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| #include <string> | ||
| #include <iostream> | ||
| #include <sstream> | ||
| #include <cstdlib> | ||
| #include <map> | ||
| #include <filesystem> | ||
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| #include "../cpp/snNet.h" | ||
| #include "../cpp/snTensor.h" | ||
| #include "../cpp/snOperator.h" | ||
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| #include "Lib/OpenCV_3.3.0/opencv2/core/core_c.h" | ||
| #include "Lib/OpenCV_3.3.0/opencv2/core/core.hpp" | ||
| #include "Lib/OpenCV_3.3.0/opencv2/imgproc/imgproc_c.h" | ||
| #include "Lib/OpenCV_3.3.0/opencv2/imgproc/imgproc.hpp" | ||
| #include "Lib/OpenCV_3.3.0/opencv2/highgui/highgui_c.h" | ||
| #include "Lib/OpenCV_3.3.0/opencv2/highgui/highgui.hpp" | ||
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| using namespace std; | ||
| namespace sn = SN_API; | ||
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| bool loadImage(string& imgPath, int classCnt, vector<vector<string>>& imgName, vector<int>& imgCntDir, map<string, cv::Mat>& images){ | ||
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| for (int i = 0; i < classCnt; ++i){ | ||
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| namespace fs = std::tr2::sys; | ||
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| if (!fs::exists(fs::path(imgPath + to_string(i) + "/"))) continue; | ||
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| fs::directory_iterator it(imgPath + to_string(i) + "/"); int cnt = 0; | ||
| while (it != fs::directory_iterator()){ | ||
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| fs::path p = it->path(); | ||
| if (fs::is_regular_file(p) && (p.extension() == ".png")){ | ||
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| imgName[i].push_back(p.filename()); | ||
| } | ||
| ++it; | ||
| ++cnt; | ||
| } | ||
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| imgCntDir[i] = cnt; | ||
| } | ||
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| return true; | ||
| } | ||
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| int main(int argc, char* argv[]){ | ||
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| sn::Net snet; | ||
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| snet.addNode("Input", sn::Input(), "FC1") | ||
| .addNode("FC1", sn::FullyConnected(256, sn::active::relu), "FC2") | ||
| .addNode("FC2", sn::FullyConnected(128, sn::active::relu), "FC3") | ||
| .addNode("FC3", sn::FullyConnected(32, sn::active::relu), "FC4") | ||
| .addNode("FC4", sn::FullyConnected(128, sn::active::relu), "FC5") | ||
| .addNode("FC5", sn::FullyConnected(256, sn::active::relu), "FC6") | ||
| .addNode("FC6", sn::FullyConnected(784, sn::active::sigmoid), "LS") | ||
| .addNode("LS", sn::LossFunction(sn::lossType::binaryCrossEntropy), "Output"); | ||
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| string imgPath = "c://cpp//skyNet//example//autoEncoder//images//"; | ||
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| int classCnt = 5, batchSz = 100, w = 28, h = 28; | ||
| float lr = 0.001F; | ||
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| vector<vector<string>> imgName(classCnt); | ||
| vector<int> imgCntDir(classCnt); | ||
| map<string, cv::Mat> images; | ||
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| if (!loadImage(imgPath, classCnt, imgName, imgCntDir, images)){ | ||
| cout << "Error 'loadImage' path: " << imgPath << endl; | ||
| system("pause"); | ||
| return -1; | ||
| } | ||
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| //snet.loadAllWeightFromFile("c:\\cpp\\w.dat"); | ||
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| sn::Tensor inLayer(sn::snLSize(w, h, 1, batchSz)); | ||
| sn::Tensor outLayer(sn::snLSize(w * h, 1, 1, batchSz)); | ||
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| size_t sum_metric = 0; | ||
| size_t num_inst = 0; | ||
| float accuratSumm = 0; | ||
| for (int k = 0; k < 1000; ++k){ | ||
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| srand(clock()); | ||
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| for (int i = 0; i < batchSz; ++i){ | ||
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| // directory | ||
| int ndir = rand() % classCnt; | ||
| while (imgCntDir[ndir] == 0) | ||
| ndir = rand() % classCnt; | ||
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| // image | ||
| int nimg = rand() % imgCntDir[ndir]; | ||
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| // read | ||
| cv::Mat img; | ||
| string nm = imgName[ndir][nimg]; | ||
| if (images.find(nm) != images.end()) | ||
| img = images[nm]; | ||
| else{ | ||
| img = cv::imread(imgPath + to_string(ndir) + "/" + nm, CV_LOAD_IMAGE_UNCHANGED); | ||
| images[nm] = img; | ||
| } | ||
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| float* refData = inLayer.data() + i * w * h; | ||
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| size_t nr = img.rows, nc = img.cols; | ||
| for (size_t r = 0; r < nr; ++r){ | ||
| uchar* pt = img.ptr<uchar>(r); | ||
| for (size_t c = 0; c < nc; ++c) | ||
| refData[r * nc + c] = pt[c] / 255.0; | ||
| } | ||
| } | ||
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| // training | ||
| float accurat = 0; | ||
| snet.training(lr, | ||
| inLayer, | ||
| outLayer, | ||
| inLayer, | ||
| accurat); | ||
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| float* refData = outLayer.data(); | ||
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| cv::Mat img(w, h, CV_8U); | ||
| for (size_t r = 0; r < h; ++r){ | ||
| uchar* pt = img.ptr<uchar>(r); | ||
| for (size_t c = 0; c < w; ++c) | ||
| pt[c] = refData[r * w + c] * 255.0; | ||
| } | ||
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| cv::namedWindow("1", 0); | ||
| cv::imshow("1", img); | ||
| cv::waitKey(1); | ||
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| accuratSumm += accurat; | ||
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| cout << k << " accurate " << accuratSumm / k << " " << snet.getLastErrorStr() << endl; | ||
| } | ||
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| snet.saveAllWeightToFile("c:\\cpp\\w.dat"); | ||
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| system("pause"); | ||
| return 0; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,67 @@ | ||
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| import os | ||
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| from libskynet import* | ||
| import numpy as np | ||
| import imageio | ||
| import random | ||
| import ctypes | ||
| import datetime | ||
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| # create net | ||
| net = snNet.Net() | ||
| net.addNode('In', snOperator.Input(), 'FC1') \ | ||
| .addNode('FC1', snOperator.FullyConnected(256), 'FC2') \ | ||
| .addNode('FC2', snOperator.FullyConnected(128), 'FC3') \ | ||
| .addNode('FC3', snOperator.FullyConnected(32), 'FC4') \ | ||
| .addNode('FC4', snOperator.FullyConnected(128), 'FC5') \ | ||
| .addNode('FC5', snOperator.FullyConnected(256), 'FC6') \ | ||
| .addNode('FC6', snOperator.FullyConnected(784), 'LS') \ | ||
| .addNode('LS', snOperator.LossFunction(snType.lossType.binaryCrossEntropy), 'Output') | ||
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| # load of weight | ||
| #if (net.loadAllWeightFromFile('c:/cpp/w.dat')): | ||
| # print('weight is load') | ||
| #else: | ||
| # print('error load weight') | ||
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| # loadImg | ||
| imgList = [] | ||
| pathImg = 'c:\\cpp\\skyNet\\example\\autoEncoder\\images\\' | ||
| for i in range(10): | ||
| imgList.append(os.listdir(pathImg + str(i))) | ||
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| bsz = 100 | ||
| lr = 0.001 | ||
| accuratSumm = 0. | ||
| inLayer = np.zeros((bsz, 1, 28, 28), ctypes.c_float) | ||
| outLayer = np.zeros((bsz, 1, 1, 28 * 28), ctypes.c_float) | ||
| imgMem = {} | ||
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| # cycle lern | ||
| for n in range(1000): | ||
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| for i in range(bsz): | ||
| ndir = random.randint(0, 10 - 1) | ||
| nimg = random.randint(0, len(imgList[ndir]) - 1) | ||
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| nm = pathImg + str(ndir) + '/' + imgList[ndir][nimg] | ||
| if (nm in imgMem): | ||
| inLayer[i][0] = imgMem[nm] | ||
| else: | ||
| inLayer[i][0] = imageio.imread(nm) | ||
| imgMem[nm] = inLayer[i][0].copy() | ||
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| acc = [0] | ||
| net.training(lr, inLayer, outLayer, inLayer, acc) | ||
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| accuratSumm += acc[0]/bsz | ||
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| print(datetime.datetime.now().strftime('%H:%M:%S'), n, "accurate", accuratSumm / (n + 1)) | ||
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| # save weight | ||
| if (net.saveAllWeightToFile('c:/cpp/w.dat')): | ||
| print('weight is save') | ||
| else: | ||
| print('error save weight') |
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