mseth10 commented on a change in pull request #18434: URL: https://github.com/apache/incubator-mxnet/pull/18434#discussion_r432744191
########## File path: docs/python_docs/python/tutorials/deploy/inference/image_classification_jetson.md ########## @@ -0,0 +1,130 @@ +<!--- Licensed to the Apache Software Foundation (ASF) under one --> +<!--- or more contributor license agreements. See the NOTICE file --> +<!--- distributed with this work for additional information --> +<!--- regarding copyright ownership. The ASF licenses this file --> +<!--- to you under the Apache License, Version 2.0 (the --> +<!--- "License"); you may not use this file except in compliance --> +<!--- with the License. You may obtain a copy of the License at --> + +<!--- http://www.apache.org/licenses/LICENSE-2.0 --> + +<!--- Unless required by applicable law or agreed to in writing, --> +<!--- software distributed under the License is distributed on an --> +<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> +<!--- KIND, either express or implied. See the License for the --> +<!--- specific language governing permissions and limitations --> +<!--- under the License. --> + +# Image Classication using pretrained ResNet-50 model on Jetson module + +This tutorial shows how to install latest MXNet v1.6 with Jetson support and use it to deploy a pre-trained MXNet model for image classification on a Jetson module. + +## What's in this tutorial? + +This tutorial shows how to: + +1. Install MXNet v1.6 with Jetson support along with its dependencies + +2. Deploy a pre-trained MXNet model for image classifcation on a Jetson module + +### Who's this tutorial for? + +This tutorial would benefit developers working on any Jetson module implementing a deep learning application. It assumes that readers have a Jetson module setup, are familiar with the Jetson working environment and are somewhat familiar with deep learning using MXNet. + +### How to use this tutorial? + +To follow this tutorial, you need to setup a [Jetson module](https://developer.nvidia.com/embedded/develop/hardware) and install latest [Jetpack 4.4](https://docs.nvidia.com/jetson/jetpack/release-notes/) using NVIDIA [SDK manager](https://developer.nvidia.com/nvidia-sdk-manager). + +All instructions described in this tutorial can be executed on the any Jetson module directly or via SSH. + +## Prerequisites + +To complete this tutorial, you will need: + +* A Jetson module with Jetpack 4.4 installed +* [Swapfile](https://help.ubuntu.com/community/SwapFaq) installed (in case of Jetson Nano) for additional memory + +## Installing MXNet v1.6 with Jetson support + +We start by installing MXNet dependencies +```bash +sudo apt-get update +sudo apt-get install -y git build-essential libopenblas-dev libopencv-dev python3-pip +sudo pip3 install -U pip +``` + +Then we download and install MXNet v1.6 wheel with Jetson support +```bash +wget https://mxnet-public.s3.us-east-2.amazonaws.com/install/jetson/1.6.0/mxnet_cu102-1.6.0-py2.py3-none-linux_aarch64.whl +sudo pip3 install mxnet_cu102-1.6.0-py2.py3-none-linux_aarch64.whl +``` + +And we are done. You can test the installation now by importing mxnet from python3 +```bash +>>> python3 -c 'import mxnet' +``` + +## Running a pre-trained ResNet-50 model on Jetson + +We are now ready to run a pre-trained model and run inference on a Jetson module. In this tutorial we are using ResNet-50 model trained on Imagenet dataset. We run the following classification script with either cpu/gpu context using python3. + +```python +from mxnet.gluon import nn +import mxnet as mx +import numpy as np +import urllib.request +import cv2 + +# set context +ctx = mx.gpu() +dtype = 'float32' +bsize = 1 + +# download model files +path = 'http://data.mxnet.io/models/imagenet/' +symbol,_ = urllib.request.urlretrieve(path+'resnet/50-layers/resnet-50-symbol.json') +params,_ = urllib.request.urlretrieve(path+'resnet/50-layers/resnet-50-0000.params') +label_file,_ = urllib.request.urlretrieve(path+'synset.txt') + +# load model +input_names = ['data', 'softmax_label'] +net = nn.SymbolBlock.imports(symbol, input_names, params, ctx) +net.cast(dtype) +net.hybridize(static_alloc=True, static_shape=True) + +# load labels +with open(label_file, 'r') as f: + labels = [l.rstrip() for l in f] + +# load image +img_file,_ = urllib.request.urlretrieve('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true') +img = cv2.imread(img_file) +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) +img = cv2.resize(img, (224, 224,)) +img = np.swapaxes(img, 0, 2) +img = np.swapaxes(img, 1, 2) + +# format input +batch = mx.nd.zeros((bsize,) + img.shape) +for i in range(bsize): + batch[i] = img +inputs = batch.astype(dtype) +mx_img = [mx.nd.array(inputs,ctx), mx.nd.zeros((bsize,),ctx)] Review comment: We need not pass the second parameter anyway. I'll remove it. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
