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安卓端、不同硬件平台、相同系统大版本下：retinaface人脸检测相同Mat输入，模型推理输出不一致

context | 编译/运行环境 | バックグラウンド

Android应用开发工具：
Android Studio 3.5.2
Gradle：gradle-5.4.1-all
NDK：21.1.6352462
Cmake：3.6.4111459

ncnn：
commit id：2ef57a6204d24d3f527832cebb902f2f04df0cca
android lib build：Ubuntu 18.04/python 3.6.9/cmake 3.10.2/android-ndk-r21e

所有Android对比测试设备：
1、展锐SC9863A/Android 10.0
2、高通qcm2150/Android 10.0
3、麒麟Kirin970/Android 10.0
4、联发科天玑800U/Android 10.0

how to reproduce | 复现步骤 | 再現方法

1.编写人脸检测模型推理及日志记录代码

//读取本地图片
......
  InputStream is = getActivity().getContentResolver().openInputStream(uri);
  Bitmap mBitmap = BitmapFactory.decodeStream(is);
  mBitmap = mBitmap.copy(Bitmap.Config.ARGB_8888, true);
......
//调用人脸检测模型native方法
......

......
AndroidBitmapInfo info;
AndroidBitmap_getInfo(env, obj_bitmap, &info);
int width = info.width;
int height = info.height;
if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
  return ret;
}
ncnn::Mat ncnn_img = ncnn::Mat::from_android_bitmap(env, obj_bitmap, ncnn::Mat::PIXEL_BGR2RGB);
pretty_print_into_file(ncnn_img, "ncnn_img");//输入ncnn mat数据打印
std::vector<FaceObject> finalBbox;
int status = mDetectRetinaFace->detect_retinaface(ncnn_img, finalBbox);//人脸检测模型推理
......

int DetectRetinaFace::detect_retinaface(const ncnn::Mat &ncnn_img, std::vector<FaceObject> &faceobjects) {

        ncnn::Extractor ex = RetinaNet.create_extractor();
        ex.input(faceDetect_opt_param_id::BLOB_data, ncnn_img);
        std::vector<FaceObject> faceproposals;

        //int status1 = get_faceproposals_stride8(ex, faceproposals);
        ncnn::Mat score_blob, bbox_blob, landmark_blob;
        int status1 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_cls_prob_reshape_stride8, score_blob);
        int status2 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_bbox_pred_stride8, bbox_blob);
        int status3 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_landmark_pred_stride8, landmark_blob);
        //输出3节点stride8 ncnn mat数据打印
        pretty_print_into_file(score_blob, "face_detect_stride8_score_blob");
        pretty_print_into_file(bbox_blob, "face_detect_stride8_bbox_blob");
        pretty_print_into_file(landmark_blob, "face_detect_stride8_landmark_blob");

        //int status2 = get_faceproposals_stride16(ex, faceproposals);
        ncnn::Mat score_blob, bbox_blob, landmark_blob;
        int status4 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_cls_prob_reshape_stride16, score_blob);
        int status5 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_bbox_pred_stride16, bbox_blob);
        int status6 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_landmark_pred_stride16, landmark_blob);
        //输出3节点stride16 ncnn mat数据打印
        pretty_print_into_file(score_blob, "face_detect_stride16_score_blob");
        pretty_print_into_file(bbox_blob, "face_detect_stride16_bbox_blob");
        pretty_print_into_file(landmark_blob, "face_detect_stride16_landmark_blob");

        //int status3 = get_faceproposals_stride32(ex, faceproposals);
        ncnn::Mat score_blob, bbox_blob, landmark_blob;
        int status7 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_cls_prob_reshape_stride32, score_blob);
        int status8 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_bbox_pred_stride32, bbox_blob);
        int status9 = ex.extract(faceDetect_opt_param_id::BLOB_face_rpn_landmark_pred_stride32, landmark_blob);
        //输出3节点stride32 ncnn mat数据打印
        pretty_print_into_file(score_blob, "face_detect_stride32_score_blob");
        pretty_print_into_file(bbox_blob, "face_detect_stride32_bbox_blob");
        pretty_print_into_file(landmark_blob, "face_detect_stride32_landmark_blob");
......
}

2.挑选一张带人脸的bmp格式图片作为待测试图
3.在不同设备上运行该应用代码，并观察结果输出

more | 其他 | その他

1、四台对比设备中：模型输入完全一致，展锐与联发科的输出数据完全一致，高通与麒麟的输出比较接近（误差小于0.1%），展锐/联发科与高通/麒麟存在较大差距（误差大于1%）

2、另外还发现一个现象，就是不同Android版本（5.1/7.1/8.1/10.0）之间读取相同bmp图片后通过ncnn库接口构造的模型输入存在一定差距，即以下对象

ncnn::Mat ncnn_img = ncnn::Mat::from_android_bitmap(env, obj_bitmap, ncnn::Mat::PIXEL_BGR2RGB);
pretty_print_into_file(ncnn_img, "ncnn_img");//输入ncnn mat数据打印，不同Android大版本上运行存在差距

测试图片与模型文件详见附件
bug_test_files.zip