "rec text: " + text[n] +
|
||||
" rec score:" + std::to_string(rec_scores[n]) + " ";
|
||||
out = out + "rec text: " + text[n] + " rec score:" +
|
||||
std::to_string(rec_scores[n]) + " ";
|
||||
}
|
||||
if (cls_labels.size() > 0) {
|
||||
out = out + "cls label: " + std::to_string(cls_labels[n]) +
|
||||
@@ -687,8 +713,8 @@ std::string OCRResult::Str() {
|
||||
cls_scores.size() > 0) {
|
||||
std::string out;
|
||||
for (int i = 0; i < rec_scores.size(); i++) {
|
||||
out = out + "rec text: " + text[i] +
|
||||
" rec score:" + std::to_string(rec_scores[i]) + " ";
|
||||
out = out + "rec text: " + text[i] + " rec score:" +
|
||||
std::to_string(rec_scores[i]) + " ";
|
||||
out = out + "cls label: " + std::to_string(cls_labels[i]) +
|
||||
" cls score: " + std::to_string(cls_scores[i]);
|
||||
out = out + "\n";
|
||||
@@ -707,8 +733,8 @@ std::string OCRResult::Str() {
|
||||
cls_scores.size() == 0) {
|
||||
std::string out;
|
||||
for (int i = 0; i < rec_scores.size(); i++) {
|
||||
out = out + "rec text: " + text[i] +
|
||||
" rec score:" + std::to_string(rec_scores[i]) + " ";
|
||||
out = out + "rec text: " + text[i] + " rec score:" +
|
||||
std::to_string(rec_scores[i]) + " ";
|
||||
out = out + "\n";
|
||||
}
|
||||
return out;
|
||||
@@ -755,9 +781,9 @@ std::string HeadPoseResult::Str() {
|
||||
std::string out;
|
||||
|
||||
out = "HeadPoseResult: [yaw, pitch, roll]\n";
|
||||
out = out + "yaw: " + std::to_string(euler_angles[0]) + "\n" +
|
||||
"pitch: " + std::to_string(euler_angles[1]) + "\n" +
|
||||
"roll: " + std::to_string(euler_angles[2]) + "\n";
|
||||
out = out + "yaw: " + std::to_string(euler_angles[0]) + "\n" + "pitch: " +
|
||||
std::to_string(euler_angles[1]) + "\n" + "roll: " +
|
||||
std::to_string(euler_angles[2]) + "\n";
|
||||
return out;
|
||||
}
|
||||
|
||||
|
||||
@@ -49,6 +49,8 @@ struct FASTDEPLOY_DECL ClassifyResult : public BaseResult {
|
||||
std::vector<int32_t> label_ids;
|
||||
/// The confidence for each classify result
|
||||
std::vector<float> scores;
|
||||
/// The feature vector of recognizer, e.g, PP-ShiTuV2 Recognizer
|
||||
std::vector<float> feature;
|
||||
ResultType type = ResultType::CLASSIFY;
|
||||
|
||||
/// Resize ClassifyResult data buffer
|
||||
|
||||
18
fastdeploy/vision/detection/ppdet/base.cc
Executable file → Normal file
18
fastdeploy/vision/detection/ppdet/base.cc
Executable file → Normal file
@@ -18,7 +18,6 @@ PPDetBase::PPDetBase(const std::string& model_file,
|
||||
runtime_option.model_format = model_format;
|
||||
runtime_option.model_file = model_file;
|
||||
runtime_option.params_file = params_file;
|
||||
|
||||
}
|
||||
|
||||
std::unique_ptr<PPDetBase> PPDetBase::Clone() const {
|
||||
@@ -83,8 +82,14 @@ bool PPDetBase::BatchPredict(const std::vector<cv::Mat>& imgs,
|
||||
return true;
|
||||
}
|
||||
|
||||
bool PPDetBase::CheckArch(){
|
||||
std::vector<std::string> archs = {"SOLOv2","YOLO","SSD","RetinaNet","RCNN","Face","GFL","YOLOX","YOLOv5","YOLOv6","YOLOv7","RTMDet","FCOS","TTFNet","TOOD","DETR"};
|
||||
bool PPDetBase::CheckArch() {
|
||||
// Add "PicoDet" arch for backward compability with the
|
||||
// old ppdet model, such as picodet from PaddleClas
|
||||
// PP-ShiTuV2 pipeline.
|
||||
std::vector<std::string> archs = {
|
||||
"SOLOv2", "YOLO", "SSD", "RetinaNet", "RCNN", "Face",
|
||||
"GFL", "YOLOX", "YOLOv5", "YOLOv6", "YOLOv7", "RTMDet",
|
||||
"FCOS", "TTFNet", "TOOD", "DETR", "PicoDet"};
|
||||
auto arch_ = preprocessor_.GetArch();
|
||||
for (auto item : archs) {
|
||||
if (arch_ == item) {
|
||||
@@ -92,11 +97,10 @@ bool PPDetBase::CheckArch(){
|
||||
}
|
||||
}
|
||||
FDWARNING << "Please set model arch,"
|
||||
<< "support value : SOLOv2, YOLO, SSD, RetinaNet, RCNN, Face , GFL , RTMDet ,"\
|
||||
<<"FCOS , TTFNet , TOOD , DETR." << std::endl;
|
||||
<< "support value : SOLOv2, YOLO, SSD, RetinaNet, "
|
||||
<< "RCNN, Face , GFL , RTMDet ,"
|
||||
<< "FCOS , TTFNet , TOOD , DETR, PicoDet" << std::endl;
|
||||
return false;
|
||||
|
||||
|
||||
}
|
||||
|
||||
} // namespace detection
|
||||
|
||||
@@ -443,7 +443,8 @@ class FASTDEPLOY_DECL GFL : public PPDetBase {
|
||||
|
||||
class FASTDEPLOY_DECL PaddleDetectionModel : public PPDetBase {
|
||||
public:
|
||||
PaddleDetectionModel(const std::string& model_file, const std::string& params_file,
|
||||
PaddleDetectionModel(const std::string& model_file,
|
||||
const std::string& params_file,
|
||||
const std::string& config_file,
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||
@@ -472,8 +473,13 @@ class FASTDEPLOY_DECL PPYOLOER : public PPDetBase {
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||
: PPDetBase(model_file, params_file, config_file, custom_option,
|
||||
model_format) {
|
||||
valid_cpu_backends = { Backend::PDINFER};
|
||||
valid_gpu_backends = {Backend::PDINFER};
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::OPENVINO, Backend::ORT,
|
||||
Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
valid_timvx_backends = {Backend::LITE};
|
||||
valid_kunlunxin_backends = {Backend::LITE};
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
valid_ascend_backends = {Backend::LITE};
|
||||
valid_sophgonpu_backends = {Backend::SOPHGOTPU};
|
||||
initialized = Initialize();
|
||||
}
|
||||
|
||||
10
fastdeploy/vision/vision_pybind.cc
Executable file → Normal file
10
fastdeploy/vision/vision_pybind.cc
Executable file → Normal file
@@ -62,18 +62,26 @@ void BindVision(pybind11::module& m) {
|
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.def(pybind11::init())
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.def_readwrite("label_ids", &vision::ClassifyResult::label_ids)
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.def_readwrite("scores", &vision::ClassifyResult::scores)
|
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.def_readwrite("feature", &vision::ClassifyResult::feature)
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.def(pybind11::pickle(
|
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[](const vision::ClassifyResult& c) {
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if (c.feature.empty()) {
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return pybind11::make_tuple(c.label_ids, c.scores);
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}
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return pybind11::make_tuple(c.label_ids, c.scores, c.feature);
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},
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[](pybind11::tuple t) {
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if (t.size() != 2)
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if ((t.size() != 2) && (t.size() != 3)) {
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throw std::runtime_error(
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"vision::ClassifyResult pickle with invalid state!");
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}
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vision::ClassifyResult c;
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c.label_ids = t[0].cast<std::vector<int32_t>>();
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c.scores = t[1].cast<std::vector<float>>();
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if (t.size() == 3) {
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c.feature = t[2].cast<std::vector<float>>();
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}
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return c;
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}))
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@@ -15,6 +15,10 @@ from __future__ import absolute_import
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from .contrib.yolov5cls import YOLOv5Cls
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from .ppcls import *
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from .ppshitu import PPShiTuV2Detector
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from .ppshitu import PPShiTuV2Recognizer
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from .ppshitu import PPShiTuV2RecognizerPreprocessor
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from .ppshitu import PPShiTuV2RecognizerPostprocessor
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from .contrib.resnet import ResNet
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|
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PPLCNet = PaddleClasModel
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||||
|
||||
131
python/fastdeploy/vision/classification/ppshitu/__init__.py
Normal file
131
python/fastdeploy/vision/classification/ppshitu/__init__.py
Normal file
@@ -0,0 +1,131 @@
|
||||
from __future__ import absolute_import
|
||||
import logging
|
||||
from .... import FastDeployModel, ModelFormat
|
||||
from .... import c_lib_wrap as C
|
||||
from ...common import ProcessorManager
|
||||
from ...detection.ppdet import PicoDet
|
||||
|
||||
|
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class PPShiTuV2Detector(PicoDet):
|
||||
"""Detect main body from an input image.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
class PPShiTuV2RecognizerPreprocessor(ProcessorManager):
|
||||
def __init__(self, config_file):
|
||||
"""Create a preprocessor for PPShiTuV2Recognizer from configuration file
|
||||
|
||||
:param config_file: (str)Path of configuration file, e.g PPLCNet/inference_cls.yaml
|
||||
"""
|
||||
super(PPShiTuV2RecognizerPreprocessor, self).__init__()
|
||||
self._manager = C.vision.classification.PPShiTuV2RecognizerPreprocessor(
|
||||
config_file)
|
||||
|
||||
def disable_normalize(self):
|
||||
"""
|
||||
This function will disable normalize in preprocessing step.
|
||||
"""
|
||||
self._manager.disable_normalize()
|
||||
|
||||
def disable_permute(self):
|
||||
"""
|
||||
This function will disable hwc2chw in preprocessing step.
|
||||
"""
|
||||
self._manager.disable_permute()
|
||||
|
||||
def initial_resize_on_cpu(self, v):
|
||||
"""
|
||||
When the initial operator is Resize, and input image size is large,
|
||||
maybe it's better to run resize on CPU, because the HostToDevice memcpy
|
||||
is time consuming. Set this True to run the initial resize on CPU.
|
||||
:param: v: True or False
|
||||
"""
|
||||
self._manager.initial_resize_on_cpu(v)
|
||||
|
||||
|
||||
class PPShiTuV2RecognizerPostprocessor:
|
||||
def __init__(self, topk=1):
|
||||
"""Create a postprocessor for PPShiTuV2Recognizer
|
||||
|
||||
"""
|
||||
self._postprocessor = C.vision.classification.PPShiTuV2RecognizerPostprocessor(
|
||||
)
|
||||
|
||||
def run(self, runtime_results):
|
||||
"""Postprocess the runtime results for PPShiTuV2Recognizer
|
||||
|
||||
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
|
||||
:return: list of ClassifyResult, the feature vector is ClassifyResult.feature (If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
|
||||
"""
|
||||
return self._postprocessor.run(runtime_results)
|
||||
|
||||
|
||||
class PPShiTuV2Recognizer(FastDeployModel):
|
||||
def __init__(self,
|
||||
model_file,
|
||||
params_file,
|
||||
config_file,
|
||||
runtime_option=None,
|
||||
model_format=ModelFormat.PADDLE):
|
||||
"""Load a image PPShiTuV2Recognizer model exported by PaddleClas.
|
||||
|
||||
:param model_file: (str)Path of model file, e.g PPLCNet/inference.pdmodel
|
||||
:param params_file: (str)Path of parameters file, e.g PPLCNet/inference.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
|
||||
:param config_file: (str) Path of configuration file for deploy, e.g PPLCNet/inference_cls.yaml
|
||||
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
|
||||
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
|
||||
"""
|
||||
|
||||
super(PPShiTuV2Recognizer, self).__init__(runtime_option)
|
||||
self._model = C.vision.classification.PPShiTuV2Recognizer(
|
||||
model_file, params_file, config_file, self._runtime_option,
|
||||
model_format)
|
||||
assert self.initialized, "PPShiTuV2Recognizer model initialize failed."
|
||||
|
||||
def clone(self):
|
||||
"""Clone PPShiTuV2Recognizer object
|
||||
|
||||
:return: a new PPShiTuV2Recognizer object
|
||||
"""
|
||||
|
||||
class PPShiTuV2RecognizerCloneModel(PPShiTuV2Recognizer):
|
||||
def __init__(self, model):
|
||||
self._model = model
|
||||
|
||||
clone_model = PPShiTuV2RecognizerCloneModel(self._model.clone())
|
||||
return clone_model
|
||||
|
||||
def predict(self, im):
|
||||
"""Extract feature from an input image
|
||||
|
||||
:param im: (numpy.ndarray) The input image data, a 3-D array with layout HWC, BGR format
|
||||
:return: ClassifyResult
|
||||
"""
|
||||
|
||||
return self._model.predict(im)
|
||||
|
||||
def batch_predict(self, images):
|
||||
"""Extract features from a batch of input image
|
||||
|
||||
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
|
||||
:return list of ClassifyResult, the feature vector is ClassifyResult.feature
|
||||
"""
|
||||
|
||||
return self._model.batch_predict(images)
|
||||
|
||||
@property
|
||||
def preprocessor(self):
|
||||
"""Get PPShiTuV2RecognizerPreprocessor object of the loaded model
|
||||
|
||||
:return PPShiTuV2RecognizerPreprocessor
|
||||
"""
|
||||
return self._model.preprocessor
|
||||
|
||||
@property
|
||||
def postprocessor(self):
|
||||
"""Get PPShiTuV2RecognizerPostprocessor object of the loaded model
|
||||
|
||||
:return PPShiTuV2RecognizerPostprocessor
|
||||
"""
|
||||
return self._model.postprocessor
|
||||
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