update code to use gluon model zoo; update doc

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

@@ -17,32 +17,29 @@
 
 # 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.
+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 with Jetson support along with its dependencies
+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 a Jetson module
+2. Deploy a pre-trained MXNet model for image classifcation on the module
 
-### Who's this tutorial for?
+## 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.
+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.
 
-### 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).
+## Prerequisites
 
-All instructions described in this tutorial can be executed on the any Jetson module directly or via SSH.
+To complete this tutorial, you need:
 
-## Prerequisites
+* 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)
 
-To complete this tutorial, you will need:
+* An SSH connection to the module OR display and keyboard setup to directly open shell on the module
 
-* A Jetson module with Jetpack 4.4 installed
-* [Swapfile](https://help.ubuntu.com/community/SwapFaq) installed (in case of Jetson Nano) for additional memory
+* [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
 
@@ -69,62 +66,44 @@ And we are done. You can test the installation now by importing mxnet from pytho
 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
+from mxnet import gluon
 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)
+
+# 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
-with open(label_file, 'r') as f:
+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]
 
-# 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)]
-
-# infer
-results = net(*mx_img)
-prob = results[0].asnumpy()
-prob = np.squeeze(prob)
-a = np.argsort(prob)[::-1]
-for i in a[0:5]:
-    print('probability=%f, class=%s' %(prob[i], labels[i]))
+# 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
-probability=0.418679, class=n02119789 kit fox, Vulpes macrotis
-probability=0.293494, class=n02119022 red fox, Vulpes vulpes
-probability=0.029321, class=n02120505 grey fox, gray fox, Urocyon cinereoargenteus
-probability=0.026230, class=n02124075 Egyptian cat
-probability=0.022557, class=n02085620 Chihuahua
+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
 ```