git clone https://github.com/pytorch/android-demo-app.git

repositories {
  jcenter()
}

dependencies {
  implementation 'org.pytorch:pytorch_android:1.4.0'
  implementation 'org.pytorch:pytorch_android_torchvision:1.4.0'
}

apply plugin: 'com.android.application'
repositories {
  jcenter()
}

android {
  compileSdkVersion 28
  buildToolsVersion "29.0.2"
  defaultConfig {
    applicationId "org.pytorch.helloworld"
    minSdkVersion 21
    targetSdkVersion 28
    versionCode 1
    versionName "1.0"
  }
  buildTypes {
    release {
      minifyEnabled false
    }
  }
}

dependencies {
  implementation 'androidx.appcompat:appcompat:1.1.0'
  implementation 'org.pytorch:pytorch_android:1.4.0'
  implementation 'org.pytorch:pytorch_android_torchvision:1.4.0'
}

Bitmap bitmap = BitmapFactory.decodeStream(getAssets().open("image.jpg"));
Module module = Module.load(assetFilePath(this, "model.pt"));
Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(bitmap,
  TensorImageUtils.TORCHVISION_NORM_MEAN_RGB, TensorImageUtils.TORCHVISION_NORM_STD_RGB);
  Tensor outputTensor = module.forward(IValue.from(inputTensor)).toTensor();
float[] scores = outputTensor.getDataAsFloatArray();
float maxScore = -Float.MAX_VALUE;
int maxScoreIdx = -1;
for (int i = 0; i < scores.length; i++) {
 if (scores[i] > maxScore) {
  maxScore = scores[i];
  maxScoreIdx = i;
 }
}
String className = ImageNetClasses.IMAGENET_CLASSES[maxScoreIdx];

package org.pytorch.helloworld;

import android.content.Context;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Bundle;
import android.util.Log;
import android.widget.ImageView;
import android.widget.TextView;

import org.pytorch.IValue;
import org.pytorch.Module;
import org.pytorch.Tensor;
import org.pytorch.torchvision.TensorImageUtils;

import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;

import androidx.appcompat.app.AppCompatActivity;

public class MainActivity extends AppCompatActivity {

 @Override
 protected void onCreate(Bundle savedInstanceState) {
  super.onCreate(savedInstanceState);
  setContentView(R.layout.activity_main);

  Bitmap bitmap = null;
  Module module = null;
  try {
   // creating bitmap from packaged into app android asset 'image.jpg',
   // app/src/main/assets/image.jpg
   bitmap = BitmapFactory.decodeStream(getAssets().open("image.jpg"));
   // loading serialized torchscript module from packaged into app android asset model.pt,
   // app/src/model/assets/model.pt
   module = Module.load(assetFilePath(this, "model.pt"));
  } catch (IOException e) {
   Log.e("PytorchHelloWorld", "Error reading assets", e);
   finish();
  }

  // showing image on UI
  ImageView imageView = findViewById(R.id.image);
  imageView.setImageBitmap(bitmap);

  // preparing input tensor
  final Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(bitmap,
    TensorImageUtils.TORCHVISION_NORM_MEAN_RGB, TensorImageUtils.TORCHVISION_NORM_STD_RGB);

  // running the model
  final Tensor outputTensor = module.forward(IValue.from(inputTensor)).toTensor();

  // getting tensor content as java array of floats
  final float[] scores = outputTensor.getDataAsFloatArray();

  // searching for the index with maximum score
  float maxScore = -Float.MAX_VALUE;
  int maxScoreIdx = -1;
  for (int i = 0; i < scores.length; i++) {
   if (scores[i] > maxScore) {
    maxScore = scores[i];
    maxScoreIdx = i;
   }
  }

  String className = ImageNetClasses.IMAGENET_CLASSES[maxScoreIdx];

  // showing className on UI
  TextView textView = findViewById(R.id.text);
  textView.setText(className);
 }

 /**
  * Copies specified asset to the file in /files app directory and returns this file absolute path.
  *
  * @return absolute file path
  */
 public static String assetFilePath(Context context, String assetName) throws IOException {
  File file = new File(context.getFilesDir(), assetName);
  if (file.exists() && file.length() > 0) {
   return file.getAbsolutePath();
  }

  try (InputStream is = context.getAssets().open(assetName)) {
   try (OutputStream os = new FileOutputStream(file)) {
    byte[] buffer = new byte[4 * 1024];
    int read;
    while ((read = is.read(buffer)) != -1) {
     os.write(buffer, 0, read);
    }
    os.flush();
   }
   return file.getAbsolutePath();
  }
 }
}

import torch
import torchvision.models as models
from PIL import Image
import numpy as np
image = Image.open("test.jpg") #圖片發(fā)在了build文件夾下
image = image.resize((224, 224),Image.ANTIALIAS)
image = np.asarray(image)
image = image / 255
image = torch.Tensor(image).unsqueeze_(dim=0)
image = image.permute((0, 3, 1, 2)).float()

model = models.resnet50(pretrained=True)
model = model.eval()
resnet = torch.jit.trace(model, torch.rand(1,3,224,224))
# output=resnet(torch.ones(1,3,224,224))
output = resnet(image)
max_index = torch.max(output, 1)[1].item()
print(max_index) # ImageNet1000類的類別序
resnet.save('model.pt')
if __name__ == '__main__':
  pass