File size: 3,428 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#include "net.h"
#include <algorithm>
#if defined(USE_NCNN_SIMPLEOCV)
#include "simpleocv.h"
#else
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#endif
#include <stdio.h>
#include <vector>
static int detect_shufflenetv2(const cv::Mat& bgr, std::vector<float>& cls_scores)
{
ncnn::Net shufflenetv2;
shufflenetv2.opt.use_vulkan_compute = true;
// https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe
// models can be downloaded from https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases
if (shufflenetv2.load_param("shufflenet_v2_x0.5.param"))
exit(-1);
if (shufflenetv2.load_model("shufflenet_v2_x0.5.bin"))
exit(-1);
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 224, 224);
const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
in.substract_mean_normalize(0, norm_vals);
ncnn::Extractor ex = shufflenetv2.create_extractor();
ex.input("data", in);
ncnn::Mat out;
ex.extract("fc", out);
// manually call softmax on the fc output
// convert result into probability
// skip if your model already has softmax operation
{
ncnn::Layer* softmax = ncnn::create_layer("Softmax");
ncnn::ParamDict pd;
softmax->load_param(pd);
softmax->forward_inplace(out, shufflenetv2.opt);
delete softmax;
}
out = out.reshape(out.w * out.h * out.c);
cls_scores.resize(out.w);
for (int j = 0; j < out.w; j++)
{
cls_scores[j] = out[j];
}
return 0;
}
static int print_topk(const std::vector<float>& cls_scores, int topk)
{
// partial sort topk with index
int size = cls_scores.size();
std::vector<std::pair<float, int> > vec;
vec.resize(size);
for (int i = 0; i < size; i++)
{
vec[i] = std::make_pair(cls_scores[i], i);
}
std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
std::greater<std::pair<float, int> >());
// print topk and score
for (int i = 0; i < topk; i++)
{
float score = vec[i].first;
int index = vec[i].second;
fprintf(stderr, "%d = %f\n", index, score);
}
return 0;
}
int main(int argc, char** argv)
{
if (argc != 2)
{
fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
return -1;
}
const char* imagepath = argv[1];
cv::Mat m = cv::imread(imagepath, 1);
if (m.empty())
{
fprintf(stderr, "cv::imread %s failed\n", imagepath);
return -1;
}
std::vector<float> cls_scores;
detect_shufflenetv2(m, cls_scores);
print_topk(cls_scores, 3);
return 0;
}
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