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143506b654
* change ocr pre and post * add pybind * change ocr * fix bug * fix bug * fix bug * fix bug * fix bug * fix bug * fix copy bug * fix code style * fix bug * add new function * fix windows ci bug
66 lines
2.2 KiB
C++
66 lines
2.2 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/ocr/ppocr/cls_postprocessor.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace ocr {
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ClassifierPostprocessor::ClassifierPostprocessor() {
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initialized_ = true;
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}
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bool SingleBatchPostprocessor(const float* out_data, const size_t& length, int* cls_label, float* cls_score) {
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*cls_label = std::distance(
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&out_data[0], std::max_element(&out_data[0], &out_data[length]));
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*cls_score =
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float(*std::max_element(&out_data[0], &out_data[length]));
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return true;
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}
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bool ClassifierPostprocessor::Run(const std::vector<FDTensor>& tensors,
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std::vector<int32_t>* cls_labels,
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std::vector<float>* cls_scores) {
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if (!initialized_) {
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FDERROR << "Postprocessor is not initialized." << std::endl;
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return false;
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}
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// Classifier have only 1 output tensor.
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const FDTensor& tensor = tensors[0];
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// For Classifier, the output tensor shape = [batch,2]
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size_t batch = tensor.shape[0];
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size_t length = accumulate(tensor.shape.begin()+1, tensor.shape.end(), 1, std::multiplies<int>());
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cls_labels->resize(batch);
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cls_scores->resize(batch);
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const float* tensor_data = reinterpret_cast<const float*>(tensor.Data());
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for (int i_batch = 0; i_batch < batch; ++i_batch) {
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if(!SingleBatchPostprocessor(tensor_data, length, &cls_labels->at(i_batch),&cls_scores->at(i_batch))) return false;
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tensor_data = tensor_data + length;
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}
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return true;
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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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