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// Copyright (c) 2020 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 <include/cls.h>
namespace PaddleClas {
void Classifier::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
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if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
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// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
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}
void Classifier::Run(cv::Mat &img) {
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
this->resize_op_.Run(img, resize_img, this->resize_short_size_);
this->crop_op_.Run(resize_img, this->crop_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
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std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
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int maxPosition =
max_element(out_data.begin(), out_data.end()) - out_data.begin();
std::cout << "result: " << std::endl;
std::cout << "\tclass id: " << maxPosition << std::endl;
std::cout << std::fixed << std::setprecision(10)
<< "\tscore: " << double(out_data[maxPosition]) << std::endl;
}
} // namespace PaddleClas