mirror of
https://github.com/PaddlePaddle/PaddleClas.git
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117 lines
4.7 KiB
C++
117 lines
4.7 KiB
C++
// Copyright (c) 2020 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 <algorithm>
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#include <include/cls.h>
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#include <numeric>
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namespace PaddleClas {
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void Classifier::LoadModel(const std::string &model_path,
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const std::string ¶ms_path) {
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paddle_infer::Config config;
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config.SetModel(model_path, params_path);
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 1, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(this->ir_optim_);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
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void Classifier::Run(cv::Mat &img, std::vector<float> &out_data,
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std::vector<int> &idx, std::vector<double> ×) {
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cv::Mat srcimg;
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cv::Mat resize_img;
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img.copyTo(srcimg);
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auto preprocess_start = std::chrono::system_clock::now();
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this->resize_op_.Run(img, resize_img, this->resize_short_size_);
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this->crop_op_.Run(resize_img, this->crop_size_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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auto preprocess_end = std::chrono::system_clock::now();
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auto infer_start = std::chrono::system_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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idx.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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auto infer_end = std::chrono::system_clock::now();
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auto postprocess_start = std::chrono::system_clock::now();
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// int maxPosition =
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// max_element(out_data.begin(), out_data.end()) - out_data.begin();
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iota(idx.begin(), idx.end(), 0);
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stable_sort(idx.begin(), idx.end(), [&out_data](int i1, int i2) {
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return out_data[i1] > out_data[i2];
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});
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auto postprocess_end = std::chrono::system_clock::now();
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std::chrono::duration<float> preprocess_diff =
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preprocess_end - preprocess_start;
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times[0] = double(preprocess_diff.count() * 1000);
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std::chrono::duration<float> inference_diff = infer_end - infer_start;
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double inference_cost_time = double(inference_diff.count() * 1000);
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times[1] = inference_cost_time;
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std::chrono::duration<float> postprocess_diff =
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postprocess_end - postprocess_start;
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times[2] = double(postprocess_diff.count() * 1000);
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/* std::cout << "result: " << std::endl; */
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/* std::cout << "\tclass id: " << maxPosition << std::endl; */
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/* std::cout << std::fixed << std::setprecision(10) */
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/* << "\tscore: " << double(out_data[maxPosition]) << std::endl; */
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}
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} // namespace PaddleClas
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