ncnn-jni: add MNIST inference using NCNN (#186)

README.md

@@ -248,6 +248,9 @@ here are some screenshots to demostrate CV inference by running the excellent <a
 
 ![988568755](https://github.com/zhouwg/kantv/assets/6889919/49c7e22e-0e4c-4ece-b690-04d59ac1382f)
 
+
+![1730654667](https://github.com/zhouwg/kantv/assets/6889919/83ef3e44-92ee-4c00-b000-840a13097544)
+
 <details>
   <summary>some other screenshots</summary>
   <ol>
@@ -356,6 +359,15 @@ Report issue in various Android-based phone or even submit PR to this project is
 
   </ul>
 
+
+### This project is suitable for(本项目适合于)
+
+- Students:understand calculus/linear algebra/mathematical statistics and probability theory, have some experiences in C/C++ development, want to learn Android software development(UI development, NDK development and Android AI application development);  学生:了解微积分，线性代数，数理统计与概率论, 且有一定的C/C++开发经验, 希望学习Android开发（UI开发与NDK开发与Android端侧AI应用开发);
+- Programmers: have some experiences in C/C++/Java software development, know nothing about real/hardcore AI technology, want to learn AI technology in depth(know how); 程序员:有一定的C/C++/Java开发经验，几乎不懂真正的AI技术，希望深入学习AI技术(know how);
+- AI researchers/developers: validate/verify AI(ASR, TTS, CV, NLP, LLM...) algorithm in the framework provided in this project for Android device;  AI特定领域(ASR, TTS, CV, NLP, LLM,...)的算法研究/开发人员:在本项目提供的框架中调试/验证Android设备上的AI特定领域算法;
+- Authors/maintainers of AI inference framework: compare the advantages of <a href="https://github.com/ggerganov/ggml">ggml</a> and <a href="https://github.com/Tencent/ncnn">ncnn</a>; AI推理框架的开发人员: 对比ggml与ncnn两个端侧推理框架的优点，相互借鉴;
+
+
 ### License
 
 ```

cdeosplayer/cdeosplayer-lib/src/main/java/cdeos/media/player/CDEUtils.java

@@ -277,11 +277,13 @@ public class CDEUtils {
      //inference using NCNN
      public static final int BENCHMARK_CV_RESNET        = 9;
      public static final int BENCHMARK_CV_SQUEEZENET    = 10;
-     public static final int BENCHAMRK_ASR_NCNN         = 11;
-     public static final int BENCHAMRK_TTS_NCNN         = 12;
+     public static final int BENCHMARK_CV_MNIST_NCNN    = 11;
+     public static final int BENCHMARK_ASR_NCNN         = 12;
+     public static final int BENCHMARK_TTS_NCNN         = 13;
 
+     //ncnn backend
      public static final int NCNN_BACKEND_CPU           = 0;
-     public static final int NCNN_BACKEND_GPU           = 0;
+     public static final int NCNN_BACKEND_GPU           = 1;
 
 
      //keep sync with ggml-qnn.h
@@ -3959,11 +3961,15 @@ public static String getBenchmarkDesc(int benchmarkIndex) {
              case BENCHMARK_CV_SQUEEZENET:
                  return "SQUEEZENET inference using NCNN";
 
-             case BENCHAMRK_ASR_NCNN:
+             case BENCHMARK_ASR_NCNN:
                  return "ASR inference using NCNN";
 
-             case BENCHAMRK_TTS_NCNN:
+             case BENCHMARK_TTS_NCNN:
                  return "TTS inference using NCNN";
+
+             case BENCHMARK_CV_MNIST_NCNN:
+                 return "MNIST inference using NCNN";
+
          }
 
          return "unknown";

cdeosplayer/cdeosplayer-lib/src/main/java/org/ncnn/ncnnjava.java

@@ -33,8 +33,19 @@ public class ncnnjava
     public native boolean closeCamera();
     public native boolean setOutputWindow(Surface surface);
 
-    public native String  detectResNet(Bitmap bitmap, boolean use_gpu);
-    public native String  detectSqueezeNet(Bitmap bitmap, boolean use_gpu);
+
+    /**
+     * @param ncnnmodelParam   param file of ncnn model
+     * @param ncnnmodelBin     bin   file of ncnn model
+     * @param userData         ASR: /sdcard/kantv/jfk.wav / LLM: user input / TEXT2IMAGE: user input / ResNet&SqueezeNet&MNIST: image path / TTS: user input
+     * @param bitmap
+     * @param nBenchType       1: NCNN_RESNET 2: NCNN_SQUEEZENET 3: NCNN_MNIST
+     * @param nThreadCounts    1 - 8
+     * @param nBackendType     0: NCNN_BACKEND_CPU  1: NCNN_BACKEND_GPU
+     * @param nOpType          type of NCNN OP
+     * @return
+     */
+    public static native String ncnn_bench(String ncnnmodelParam, String ncnnmodelBin, String userData, Bitmap bitmap, int nBenchType, int nThreadCounts, int nBackendType, int nOpType);
 
     static {
         System.loadLibrary("ncnn-jni");

cdeosplayer/kantv/src/main/assets/mnist-int8.param

@@ -0,0 +1,8 @@
+7767517
+6 6
+Input                    in                       0 1 data 0=28 1=28 2=1
+Convolution              conv0                    1 1 data conv0 0=8 1=5 4=2 5=1 6=200 8=2 9=1
+Pooling                  pool0                    1 1 conv0 pool0 1=2 2=2 5=1
+Convolution              conv1                    1 1 pool0 conv1 0=16 1=5 4=2 5=1 6=3200 8=2 9=1
+Pooling                  pool1                    1 1 conv1 pool1 1=3 2=3 5=1
+InnerProduct             fc                       1 1 pool1 fc 0=10 1=1 2=2560 8=2

cdeosplayer/kantv/src/main/assets/mnist.param

@@ -0,0 +1,8 @@
+7767517
+6 7
+Input           in      0 1 data 0=28 1=28 2=1
+Convolution     conv0   1 1 data conv0 0=8 1=5 4=2 5=1 6=200 9=1
+Pooling         pool0   1 1 conv0 pool0 1=2 2=2 5=1
+Convolution     conv1   1 1 pool0 conv1 0=16 1=5 4=2 5=1 6=3200 9=1
+Pooling         pool1   1 1 conv1 pool1 1=3 2=3 5=1
+InnerProduct    fc      1 1 pool1 fc 0=10 1=1 2=2560