mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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312e1b097d
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs Co-authored-by: Jason <jiangjiajun@baidu.com>
100 lines
3.4 KiB
C++
100 lines
3.4 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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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 segmentation {
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class FASTDEPLOY_DECL PaddleSegPostprocessor {
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public:
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/** \brief Create a postprocessor instance for PaddleSeg serials model
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*
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* \param[in] config_file Path of configuration file for deployment, e.g ppliteseg/deploy.yaml
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*/
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explicit PaddleSegPostprocessor(const std::string& config_file);
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/** \brief Process the result of runtime and fill to SegmentationResult structure
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*
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* \param[in] tensors The inference result from runtime
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* \param[in] result The output result of detection
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* \param[in] imgs_info The original input images shape info map, key is "shape_info", value is vector<array<int, 2>> a{{height, width}}
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* \return true if the postprocess successed, otherwise false
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*/
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virtual bool Run(
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const std::vector<FDTensor>& infer_results,
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std::vector<SegmentationResult>* results,
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const std::map<std::string, std::vector<std::array<int, 2>>>& imgs_info);
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/** \brief Get apply_softmax property of PaddleSeg model, default is false
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*/
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bool GetApplySoftmax() const {
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return apply_softmax_;
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}
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/// Set apply_softmax value, bool type required
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void SetApplySoftmax(bool value) {
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apply_softmax_ = value;
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}
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/// Get store_score_map property of PaddleSeg model, default is false
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bool GetStoreScoreMap() const {
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return store_score_map_;
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}
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/// Set store_score_map value, bool type required
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void SetStoreScoreMap(bool value) {
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store_score_map_ = value;
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}
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private:
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virtual bool ReadFromConfig(const std::string& config_file);
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virtual bool SliceOneResultFromBatchInferResults(
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const FDTensor& infer_results,
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FDTensor* infer_result,
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const std::vector<int64_t>& infer_result_shape,
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const int64_t& start_idx);
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virtual bool ProcessWithScoreResult(const FDTensor& infer_result,
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const int64_t& out_num,
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SegmentationResult* result);
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virtual bool ProcessWithLabelResult(const FDTensor& infer_result,
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const int64_t& out_num,
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SegmentationResult* result);
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virtual bool FDTensorCast2Uint8(FDTensor* infer_result,
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const int64_t& offset,
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std::vector<uint8_t>* uint8_result_buffer);
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bool is_with_softmax_ = false;
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bool is_with_argmax_ = true;
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bool apply_softmax_ = false;
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bool store_score_map_ = false;
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bool initialized_ = false;
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};
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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