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https://github.com/PaddlePaddle/FastDeploy.git
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d45382e3cc
* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend
96 lines
3.4 KiB
C++
Executable File
96 lines
3.4 KiB
C++
Executable File
// 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/classifier.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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Classifier::Classifier() {}
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Classifier::Classifier(const std::string& model_file,
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const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::ORT,
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Backend::OPENVINO};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO, Backend::LITE};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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valid_ascend_backends = {Backend::LITE};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool Classifier::Initialize() {
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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bool Classifier::Predict(const cv::Mat& img, int32_t* cls_label, float* cls_score) {
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std::vector<int32_t> cls_labels(1);
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std::vector<float> cls_scores(1);
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bool success = BatchPredict({img}, &cls_labels, &cls_scores);
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if(!success){
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return success;
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}
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*cls_label = cls_labels[0];
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*cls_score = cls_scores[0];
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return true;
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}
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bool Classifier::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<int32_t>* cls_labels, std::vector<float>* cls_scores) {
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return BatchPredict(images, cls_labels, cls_scores, 0, images.size());
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}
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bool Classifier::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<int32_t>* cls_labels, std::vector<float>* cls_scores,
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size_t start_index, size_t end_index) {
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size_t total_size = images.size();
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std::vector<FDMat> fd_images = WrapMat(images);
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, start_index, end_index)) {
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FDERROR << "Failed to preprocess the input image." << std::endl;
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return false;
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}
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reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, cls_labels, cls_scores, start_index, total_size)) {
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FDERROR << "Failed to postprocess the inference cls_results by runtime." << std::endl;
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return false;
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}
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return true;
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}
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} // namesapce ocr
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} // namespace vision
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} // namespace fastdeploy
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