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77cb9db6da
* [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
59 lines
1.9 KiB
C++
59 lines
1.9 KiB
C++
// 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");
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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 "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_postprocessor.h"
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#include <cmath>
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#include <numeric>
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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bool PPShiTuV2RecognizerPostprocessor::Run(
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const std::vector<FDTensor>& tensors,
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std::vector<ClassifyResult>* results) {
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int batch = tensors[0].shape[0]; // e.g [batch, 512]
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int num_feature = tensors[0].shape[1];
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const float* tensor_data = reinterpret_cast<const float*>(tensors[0].Data());
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results->resize(batch);
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// post processing per batch=1
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for (int i = 0; i < batch; ++i) {
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(*results)[i].feature.resize(num_feature);
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const float* tensor_data_i_start = tensor_data + i * num_feature;
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std::memcpy((*results)[i].feature.data(), tensor_data_i_start,
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num_feature * sizeof(float));
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if (feature_norm_) {
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FeatureNorm((*results)[i].feature);
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}
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}
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return true;
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}
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void PPShiTuV2RecognizerPostprocessor::FeatureNorm(
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std::vector<float>& feature) {
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float feature_sqrt = std::sqrt(std::inner_product(
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feature.begin(), feature.end(), feature.begin(), 0.0f));
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for (int i = 0; i < feature.size(); ++i) {
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feature[i] /= feature_sqrt;
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}
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}
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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