mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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135 lines
4.5 KiB
C++
135 lines
4.5 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/classification/contrib/resnet.h"
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#include "fastdeploy/vision/utils/utils.h"
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#include "fastdeploy/utils/perf.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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ResNet::ResNet(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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// In constructor, the 3 steps below are necessary.
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// 1. set the Backend 2. set RuntimeOption 3. call Initialize()
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::ORT, 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};
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valid_gpu_backends = {Backend::PDINFER};
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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 ResNet::Initialize() {
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// In this function, the 3 steps below are necessary.
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// 1. assign values to the global variables 2. call InitRuntime()
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size = {224, 224};
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mean_vals = {0.485f, 0.456f, 0.406f};
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std_vals = {0.229f, 0.224f, 0.225f};
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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 ResNet::Preprocess(Mat* mat, FDTensor* output) {
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// In this function, the preprocess need be implemented according to the original Repos,
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// The result of preprocess has to be saved in FDTensor variable, because the input of Infer() need to be std::vector<FDTensor>.
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// 1. Resize 2. BGR2RGB 3. Normalize 4. HWC2CHW 5. Put the result into FDTensor variable.
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if (mat->Height()!=size[0] || mat->Width()!=size[1]){
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int interp = cv::INTER_LINEAR;
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Resize::Run(mat, size[1], size[0], -1, -1, interp);
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}
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BGR2RGB::Run(mat);
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Normalize::Run(mat, mean_vals, std_vals);
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
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return true;
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}
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bool ResNet::Postprocess(FDTensor& infer_result,
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ClassifyResult* result, int topk) {
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// In this function, the postprocess need be implemented according to the original Repos,
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// Finally the reslut of postprocess should be saved in ClassifyResult variable.
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// 1. Softmax 2. Choose topk labels 3. Put the result into ClassifyResult variable.
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int num_classes = infer_result.shape[1];
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function::Softmax(infer_result, &infer_result);
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const float* infer_result_buffer = reinterpret_cast<float*>(infer_result.Data());
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topk = std::min(num_classes, topk);
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result->label_ids =
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utils::TopKIndices(infer_result_buffer, num_classes, topk);
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result->scores.resize(topk);
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for (int i = 0; i < topk; ++i) {
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result->scores[i] = *(infer_result_buffer + result->label_ids[i]);
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}
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return true;
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}
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bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
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// In this function, the Preprocess(), Infer(), and Postprocess() are called sequentially.
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Mat mat(*im);
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std::vector<FDTensor> processed_data(1);
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if (!Preprocess(&mat, &(processed_data[0]))) {
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FDERROR << "Failed to preprocess input data while using model:"
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<< ModelName() << "." << std::endl;
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return false;
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}
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processed_data[0].name = InputInfoOfRuntime(0).name;
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std::vector<FDTensor> output_tensors;
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if (!Infer(processed_data, &output_tensors)) {
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FDERROR << "Failed to inference while using model:" << ModelName() << "."
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<< std::endl;
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return false;
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}
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if (!Postprocess(output_tensors[0], result, topk)) {
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FDERROR << "Failed to postprocess while using model:" << ModelName() << "."
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<< 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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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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