pytorch 移动端部署之helloworld的使用

 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-600")); 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  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-600', // app/src/main/assets/image.jpg-600 bitmap = BitmapFactory.decodeStream(getAssets().open("image.jpg-600")); // 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  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-600") #图片发在了build文件夹下 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类的类别序 resnet.save('model.pt') if __name__ == '__main__': pass