mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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6c31198342
* 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 * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST Co-authored-by: Jason <jiangjiajun@baidu.com>
76 lines
2.2 KiB
C++
76 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.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void InitAndInfer(const std::string& model_dir, const std::string& image_file,
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const fastdeploy::RuntimeOption& option) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "deploy.yaml";
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file,option);
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assert(model.Initialized());
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::SegmentationResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout << "Usage: infer_demo path/to/quant_model "
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"path/to/image "
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"run_option, "
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"e.g ./infer_demo ./ResNet50_vd_quant ./test.jpeg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run on cpu with ORT "
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"backend; 1: run "
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"on gpu with TensorRT backend. "
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<< std::endl;
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return -1;
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}
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fastdeploy::RuntimeOption option;
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int flag = std::atoi(argv[3]);
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if (flag == 0) {
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option.UseCpu();
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option.UseOrtBackend();
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} else if (flag == 1) {
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option.UseCpu();
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option.UsePaddleInferBackend();
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}
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std::string model_dir = argv[1];
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std::string test_image = argv[2];
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InitAndInfer(model_dir, test_image, option);
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return 0;
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} |