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https://github.com/PaddlePaddle/FastDeploy.git
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3589c0fa94
* Refactor the PaddleClas module * fix bug * remove debug code * clean unused code * support pybind * Update fd_tensor.h * Update fd_tensor.cc * temporary revert python api * fix ci error * fix code style problem
56 lines
1.8 KiB
C++
56 lines
1.8 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/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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/*! @brief Postprocessor object for PaddleClas serials model.
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*/
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class FASTDEPLOY_DECL PaddleClasPostprocessor {
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public:
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/** \brief Create a postprocessor instance for PaddleClas serials model
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*
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* \param[in] topk The topk result filtered by the classify confidence score, default 1
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*/
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explicit PaddleClasPostprocessor(int topk = 1);
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/** \brief Process the result of runtime and fill to ClassifyResult 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 classification
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* \return true if the postprocess successed, otherwise false
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*/
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bool Run(const std::vector<FDTensor>& tensors,
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std::vector<ClassifyResult>* result);
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/// Set topk value
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void SetTopk(int topk) { topk_ = topk; }
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/// Get topk value
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int GetTopk() const { return topk_; }
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private:
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int topk_ = 1;
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bool initialized_ = false;
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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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