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https://github.com/PaddlePaddle/FastDeploy.git
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81fbd54c9d
* add tests for timvx * add mobilenetv1 test * update code * fix log info * update log * fix test --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
69 lines
2.1 KiB
C++
Executable File
69 lines
2.1 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 <string>
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#include "common.h"
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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 std::string& cls_result) {
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auto model_file = model_dir + sep + "inference.pdmodel";
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auto params_file = model_dir + sep + "inference.pdiparams";
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auto config_file = model_dir + sep + "inference_cls.yaml";
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fastdeploy::vision::EnableFlyCV();
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fastdeploy::RuntimeOption option;
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option.UseTimVX();
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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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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fastdeploy::vision::ClassifyResult 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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if (CompareClsResult(res, cls_result)) {
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std::cout << model_dir + " run successfully." << std::endl;
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} else {
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std::cerr << model_dir + " run failed." << std::endl;
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}
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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
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<< "Usage: test_clas path/to/quant_model "
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"path/to/image "
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"e.g ./test_clas ./ResNet50_vd_quant ./test.jpeg resnet50_clas.txt"
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<< std::endl;
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return -1;
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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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std::string cls_result = argv[3];
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InitAndInfer(model_dir, test_image, cls_result);
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return 0;
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}
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