![]() Image Segmentation demonstrates a Python script that converts the PyTorch DeepLabV3 model and an Android app that uses the model to segment images. This demo app also shows how to use the native pre-built torchvision-ops library. More PyTorch Android Demo Apps D2goĭ2Go demonstrates a Python script that creates the much lighter and much faster Facebook D2Go model that is powered by PyTorch 1.8, torchvision 0.9, and Detectron2 with built-in SOTA networks for mobile, and an Android app that uses it to detect objects from pictures in your photos, taken with camera, or with live camera. Tensor outputTensor = mModule.forward(om(inputTensor)).toTensor()Īfter that, the code processes the output, finding classes with the highest scores. The module has a get_classes method that returns List, which can be called using method nMethod(methodName): Result class names are packaged inside the TorchScript model and initialized just after initial module initialization. The logic happens in TextClassificattionActivity. ![]() Language Processing ExampleĪnother example is natural language processing, based on an LSTM model, trained on a reddit comments dataset. It uses the aforementioned TensorImageUtils.imageYUV420CenterCropToFloat32Tensor method to convert in YUV420 format to input tensor.Īfter getting predicted scores from the model it finds top K classes with the highest scores and shows on the UI. ![]() Where the analyzeImage method process the camera output,. setImageReaderMode(_LATEST_IMAGE)įinal ImageAnalysis imageAnalysis = new ImageAnalysis(imageAnalysisConfig) ĬameraX.bindToLifecycle(this, preview, imageAnalysis) tOnPreviewOutputUpdateListener(output -> tSurfaceTexture(output.getSurfaceTexture())) įinal ImageAnalysisConfig imageAnalysisConfig = Final PreviewConfig previewConfig = new PreviewConfig.Builder().build() įinal Preview preview = new Preview(previewConfig) ![]()
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