mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-22 08:46:02 +08:00
nihuini
134 lines
3.5 KiB
C++
134 lines
3.5 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include <stdio.h>
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#include <algorithm>
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#include <vector>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include "platform.h"
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#include "net.h"
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#if NCNN_VULKAN
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#include "gpu.h"
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#endif // NCNN_VULKAN
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static int detect_shufflenetv2(const cv::Mat& bgr, std::vector<float>& cls_scores)
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{
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ncnn::Net shufflenetv2;
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#if NCNN_VULKAN
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shufflenetv2.opt.use_vulkan_compute = true;
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#endif // NCNN_VULKAN
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// https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe
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// models can be downloaded from https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases
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shufflenetv2.load_param("shufflenet_v2_x0.5.param");
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shufflenetv2.load_model("shufflenet_v2_x0.5.bin");
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ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 224, 224);
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const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};
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in.substract_mean_normalize(0, norm_vals);
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ncnn::Extractor ex = shufflenetv2.create_extractor();
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ex.input("data", in);
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ncnn::Mat out;
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ex.extract("fc", out);
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// manually call softmax on the fc output
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// convert result into probability
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// skip if your model already has softmax operation
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{
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ncnn::Layer* softmax = ncnn::create_layer("Softmax");
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ncnn::ParamDict pd;
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softmax->load_param(pd);
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softmax->forward_inplace(out, shufflenetv2.opt);
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delete softmax;
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}
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out = out.reshape(out.w * out.h * out.c);
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cls_scores.resize(out.w);
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for (int j=0; j<out.w; j++)
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{
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cls_scores[j] = out[j];
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}
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return 0;
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}
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static int print_topk(const std::vector<float>& cls_scores, int topk)
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{
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// partial sort topk with index
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int size = cls_scores.size();
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std::vector< std::pair<float, int> > vec;
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vec.resize(size);
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for (int i=0; i<size; i++)
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{
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vec[i] = std::make_pair(cls_scores[i], i);
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}
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std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
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std::greater< std::pair<float, int> >());
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// print topk and score
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for (int i=0; i<topk; i++)
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{
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float score = vec[i].first;
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int index = vec[i].second;
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fprintf(stderr, "%d = %f\n", index, score);
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}
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return 0;
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}
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int main(int argc, char** argv)
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{
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if (argc != 2)
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{
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fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
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return -1;
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}
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const char* imagepath = argv[1];
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cv::Mat m = cv::imread(imagepath, 1);
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if (m.empty())
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{
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fprintf(stderr, "cv::imread %s failed\n", imagepath);
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return -1;
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}
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#if NCNN_VULKAN
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ncnn::create_gpu_instance();
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#endif // NCNN_VULKAN
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std::vector<float> cls_scores;
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detect_shufflenetv2(m, cls_scores);
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#if NCNN_VULKAN
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ncnn::destroy_gpu_instance();
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#endif // NCNN_VULKAN
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print_topk(cls_scores, 3);
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return 0;
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}
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