bhiziroglu / image-classification-with-deep-convolutional-neural-networks Goto Github PK

Tensor tmp=images[i].clone();
tmp=tmp.permute({1,2,0}).to(torch::kU8);  
cv::Mat image(32,32,CV_8UC3);
std::memcpy((void *)image.data,tmp.data_ptr(),sizeof(torch::kU8)*tmp.numel());
char imgpath[814];
sprintf(imgpath,"/my/path/%d.png",i+1);
cv::imwrite(imgpath,image); 

pair<Tensor, Tensor> Cifar::read_data(const string &root, int train){
    int num_samples;
    vector<string> files;
    if(train==0){
        files = trainBatchFiles;
        num_samples = 50000;
    }else{
        files = testBatchFiles;
        num_samples = 10000;
    }
    auto images =torch::empty({num_samples, 3,32,32}, torch::kByte);
    auto targets = torch::empty(num_samples, torch::kByte);
    int labelr(0);
    Mat image(32,32,CV_8UC3,cv::Scalar::all(0));
    char* pData=(char*)image.data;
    int tot_img=0;
    for (const auto &file : files)
    {
        const auto path = join_paths(root, file);
        FILE * fptr=fopen(path.c_str(),"rb");
        if(fptr== nullptr){
            cout<<"Open error!"<<endl;
            fclose(fptr);
            continue;
        }
        for(int id_img=0; id_img<1e4;id_img++,tot_img++){
            fread(&labelr,sizeof(char),1,fptr);
            //cout<<labelr<<endl;
            for(int id_pic=0;id_pic<32*32;id_pic++){
                fread(pData+id_pic*3+2,sizeof(char),1,fptr);
            }
            for(int id_pic=0;id_pic<32*32;id_pic++){
                fread(pData+id_pic*3+1,sizeof(char),1,fptr);
            }
            for(int id_pic=0;id_pic<32*32;id_pic++){
                fread(pData+id_pic*3,sizeof(char),1,fptr);
            }
            images[tot_img]=torch::from_blob(image.data,{3,32,32},torch::kByte);
            targets[tot_img]=labelr;
        }
    }
   
    return {images.to(torch::kFloat32).div_(255), targets.to(torch::kInt64)};
}