Welcome to PyTorch Tutorials ============================ .. raw:: html

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.. Add callout items below this line .. customcalloutitem:: :description: Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide. :header: Learn the Basics :button_link: beginner/basics/intro.html :button_text: Get started with PyTorch .. customcalloutitem:: :description: Bite-size, ready-to-deploy PyTorch code examples. :header: PyTorch Recipes :button_link: recipes/recipes_index.html :button_text: Explore Recipes .. End of callout item section .. raw:: html
.. Add tutorial cards below this line .. Learning PyTorch .. customcarditem:: :header: Learn the Basics :card_description: A step-by-step guide to building a complete ML workflow with PyTorch. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/basics/intro.html :tags: Getting-Started .. customcarditem:: :header: Learning PyTorch with Examples :card_description: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. :image: _static/img/thumbnails/cropped/learning-pytorch-with-examples.png :link: beginner/pytorch_with_examples.html :tags: Getting-Started .. customcarditem:: :header: What is torch.nn really? :card_description: Use torch.nn to create and train a neural network. :image: _static/img/thumbnails/cropped/torch-nn.png :link: beginner/nn_tutorial.html :tags: Getting-Started .. customcarditem:: :header: Visualizing Models, Data, and Training with Tensorboard :card_description: Learn to use TensorBoard to visualize data and model training. :image: _static/img/thumbnails/cropped/visualizing-with-tensorboard.png :link: intermediate/tensorboard_tutorial.html :tags: Interpretability,Getting-Started,Tensorboard .. Image/Video .. customcarditem:: :header: TorchVision Object Detection Finetuning Tutorial :card_description: Finetune a pre-trained Mask R-CNN model. :image: _static/img/thumbnails/cropped/TorchVision-Object-Detection-Finetuning-Tutorial.png :link: intermediate/torchvision_tutorial.html :tags: Image/Video .. customcarditem:: :header: Transfer Learning for Computer Vision Tutorial :card_description: Train a convolutional neural network for image classification using transfer learning. :image: _static/img/thumbnails/cropped/Transfer-Learning-for-Computer-Vision-Tutorial.png :link: beginner/transfer_learning_tutorial.html :tags: Image/Video .. customcarditem:: :header: Optimizing Vision Transformer Model :card_description: Apply cutting-edge, attention-based transformer models to computer vision tasks. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/vt_tutorial.html :tags: Image/Video .. customcarditem:: :header: Adversarial Example Generation :card_description: Train a convolutional neural network for image classification using transfer learning. :image: _static/img/thumbnails/cropped/Adversarial-Example-Generation.png :link: beginner/fgsm_tutorial.html :tags: Image/Video .. customcarditem:: :header: DCGAN Tutorial :card_description: Train a generative adversarial network (GAN) to generate new celebrities. :image: _static/img/thumbnails/cropped/DCGAN-Tutorial.png :link: beginner/dcgan_faces_tutorial.html :tags: Image/Video .. Audio .. customcarditem:: :header: torchaudio Tutorial :card_description: Learn to load and preprocess data from a simple dataset with PyTorch's torchaudio library. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_preprocessing_tutorial.html :tags: Audio .. customcarditem:: :header: Speech Command Recognition :card_description: Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset. :image: _static/img/thumbnails/cropped/torchaudio-speech.png :link: intermediate/speech_command_recognition_with_torchaudio_tutorial.html :tags: Audio .. Text .. customcarditem:: :header: Sequence-to-Sequence Modeling with nn.Transformer and torchtext :card_description: Learn how to train a sequence-to-sequence model that uses the nn.Transformer module. :image: _static/img/thumbnails/cropped/Sequence-to-Sequence-Modeling-with-nnTransformer-andTorchText.png :link: beginner/transformer_tutorial.html :tags: Text .. customcarditem:: :header: NLP from Scratch: Classifying Names with a Character-level RNN :card_description: Build and train a basic character-level RNN to classify word from scratch without the use of torchtext. First in a series of three tutorials. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Classifying-Names-with-a-Character-Level-RNN.png :link: intermediate/char_rnn_classification_tutorial :tags: Text .. customcarditem:: :header: NLP from Scratch: Generating Names with a Character-level RNN :card_description: After using character-level RNN to classify names, leanr how to generate names from languages. Second in a series of three tutorials. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Generating-Names-with-a-Character-Level-RNN.png :link: intermediate/char_rnn_generation_tutorial.html :tags: Text .. customcarditem:: :header: NLP from Scratch: Translation with a Sequence-to-sequence Network and Attention :card_description: This is the third and final tutorial on doing “NLP From Scratch”, where we write our own classes and functions to preprocess the data to do our NLP modeling tasks. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Translation-with-a-Sequence-to-Sequence-Network-and-Attention.png :link: intermediate/seq2seq_translation_tutorial.html :tags: Text .. customcarditem:: :header: Text Classification with Torchtext :card_description: This is the third and final tutorial on doing “NLP From Scratch”, where we write our own classes and functions to preprocess the data to do our NLP modeling tasks. :image: _static/img/thumbnails/cropped/Text-Classification-with-TorchText.png :link: beginner/text_sentiment_ngrams_tutorial.html :tags: Text .. customcarditem:: :header: Language Translation with Transformer :card_description: Train a language translation model from scratch using Transformer. :image: _static/img/thumbnails/cropped/Language-Translation-with-TorchText.png :link: beginner/translation_transformer.html :tags: Text .. Reinforcement Learning .. customcarditem:: :header: Reinforcement Learning (DQN) :card_description: Learn how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym. :image: _static/img/cartpole.gif :link: intermediate/reinforcement_q_learning.html :tags: Reinforcement-Learning .. customcarditem:: :header: Train a Mario-playing RL Agent :card_description: Use PyTorch to train a Double Q-learning agent to play Mario. :image: _static/img/mario.gif :link: intermediate/mario_rl_tutorial.html :tags: Reinforcement-Learning .. Deploying PyTorch Models in Production .. customcarditem:: :header: Deploying PyTorch in Python via a REST API with Flask :card_description: Deploy a PyTorch model using Flask and expose a REST API for model inference using the example of a pretrained DenseNet 121 model which detects the image. :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/flask_rest_api_tutorial.html :tags: Production .. customcarditem:: :header: Introduction to TorchScript :card_description: Introduction to TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++. :image: _static/img/thumbnails/cropped/Introduction-to-TorchScript.png :link: beginner/Intro_to_TorchScript_tutorial.html :tags: Production,TorchScript .. customcarditem:: :header: Loading a TorchScript Model in C++ :card_description: Learn how PyTorch provides to go from an existing Python model to a serialized representation that can be loaded and executed purely from C++, with no dependency on Python. :image: _static/img/thumbnails/cropped/Loading-a-TorchScript-Model-in-Cpp.png :link: advanced/cpp_export.html :tags: Production,TorchScript .. customcarditem:: :header: (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime :card_description: Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime. :image: _static/img/thumbnails/cropped/optional-Exporting-a-Model-from-PyTorch-to-ONNX-and-Running-it-using-ONNX-Runtime.png :link: advanced/super_resolution_with_onnxruntime.html :tags: Production .. Code Transformations with FX .. customcarditem:: :header: Building a Convolution/Batch Norm fuser in FX :card_description: Build a simple FX pass that fuses batch norm into convolution to improve performance during inference. :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/fx_conv_bn_fuser.html :tags: FX .. customcarditem:: :header: Building a Simple Performance Profiler with FX :card_description: Build a simple FX interpreter to record the runtime of op, module, and function calls and report statistics :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/fx_profiling_tutorial.html :tags: FX .. Frontend APIs .. customcarditem:: :header: (beta) Channels Last Memory Format in PyTorch :card_description: Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions. :image: _static/img/thumbnails/cropped/experimental-Channels-Last-Memory-Format-in-PyTorch.png :link: intermediate/memory_format_tutorial.html :tags: Memory-Format,Best-Practice,Frontend-APIs .. customcarditem:: :header: Using the PyTorch C++ Frontend :card_description: Walk through an end-to-end example of training a model with the C++ frontend by training a DCGAN – a kind of generative model – to generate images of MNIST digits. :image: _static/img/thumbnails/cropped/Using-the-PyTorch-Cpp-Frontend.png :link: advanced/cpp_frontend.html :tags: Frontend-APIs,C++ .. customcarditem:: :header: Custom C++ and CUDA Extensions :card_description: Create a neural network layer with no parameters using numpy. Then use scipy to create a neural network layer that has learnable weights. :image: _static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png :link: advanced/cpp_extension.html :tags: Extending-PyTorch,Frontend-APIs,C++,CUDA .. customcarditem:: :header: Extending TorchScript with Custom C++ Operators :card_description: Implement a custom TorchScript operator in C++, how to build it into a shared library, how to use it in Python to define TorchScript models and lastly how to load it into a C++ application for inference workloads. :image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Operators.png :link: advanced/torch_script_custom_ops.html :tags: Extending-PyTorch,Frontend-APIs,TorchScript,C++ .. customcarditem:: :header: Extending TorchScript with Custom C++ Classes :card_description: This is a continuation of the custom operator tutorial, and introduces the API we’ve built for binding C++ classes into TorchScript and Python simultaneously. :image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Classes.png :link: advanced/torch_script_custom_classes.html :tags: Extending-PyTorch,Frontend-APIs,TorchScript,C++ .. customcarditem:: :header: Dynamic Parallelism in TorchScript :card_description: This tutorial introduces the syntax for doing *dynamic inter-op parallelism* in TorchScript. :image: _static/img/thumbnails/cropped/TorchScript-Parallelism.jpg :link: advanced/torch-script-parallelism.html :tags: Frontend-APIs,TorchScript,C++ .. customcarditem:: :header: Autograd in C++ Frontend :card_description: The autograd package helps build flexible and dynamic nerural netorks. In this tutorial, exploreseveral examples of doing autograd in PyTorch C++ frontend :image: _static/img/thumbnails/cropped/Autograd-in-Cpp-Frontend.png :link: advanced/cpp_autograd.html :tags: Frontend-APIs,C++ .. customcarditem:: :header: Registering a Dispatched Operator in C++ :card_description: The dispatcher is an internal component of PyTorch which is responsible for figuring out what code should actually get run when you call a function like torch::add. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.PNG :link: advanced/dispatcher.html :tags: Extending-PyTorch,Frontend-APIs,C++ .. customcarditem:: :header: Extending Dispatcher For a New Backend in C++ :card_description: Learn how to extend the dispatcher to add a new device living outside of the pytorch/pytorch repo and maintain it to keep in sync with native PyTorch devices. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.PNG :link: advanced/extend_dispatcher.html :tags: Extending-PyTorch,Frontend-APIs,C++ .. Model Optimization .. customcarditem:: :header: Performance Profiling in PyTorch :card_description: Learn how to use the PyTorch Profiler to benchmark your module's performance. :image: _static/img/thumbnails/cropped/profiler.png :link: beginner/profiler.html :tags: Model-Optimization,Best-Practice,Profiling .. customcarditem:: :header: Performance Profiling in Tensorboard :card_description: Learn how to use tensorboard plugin to profile and analyze your model's performance. :image: _static/img/thumbnails/cropped/profiler.png :link: intermediate/tensorboard_profiler_tutorial.html :tags: Model-Optimization,Best-Practice,Profiling .. customcarditem:: :header: Hyperparameter Tuning Tutorial :card_description: Learn how to use Ray Tune to find the best performing set of hyperparameters for your model. :image: _static/img/ray-tune.png :link: beginner/hyperparameter_tuning_tutorial.html :tags: Model-Optimization,Best-Practice .. customcarditem:: :header: Parametrizations Tutorial :card_description: Learn how to use torch.nn.utils.parametrize to put constriants on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...) :image: _static/img/thumbnails/cropped/parametrizations.png :link: intermediate/parametrizations.html :tags: Model-Optimization,Best-Practice .. customcarditem:: :header: Pruning Tutorial :card_description: Learn how to use torch.nn.utils.prune to sparsify your neural networks, and how to extend it to implement your own custom pruning technique. :image: _static/img/thumbnails/cropped/Pruning-Tutorial.png :link: intermediate/pruning_tutorial.html :tags: Model-Optimization,Best-Practice .. customcarditem:: :header: (beta) Dynamic Quantization on an LSTM Word Language Model :card_description: Apply dynamic quantization, the easiest form of quantization, to a LSTM-based next word prediction model. :image: _static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-an-LSTM-Word-Language-Model.png :link: advanced/dynamic_quantization_tutorial.html :tags: Text,Quantization,Model-Optimization .. customcarditem:: :header: (beta) Dynamic Quantization on BERT :card_description: Apply the dynamic quantization on a BERT (Bidirectional Embedding Representations from Transformers) model. :image: _static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-BERT.png :link: intermediate/dynamic_quantization_bert_tutorial.html :tags: Text,Quantization,Model-Optimization .. customcarditem:: :header: (beta) Quantized Transfer Learning for Computer Vision Tutorial :card_description: Extends the Transfer Learning for Computer Vision Tutorial using a quantized model. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: intermediate/quantized_transfer_learning_tutorial.html :tags: Image/Video,Quantization,Model-Optimization .. customcarditem:: :header: (beta) Static Quantization with Eager Mode in PyTorch :card_description: This tutorial shows how to do post-training static quantization. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: advanced/static_quantization_tutorial.html :tags: Quantization .. Parallel-and-Distributed-Training .. customcarditem:: :header: PyTorch Distributed Overview :card_description: Briefly go over all concepts and features in the distributed package. Use this document to find the distributed training technology that can best serve your application. :image: _static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png :link: beginner/dist_overview.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Single-Machine Model Parallel Best Practices :card_description: Learn how to implement model parallel, a distributed training technique which splits a single model onto different GPUs, rather than replicating the entire model on each GPU :image: _static/img/thumbnails/cropped/Model-Parallel-Best-Practices.png :link: intermediate/model_parallel_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Getting Started with Distributed Data Parallel :card_description: Learn the basics of when to use distributed data paralle versus data parallel and work through an example to set it up. :image: _static/img/thumbnails/cropped/Getting-Started-with-Distributed-Data-Parallel.png :link: intermediate/ddp_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Writing Distributed Applications with PyTorch :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package. :image: _static/img/thumbnails/cropped/Writing-Distributed-Applications-with-PyTorch.png :link: intermediate/dist_tuto.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Getting Started with Distributed RPC Framework :card_description: Learn how to build distributed training using the torch.distributed.rpc package. :image: _static/img/thumbnails/cropped/Getting Started with Distributed-RPC-Framework.png :link: intermediate/rpc_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Implementing a Parameter Server Using Distributed RPC Framework :card_description: Walk through a through a simple example of implementing a parameter server using PyTorch’s Distributed RPC framework. :image: _static/img/thumbnails/cropped/Implementing-a-Parameter-Server-Using-Distributed-RPC-Framework.png :link: intermediate/rpc_param_server_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Distributed Pipeline Parallelism Using RPC :card_description: Demonstrate how to implement distributed pipeline parallelism using RPC :image: _static/img/thumbnails/cropped/Distributed-Pipeline-Parallelism-Using-RPC.png :link: intermediate/dist_pipeline_parallel_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Implementing Batch RPC Processing Using Asynchronous Executions :card_description: Learn how to use rpc.functions.async_execution to implement batch RPC :image: _static/img/thumbnails/cropped/Implementing-Batch-RPC-Processing-Using-Asynchronous-Executions.png :link: intermediate/rpc_async_execution.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Combining Distributed DataParallel with Distributed RPC Framework :card_description: Walk through a through a simple example of how to combine distributed data parallelism with distributed model parallelism. :image: _static/img/thumbnails/cropped/Combining-Distributed-DataParallel-with-Distributed-RPC-Framework.png :link: advanced/rpc_ddp_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Training Transformer models using Pipeline Parallelism :card_description: Walk through a through a simple example of how to train a transformer model using pipeline parallelism. :image: _static/img/thumbnails/cropped/Training-Transformer-models-using-Pipeline-Parallelism.png :link: intermediate/pipeline_tutorial.html :tags: Parallel-and-Distributed-Training .. customcarditem:: :header: Training Transformer models using Distributed Data Parallel and Pipeline Parallelism :card_description: Walk through a through a simple example of how to train a transformer model using Distributed Data Parallel and Pipeline Parallelism :image: _static/img/thumbnails/cropped/Training-Transformer-Models-using-Distributed-Data-Parallel-and-Pipeline-Parallelism.png :link: advanced/ddp_pipeline.html :tags: Parallel-and-Distributed-Training .. Mobile .. customcarditem:: :header: Image Segmentation DeepLabV3 on iOS :card_description: A comprehensive step-by-step tutorial on how to prepare and run the PyTorch DeepLabV3 image segmentation model on iOS. :image: _static/img/thumbnails/cropped/ios.png :link: beginner/deeplabv3_on_ios.html :tags: Mobile .. customcarditem:: :header: Image Segmentation DeepLabV3 on Android :card_description: A comprehensive step-by-step tutorial on how to prepare and run the PyTorch DeepLabV3 image segmentation model on Android. :image: _static/img/thumbnails/cropped/android.png :link: beginner/deeplabv3_on_android.html :tags: Mobile .. End of tutorial card section .. raw:: html


Additional Resources ============================ .. raw:: html
.. Add callout items below this line .. customcalloutitem:: :header: Examples of PyTorch :description: A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. :button_link: https://github.com/pytorch/examples :button_text: Checkout Examples .. customcalloutitem:: :header: PyTorch Cheat Sheet :description: Quick overview to essential PyTorch elements. :button_link: beginner/ptcheat.html :button_text: Open .. customcalloutitem:: :header: Tutorials on GitHub :description: Access PyTorch Tutorials from GitHub. :button_link: https://github.com/pytorch/tutorials :button_text: Go To GitHub .. customcalloutitem:: :header: Run Tutorials on Google Colab :description: Learn how to copy tutorial data into Google Drive so that you can run tutorials on Google Colab. :button_link: beginner/colab.html :button_text: Open .. End of callout section .. raw:: html
.. ----------------------------------------- .. Page TOC .. ----------------------------------------- .. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: PyTorch Recipes See All Recipes See All Prototype Recipes .. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: Introduction to PyTorch beginner/basics/intro beginner/basics/quickstart_tutorial beginner/basics/tensorqs_tutorial beginner/basics/data_tutorial beginner/basics/transforms_tutorial beginner/basics/buildmodel_tutorial beginner/basics/autogradqs_tutorial beginner/basics/optimization_tutorial beginner/basics/saveloadrun_tutorial .. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: Learning PyTorch beginner/deep_learning_60min_blitz beginner/pytorch_with_examples beginner/nn_tutorial intermediate/tensorboard_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Image and Video intermediate/torchvision_tutorial beginner/transfer_learning_tutorial beginner/fgsm_tutorial beginner/dcgan_faces_tutorial beginner/vt_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Audio beginner/audio_preprocessing_tutorial intermediate/speech_command_recognition_with_torchaudio_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Text beginner/transformer_tutorial intermediate/char_rnn_classification_tutorial intermediate/char_rnn_generation_tutorial intermediate/seq2seq_translation_tutorial beginner/text_sentiment_ngrams_tutorial beginner/translation_transformer .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Reinforcement Learning intermediate/reinforcement_q_learning intermediate/mario_rl_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Deploying PyTorch Models in Production intermediate/flask_rest_api_tutorial beginner/Intro_to_TorchScript_tutorial advanced/cpp_export advanced/super_resolution_with_onnxruntime .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Code Transforms with FX intermediate/fx_conv_bn_fuser intermediate/fx_profiling_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Frontend APIs intermediate/memory_format_tutorial advanced/cpp_frontend advanced/torch-script-parallelism advanced/cpp_autograd .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Extending PyTorch advanced/cpp_extension advanced/torch_script_custom_ops advanced/torch_script_custom_classes advanced/dispatcher advanced/extend_dispatcher .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Model Optimization beginner/profiler intermediate/tensorboard_profiler_tutorial beginner/hyperparameter_tuning_tutorial intermediate/parametrizations intermediate/pruning_tutorial advanced/dynamic_quantization_tutorial intermediate/dynamic_quantization_bert_tutorial intermediate/quantized_transfer_learning_tutorial advanced/static_quantization_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Parallel and Distributed Training beginner/dist_overview intermediate/model_parallel_tutorial intermediate/ddp_tutorial intermediate/dist_tuto intermediate/rpc_tutorial intermediate/rpc_param_server_tutorial intermediate/dist_pipeline_parallel_tutorial intermediate/rpc_async_execution advanced/rpc_ddp_tutorial intermediate/pipeline_tutorial advanced/ddp_pipeline .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Mobile beginner/deeplabv3_on_ios beginner/deeplabv3_on_android