Published January 8, 2018 © CC BY-NC-SA

Amazon DeepLens - First Look

A first look at Amazon's DeepLens machine learning platform.

IntermediateProtip1 hour5,159

Things used in this project

Hardware components

Amazon Web Services AWS DeepLens

Software apps and online services

AWS SageMaker

Amazon Web Services AWS Lambda

Amazon Web Services AWS Greengrass

Story

Amazon DeepLens - What Is It?

According to Amazon, DeepLens is "the world’s first deep learning enabled video camera for developers."

From Amazon's DeepLens Portal:

AWS DeepLens is a wireless video camera and API that you can use to learn how to use the latest Artificial Intelligence (AI) tools and technology and develop your own computer vision applications. Use AWS DeepLens to get hands-on experience using a physical camera that runs real-time computer vision models, examples, and tutorials. Get started with AWS DeepLens by using any of the pretrained models that come with your device. As you become proficienct, you can develop, train, and deploy your own models.

AWS DeepLens Hardware and Framework

The AWS DeepLens camera, or device, uses deep convolutional neural networks (CNNs) to analyze visual imagery. You can use the device as a development environment to build computer vision applications. The device includes the following:

A 4 megapixel camera with MJPEG

8 GB of on-board memory

16 GB storage capacity

A 32 GB SD card

WiFi support for both 2.4 GHz and 5 GHz standard dual-band networking

A micro HDMI display port

Audio out and USB ports

AWS DeepLens is powered by an Intel® Atom processor, which is capable of processing 100 billion floating point operations per second (GFLOPS). This gives you all of the compute power that you need to perform inference on your device. The micro HDMI display port, audio out, and USB ports allow you to attach peripherals so you can get creative with your computer vision applications.

AWS DeepLens works with the following AWS services:

Amazon SageMaker for model training and validation

AWS Lambda for running inference against the CNN models

AWS Greengrass for deploying updates and functions to the device

AWS DeepLens is ready to use right out of the box. After you register AWS DeepLens, deploy a sample project, and begin using it to develop your own applications computer vision applications.

How It Works:

The following diagram illustrates how AWS DeepLens works.

How it works

When turned on, the AWS DeepLens captures a video stream.

AWS DeepLens produces two output streams:

Device stream – the video stream is passed through with no processing.

Project stream – the results of the model's processing video frames.

The Inference Lambda function receives unprocessed video frames.

The Inference Lambda function passes the unprocessed frames to the project's deep learning model where they are processed.

The Inference Lambda function receives the processed frames back from the model and then passes the processed frames on in the project stream.

Inference Output

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It has a list price of $249 and it will be released on April 14, 2018.

I attended the 2017 re:Invent conference and was able to get early access to the hardware.

Speaking of hardware...

Hardware

Front View

Side View

Rear View

CPU

Intel Atom® Processor

MEMORY

8GB RAM

Ubuntu OS-16.04 LTS

BUILT-IN STORAGE

16GB Memory (expandable)

GRAPHICS

Intel Gen9 Graphics Engine

Getting Started - Device Registration

Amazon has a really good portal for the initial setup required to prepare your DeepLens and connect it to your network.

Here are some of the key excepts to get you started:

Before using AWS DeepLens, you must register your device, connect it set it up, and verify that it's connected. The following graphic shows where you perform each step:

Setup Location Flow

The easy to follow guide can be found here:

https://docs.aws.amazon.com/deeplens/latest/dg/deeplens-getting-started.html

Your First Project: Hot Dog Recognition

Inspired by HBO's Silicon Valley, you can turn your DeepLens into a "Hot Dog" detector.

It uses a model based on the SqueezeNet deep neural network. The model takes the video stream from your AWS DeepLens device as input, and labels images as a hot dog or not a hot dog. The project uses a pretrained, optimized model that is ready to be deployed to your AWS DeepLens device. After deploying the model, you can use the Live View feature to watch as the model recognizes hot dogs .

You can edit this model by creating Lambda functions that are triggered by the model's output. For example, if the model detects a hot dog, a Lambda function could send you an SMS message.

Editing a Model with Amazon SageMaker

You start with a SqueezeNet object detection model and use Amazon SageMaker to train it to perform binary classification to determine whether an object is a hot dog.

AWS has provided a Jupyter notebook instance, which is open source software for interactive computing. It includes the editing code to execute and explanations for the entire process.

After training the model, you import its artifacts into AWS DeepLens, and create a project. You then watch as your AWS DeepLens detects and identifies hot dogs.

Log into AWS and follow the 9 steps here:

https://docs.aws.amazon.com/deeplens/latest/dg/deeplens-edit-model.html

Contents

Step 1: Create an Amazon S3 Bucket

Step 2: Create an Amazon SageMaker Notebook Instance

Step 3: Edit the Model in Amazon SageMaker

Step 4: Optimize the Model

Step 5: Import the Model

Step 6: Create a Lambda Function

Step 7: Create a New AWS DeepLens Project

Step 8: Review and Deploy the Project

Step 9: View Your Model's Output

Summary

Amazon includes several other sample projects to help kick start your use of the DeepLens and understanding the basics of "Deep Learning".

I hope this gives you some information and direction on getting in line to pick up one of these development kits when they become available in April.

Code

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Editing a model in Amazon SageMaker \n",
    "\n",
    "In this lab you will learn to edit a pre-trained object recognition model to perform a binary classification to classify an object as either a Hotdog or Not Hotdog. This fun exercise is based on a popular sitcom and demonstrates the extensibility of AWS DeepLens. We will edit the model in Amazon SageMaker. Amazon SageMaker is an end to end machine learning platform to train and host your models to production. \n",
    "\n",
    "In this exercise, you will learn to:\n",
    "\n",
    "1. Load your notebook into Amazon SageMaker notebook instance\n",
    "2. Train your model in the notebook\n",
    "3. Save your model artifacts into your S3 bucket\n",
    "4. Import the model artifacts to DeepLens\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1- Create a bucket in S3\n",
    "\n",
    "1. Visit https://s3.console.aws.amazon.com/s3/home?region=us-east-1# to access Amazon S3 console\n",
    "2. Make sure you are on US East (N.Virginia) region\n",
    "3. Click on Create a bucket. \n",
    "4. Name the bucket- deeplens-sagemaker-your-full-name (Please note: It is important that is prefixed with deeplens-sagemaker prefix, else these services cannot access. Click Next\n",
    "5. Give the bucket public read and write access. A new bucket will be created in the account.\n",
    "6. After you create the bucket, create a folder in the bucket and name it test. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Step 2- Amazon SageMaker console\n",
    "\n",
    "1. Visit https://console.aws.amazon.com/sagemaker/home?region=us-east-1#/dashboard to access Amazon SageMaker console\n",
    "\n",
    "2. Make sure you are on US East (N. Virginia) region\n",
    "\n",
    "3. Click on notebook instances\n",
    "\n",
    "4. You will find a notebook instance pre-launched and running for you in the account. It is titled myWorkspace\n",
    "\n",
    "\n",
    "\n",
    "![image.png](https://github.com/aws-samples/reinvent-2017-deeplens-workshop/blob/master/lab%20session%203/assets/1.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3- Open Notebook instance\n",
    "\n",
    "1. Click on myWorkspace notebook instance and click on Open \n",
    "\n",
    "2. Jupyter notebook instance will open. Click sample notebook\n",
    "\n",
    "3. Find the deeplens-hotdog-or-not-hotdog notebook\n",
    "\n",
    "4. Click on the notebook to launch it."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4- Execute the notebook\n",
    "\n",
    "1. Read through the notebook\n",
    "2. Execute each cell by using the play button on the navigation bar or using shift+ enter or cmd + enter\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5- Provide S3 bucket name\n",
    "\n",
    "1. In the last cell, you need to provide the name of the S3 bucket you created earlier\n",
    "\n",
    "2. your s3 file path will be deeplens-sagemaker-your-full-name\n",
    "\n",
    "3. Your prefix path for key variable will be key= 'folder-name/hotdog_or_not...' In our case, since the folder is named as test, it will be key= 'test/hotdog_or_not...'\n",
    "\n",
    "4. After making the above changes, execute the cell.\n",
    "\n",
    "5. The json and params file have been uploaded to your S3 bucket. It takes a couple of minutes for the json and params file to be created.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6- Import the artifacts into DeepLens\n",
    "\n",
    "1. Json and Params file are referred to as model artifacts. The JSON file contains the architecture of the network and the params file consists of the corresponding weights for the architecture\n",
    "\n",
    "2. Visit AWS DeepLens console (https://console.aws.amazon.com/deeplens/home?region=us-east-1#projects)\n",
    "\n",
    "3. Navigate to Models section\n",
    "\n",
    "4. Click on Import Model\n",
    "\n",
    "5. Choose externally trained model\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7- Provide S3 path\n",
    "\n",
    "1. Provide the model artifact path. It will be s3://deeplens-sagemaker-your full name/test\n",
    "\n",
    "2. Provide a name for the model: hotdog-your full name\n",
    "\n",
    "3. Click on Import Model\n",
    "\n",
    "4. Your model (hotdog- your full name) will appear in the list of models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since these models are not yet optimized, they will run on the CPU and will be very slow. For the purpose of this exercise, we have provided the optimized version of the machine learning model and a lambda function that does inference on your AWS DeepLens. This optimized model runs on the on-board GPU to provide accurate and responsive inferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8- Create a lambda function\n",
    "\n",
    "1. Navigate to lab 3 github link (https://github.com/aws-samples/reinvent-2017-deeplens-workshop/tree/master/lab%20session%203) or in S3 bucket here  (paste link here)\n",
    "2. Find the zipfile titled deeplens-squeezenet.zip\n",
    "3. Download the zip file\n",
    "4. Navigate to Lambda console: https://console.aws.amazon.com/lambda/home?region=us-east-1#/functions\n",
    "5. Make sure you are on US East-1- N.Virginia region\n",
    "6. Click on Create function\n",
    "7. Click Author from scratch\n",
    "8. Name it deeplens-squeezenet (the model and function should be identical)\n",
    "9. Choose an existing role\n",
    "10. Choose the existing deeplens_lambda role\n",
    "11. Click Create function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9- Configure your lambda function\n",
    "\n",
    "1. In the Runtime, change it to python 2.7\n",
    "2. In the handler box, change it to greengrassHelloWorld.function_handler\n",
    "3. In the code entry type, upload a zip file. Upload the zip file that you just downloaded\n",
    "4. Click Save\n",
    "5. In Actions tab, click publish and provide your name_sagemaker as the description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 10- Create a Project\n",
    "\n",
    "1. Navigate to Projects in the AWS DeepLens console\n",
    "\n",
    "2. Click on Create a new project\n",
    "\n",
    "3. Click on Create a new blank project template\n",
    "\n",
    "4. Give the project a name- your full name- hotdog- gpu\n",
    "\n",
    "5. Click on add model and choose the deeplens-squeezenet model\n",
    "\n",
    "6. Click on Add function and choose the deeplens-squeezenet function\n",
    "\n",
    "7. Create project\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 11- Deploy project to AWS DeepLens\n",
    "\n",
    "1. Choose the project you just created\n",
    "\n",
    "2. Click deploy to device\n",
    "\n",
    "3. Choose your device\n",
    "\n",
    "4. Review and hit deploy\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 12- View the output\n",
    "\n",
    "1. open the terminal. You can access the top left search icon on Ubuntu and type Terminal\n",
    "\n",
    "2. copy paste the following command: mplater -demuxer lavf -lavfdopts format=mjpeg:probesize=32 /tmp/results.mjpeg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This optimized model will let you access the GPU for running inference. Show a hotdog to your AWS DeepLens and watch its prediction. "
   ]
  }
 ],
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hotdog or Not HotDog\n",
    "\n",
    "Welcome to this SageMaker Notebook! This is an entirely managed notebook service that you can use to create and edit machine learning models. We will be using it today to create a binary image classification model using the Apache MXNet deep learning framework. We will then learn how to delpoy this model onto our DeepLens device.\n",
    "\n",
    "In this notebook we will be to using MXNet's Gluon interface, to download and edit a pre-trained ImageNet model and transform it into binary classifier, which we can use to differentiate between hot dogs and not hot dogs.\n",
    "\n",
    "### Setup\n",
    "\n",
    "Before we start, make sure the kernel in the the notebook is set to the correct one, `condamxnet3.6` which has all the dependencies we will need for this tutorial already installed.\n",
    "\n",
    "First we'll start by importing a bunch of packages into the notebook that you'll need later and installing any required packages that are missing into our notebook kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching package metadata ...........\n",
      "Solving package specifications: .\n",
      "\n",
      "Package plan for installation in environment /home/ec2-user/anaconda3/envs/mxnet_p36:\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    scikit-image: 0.13.0-py36had3c07a_1 --> 0.13.1-py36ha4a0841_0\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    networkx:     2.0-py36h7e96fb8_0    --> 1.11-py36hfb3574a_0  \n",
      "\n",
      "Proceed ([y]/n)? \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "conda install scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/matplotlib/colors.py:680: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.\n",
      "  not cbook.is_string_like(colors[0]):\n",
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/urllib3/contrib/pyopenssl.py:46: DeprecationWarning: OpenSSL.rand is deprecated - you should use os.urandom instead\n",
      "  import OpenSSL.SSL\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "import os\n",
    "import time\n",
    "from collections import OrderedDict\n",
    "import skimage.io as io\n",
    "import numpy as np\n",
    "\n",
    "import mxnet as mx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "The model we will be downloading and editing is [SqueezeNet](https://arxiv.org/abs/1602.07360), an extremely efficient image classification model that achived 2012 State of the Art accuracy on the popular [ImageNet](http://www.image-net.org/challenges/LSVRC/), image classification challenge. SqueezeNet is just a convolutional neural network, with an architecture chosen to have a small number of parameters and to require a minimal amount of computation. It's especially popular for folks that need to run CNNs on low-powered devices like cell phones and other internet-of-things devices, such as DeepLens. The MXNet Deep Learning framework offers squeezenet v1.0 and v1.1 that are pretrained on ImageNet through it's model Zoo.\n",
    "\n",
    "## Pulling the pre-trained model\n",
    "The MXNet model zoo  gives us convenient access to a number of popular models,\n",
    "both their architectures and their pretrained parameters.\n",
    "Let's download SqueezeNet right now with just a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mxnet.gluon import nn\n",
    "from mxnet.gluon.model_zoo import vision as models\n",
    "\n",
    "# get pretrained squeezenet\n",
    "net = models.squeezenet1_1(pretrained=True, prefix='deep_dog_')\n",
    "# hot dog happens to be a class in imagenet.\n",
    "# we can reuse the weight for that class for better performance\n",
    "# here's the index for that class for later use\n",
    "imagenet_hotdog_index = 713"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DeepDog Net\n",
    "\n",
    "In vision networks its common that the first set of layers learns the task of recognizing edges, curves and other important visual features of the input image. We call this feature extraction, and once the abstract features are extracted we can leverage a much simpler model to classify images using these features.\n",
    "\n",
    "We will use the feature extractor from the pretrained squeezenet (every layer except the last one) to build our own classifier for hotdogs. Conveniently, the MXNet model zoo handles the decaptiation for us. All we have to do is specify the number out of output classes in our new task, which we do via the keyword argument `classes=2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): HybridSequential(\n",
      "    (0): Conv2D(64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): Activation(relu)\n",
      "    (2): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=True)\n",
      "    (3): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(16, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(16, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(64, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=True)\n",
      "    (6): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(32, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (7): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(32, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(128, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (8): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(0, 0), ceil_mode=True)\n",
      "    (9): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(48, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(192, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (10): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(48, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(192, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (11): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(64, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (12): HybridSequential(\n",
      "      (0): HybridSequential(\n",
      "        (0): Conv2D(64, kernel_size=(1, 1), stride=(1, 1))\n",
      "        (1): Activation(relu)\n",
      "      )\n",
      "      (1): HybridConcurrent(\n",
      "        (0): HybridSequential(\n",
      "          (0): Conv2D(256, kernel_size=(1, 1), stride=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "        (1): HybridSequential(\n",
      "          (0): Conv2D(256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): Activation(relu)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (classifier): HybridSequential(\n",
      "    (0): Dropout(p = 0.5)\n",
      "    (1): Conv2D(2, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): Activation(relu)\n",
      "    (3): AvgPool2D(size=(13, 13), stride=(13, 13), padding=(0, 0), ceil_mode=False)\n",
      "    (4): Flatten\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "deep_dog_net = models.squeezenet1_1(prefix='deep_dog_', classes=2)\n",
    "deep_dog_net.collect_params().initialize()\n",
    "deep_dog_net.features = net.features\n",
    "\n",
    "# Lets take a look at what this network looks like\n",
    "print(deep_dog_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network can already be used for prediction. However, since it hasn't been finetuned yet so the network performance could not be optimal.\n",
    "\n",
    "Let's test it out by defining a prediction function to feed a local image into the network and get the predicted output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from skimage.color import rgba2rgb\n",
    "\n",
    "def classify_hotdog(net, url):\n",
    "    I = io.imread(url)\n",
    "    if I.shape[2] == 4:\n",
    "        I = rgba2rgb(I)\n",
    "    image = mx.nd.array(I).astype(np.uint8)\n",
    "    image = mx.image.resize_short(image, 256)\n",
    "    image, _ = mx.image.center_crop(image, (224, 224))\n",
    "    image = mx.image.color_normalize(image.astype(np.float32)/255,\n",
    "                                     mean=mx.nd.array([0.485, 0.456, 0.406]),\n",
    "                                     std=mx.nd.array([0.229, 0.224, 0.225]))\n",
    "    image = mx.nd.transpose(image.astype('float32'), (2,1,0))\n",
    "    image = mx.nd.expand_dims(image, axis=0)\n",
    "    out = mx.nd.SoftmaxActivation(net(image))\n",
    "    print('Probabilities are: '+str(out[0].asnumpy()))\n",
    "    result = np.argmax(out.asnumpy())\n",
    "    outstring = ['Not hotdog!', 'Hotdog!']\n",
    "    print(outstring[result])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets download a hot dog image and an image of another object to our local directory to test this model on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2017-11-24 08:32:34--  http://www.wienerschnitzel.com/wp-content/uploads/2014/10/hotdog_mustard-main.jpg\n",
      "Resolving www.wienerschnitzel.com (www.wienerschnitzel.com)... 104.198.109.247\n",
      "Connecting to www.wienerschnitzel.com (www.wienerschnitzel.com)|104.198.109.247|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 22917 (22K) [image/jpeg]\n",
      "Saving to: ‘hotdog_mustard-main.jpg.1’\n",
      "\n",
      "     0K .......... .......... ..                              100%  358K=0.06s\n",
      "\n",
      "2017-11-24 08:32:34 (358 KB/s) - ‘hotdog_mustard-main.jpg.1’ saved [22917/22917]\n",
      "\n",
      "--2017-11-24 08:32:34--  https://www.what-dog.net/Images/faces2/scroll001.jpg\n",
      "Resolving www.what-dog.net (www.what-dog.net)... 191.237.47.20\n",
      "Connecting to www.what-dog.net (www.what-dog.net)|191.237.47.20|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 48316 (47K) [image/jpeg]\n",
      "Saving to: ‘scroll001.jpg’\n",
      "\n",
      "     0K .......... .......... .......... .......... .......   100% 8.58M=0.005s\n",
      "\n",
      "2017-11-24 08:32:34 (8.58 MB/s) - ‘scroll001.jpg’ saved [48316/48316]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "wget http://www.wienerschnitzel.com/wp-content/uploads/2014/10/hotdog_mustard-main.jpg\n",
    "wget https://www.what-dog.net/Images/faces2/scroll001.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities are: [ 0.66635329  0.33364674]\n",
      "Not hotdog!\n",
      "Probabilities are: [ 0.48589313  0.51410687]\n",
      "Hotdog!\n"
     ]
    }
   ],
   "source": [
    "# To make the defined network run quickly we usually hybridize it first. \n",
    "# This also allows us to serialize and export our model\n",
    "deep_dog_net.hybridize()\n",
    "\n",
    "# Let's run the classification on our tow downloaded images to see what our model comes up with\n",
    "classify_hotdog(deep_dog_net, './hotdog_mustard-main.jpg') # check for hotdog\n",
    "classify_hotdog(deep_dog_net, './scroll001.jpg') # check for not-hotdog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deep_dog_net.export('hotdog_or_not_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions are a bit off so we can download a set of new parameters for the model that we have pre-optimized through a \"fine tuning\" process, where we retrained the model with images of hotdogs and not hotdogs. We can then apply these new parameters to our model to make it even more accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/deep-dog-5a342a6f.params into deep-dog-5a342a6f.params successfully\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities are: [ 0.37135434  0.62864566]\n",
      "Hotdog!\n",
      "Probabilities are: [ 0.99881637  0.00118361]\n",
      "Not hotdog!\n"
     ]
    }
   ],
   "source": [
    "from mxnet.test_utils import download\n",
    "\n",
    "download('https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/deep-dog-5a342a6f.params',\n",
    "         overwrite=True)\n",
    "deep_dog_net.load_params('deep-dog-5a342a6f.params', mx.cpu())\n",
    "deep_dog_net.hybridize()\n",
    "classify_hotdog(deep_dog_net, './hotdog_mustard-main.jpg')\n",
    "classify_hotdog(deep_dog_net, './scroll001.jpg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions seem reasonable, so we can export this as a serialized model to our local dirctory. This is a simple one line command, which produces a set of two files: a json file holding the network architecture, and a params file holding the parameters the network learned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deep_dog_net.export('hotdog_or_not_model_v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's push this serialized model to S3, where we can then optimize it for our DeepLense device and then push it down onto our device for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:botocore.vendored.requests.packages.urllib3.connectionpool:Starting new HTTPS connection (1): sts.amazonaws.com\n",
      "INFO:botocore.vendored.requests.packages.urllib3.connectionpool:Starting new HTTPS connection (1): s3.amazonaws.com\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:iam::622803848910:role/SageMaker_role_IM\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "s3.Object(bucket_name='sagemaker-test1', key='hotdog_or_not_model-0000.params')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import boto3\n",
    "import re\n",
    "\n",
    "assumed_role = boto3.client('sts').get_caller_identity()['Arn']\n",
    "s3_access_role = re.sub(r'^(.+)sts::(\\d+):assumed-role/(.+?)/.*$', r'\\1iam::\\2:role/\\3', assumed_role)\n",
    "print(s3_access_role)\n",
    "s3 = boto3.resource('s3')\n",
    "bucket= 'your s3 bucket name here' \n",
    "\n",
    "json = open('hotdog_or_not_model-symbol.json', 'rb')\n",
    "params = open('hotdog_or_not_model-0000.params', 'rb')\n",
    "s3.Bucket(bucket).put_object(Key='test/hotdog_or_not_model-symbol.json', Body=json)\n",
    "s3.Bucket(bucket).put_object(Key='test/hotdog_or_not_model-0000.params', Body=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Environment (conda_mxnet_p36)",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
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}

Credits

Paul Langdon

49 projects • 320 followers

Working as a cloud architect for an IoT hardware company

Amazon DeepLens - First Look

Things used in this project

Hardware components

Software apps and online services

Story

Amazon DeepLens - What Is It?

How It Works:

Hardware

Getting Started - Device Registration

Your First Project: Hot Dog Recognition

Summary

Schematics

AWS DeepLens Front

AWS DeepLens Side

AWS DeepLens Back

Code

setup.ipynb

deeplens-hotdog-or-not-hotdog.ipynb

Credits

Paul Langdon

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Amazon DeepLens - First Look

Amazon DeepLens - First Look

Things used in this project

Hardware components

Software apps and online services

Story

Amazon DeepLens - What Is It?

How It Works:

Hardware

Getting Started - Device Registration

Your First Project: Hot Dog Recognition

Summary

Schematics

AWS DeepLens Front

AWS DeepLens Side

AWS DeepLens Back

Code

setup.ipynb

deeplens-hotdog-or-not-hotdog.ipynb

Credits

Paul Langdon

Comments

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