add image classification tutorial for jetson

docs/python_docs/python/tutorials/deploy/inference/image_classification_jetson.md

@@ -0,0 +1,117 @@
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+
+# Image Classication using pretrained ResNet-50 model on Jetson module
+
+This tutorial shows how to install 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 along with its dependencies on a Jetson module (This tutorial has been tested on Jetson Xavier AGX and Jetson Nano modules)
+
+2. Deploy a pre-trained MXNet model for image classifcation on the module
+
+## Who's this tutorial for?
+
+This tutorial would benefit developers working on Jetson modules implementing deep learning applications. It assumes that readers have a Jetson module setup with Jetpack installed, are familiar with the Jetson working environment and are somewhat familiar with deep learning using MXNet.
+
+## Prerequisites
+
+To complete this tutorial, you need:
+
+* A [Jetson module](https://developer.nvidia.com/embedded/develop/hardware) setup with [Jetpack 4.4](https://docs.nvidia.com/jetson/jetpack/release-notes/) installed using NVIDIA [SDK Manager](https://developer.nvidia.com/nvidia-sdk-manager)
+
+* An SSH connection to the module OR display and keyboard setup to directly open shell on the module
+
+* [Swapfile](https://help.ubuntu.com/community/SwapFaq) installed, especially on Jetson Nano for additional memory (increase memory if the inference script terminates with a `Killed` message)
+
+## Installing MXNet v1.6 with Jetson support
+
+To install MXNet with Jetson support, you can follow the [installation guide](https://mxnet.apache.org/get_started/jetson_setup) on MXNet official website.
+
+Alternatively, you can also directly install MXNet v1.6 wheel with Jetson support, hosted on a public s3 bucket. Here are the steps to install this wheel:
+
+*WARNING: this MXNet wheel is provided for your convenience but it contains packages that are not provided nor endorsed by the Apache Software Foundation.
+As such, they might contain software components with more restrictive licenses than the Apache License and you'll need to decide whether they are appropriate for your usage. Like all Apache Releases, the
+official Apache MXNet (incubating) releases consist of source code only and are found at https://downloads.apache.org/incubator/mxnet .*
+
+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 import gluon
+import mxnet as mx
+
+# set context
+ctx = mx.gpu()
+
+# load pre-trained model
+net = gluon.model_zoo.vision.resnet50_v1(pretrained=True, ctx=ctx)
+net.hybridize(static_alloc=True, static_shape=True)
+
+# load labels
+lbl_path = gluon.utils.download('http://data.mxnet.io/models/imagenet/synset.txt')
+with open(lbl_path, 'r') as f:
+    labels = [l.rstrip() for l in f]
+
+# download and format image as (batch, RGB, width, height)
+img_path = gluon.utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true')
+img = mx.image.imread(img_path)
+img = mx.image.imresize(img, 224, 224) # resize
+img = mx.image.color_normalize(img.astype(dtype='float32')/255,
+                               mean=mx.nd.array([0.485, 0.456, 0.406]),
+                               std=mx.nd.array([0.229, 0.224, 0.225])) # normalize
+img = img.transpose((2, 0, 1)) # channel first
+img = img.expand_dims(axis=0) # batchify
+img = img.as_in_context(ctx)
+
+prob = net(img).softmax() # predict and normalize output
+idx = prob.topk(k=5)[0] # get top 5 result
+for i in idx:
+    i = int(i.asscalar())
+    print('With prob = %.5f, it contains %s' % (prob[0,i].asscalar(), labels[i]))
+```
+
+After running the above script, you should get the following output showing the five classes that the image most relates to with probability:
+```bash
+With prob = 0.41940, it contains n02119789 kit fox, Vulpes macrotis
+With prob = 0.28096, it contains n02119022 red fox, Vulpes vulpes
+With prob = 0.06857, it contains n02124075 Egyptian cat
+With prob = 0.03046, it contains n02120505 grey fox, gray fox, Urocyon cinereoargenteus
+With prob = 0.02770, it contains n02441942 weasel
+```

docs/python_docs/python/tutorials/deploy/inference/index.rst

@@ -58,6 +58,12 @@ The following tutorials will help you learn how to deploy MXNet models for infer
 
       Example of running a wine detector on a raspberry pi.
 
+   .. card::
+      :title: Image Classification on Jetson
+      :link: image_classification_jetson.html
+
+      Example of running a pretrained image classification model on a Jetson module.
+
 
 .. toctree::
    :hidden: