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Lesson 2 - Machine Learning

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In this lesson, you will learn two new concepts 1) building a rune using ML model 2) processing the data using proc-blocks

Before jumping to building a rune with the ML model, let's first revise some ML concepts.

Machine learning workflows typically have the following steps:

  1. prepare dataset
  2. train model
  3. deploy model

To successfully run a deployed model on edge, the input has to be processed on the deployment in the same way as during the training of the model. Therefore, consistency is required across the whole pipeline - the same operators have to be used during deployment as used during training.

To achieve that for an existing model, there are two options:

  1. Match the preprocessing function and distribution of the new data with that of the originally trained model
  2. Re-calibrate the model using the preprocessing function used in the deployment and using data drawn from the distribution encountered in the deployment scenario

We have to process the data before feeding it into the model and after receiving the output. The processing done before feeding it into the model is called pre-processing, and processing done on the model output is called post-processing.

Let's start with taking a quick look at the below Runefile.

version: 1
image: runicos/base
pipeline:
image:
capability: IMAGE
outputs:
- type: U8
dimensions: [1, 96, 96, 1]
args:
pixel-format: "@PixelFormat::GrayScale"
height: 96
width: 96
person_detection:
model: "./person_detection.tflite"
inputs:
- image
outputs:
- type: U8
dimensions: [1, 1, 1, 3]
most_confident_index:
proc-block: "hotg-ai/proc-blocks#most_confident_indices"
inputs:
- person_detection
outputs:
- type: U32
dimensions: [1]
args:
count: 1
label:
proc-block: "hotg-ai/proc-blocks#label"
inputs:
- most_confident_index
outputs:
- type: UTF8
dimensions: [1]
args:
labels:
- unknown
- person_prob
- not_person_prob
serial:
out: SERIAL
inputs:
- label

You can notice a lot of new things in the Runefile. Let's start understanding it one by one.

Choose a Model#

The first step of creating a Rune is to find (or train) a Machine Learning Model that matches your application.

Developers embedding Rune in their applications can provide inference backends for any model type they want, but by default, Rune comes with support for TensorFlow Lite.

Several pre-trained TensorFlow Lite models are available for download from the TF Hub.

In the above Runefile, we use a person detection model, which takes grayscale image as input and detects whether a person is present in the image. You may not find this model on TF Hub but you can use it from our repo.

Now, we have selected the model. First, we would like to know model input/output information. This information will be used to build the Runefile. To find this, we will run

rune model-info person_detection.tflite

We get:

Inputs:
input: u8[1,96,96,1]
Outputs:
MobilenetV1/Logits/Conv2d_1c_1x1/act_quant/FakeQuantWithMinMaxVars: u8[1,1,1,3]

Comparing input with the image format: [batch_size, height, width, channels], we can see the model will take a 96 x 96 grayscale image (because the channel is 1). The input image type is u8, i.e., all the values will lie between [0, 255]. The output is an array of size [1, 1, 1, 3].

Capability#

Now, we have got all the information about our model. We will start by setting our first node name as the image (you can change it to anything suitable to you) and will define capability to IMAGE to take an image as input to our rune. We will set the output type to u8 and dimensions to [1, 96, 96, 1] because, as we saw above, this is the format needed by our model. We will set our argument like width, height, and format of image in the args section.

Basically, we process the data until we change it to the format needed by our model. In the later chapters, we will have to include some proc-blocks before our model node to change the input data to the format required by our model node.

The IMAGE capability take the following arguments:

  • width - the image's width in pixels
  • height - the image's height in pixels
  • pixel-format - the format used by the pixels. Possible values are:
    • @PixelFormat::Grayscale
    • @PixelFormat::RGB
    • @PixelFormat::BGR
image:
capability: IMAGE
outputs:
- type: U8
dimensions: [1, 96, 96, 1]
args:
pixel-format: "@PixelFormat::GrayScale"
height: 96
width: 96

You can find more details on how to select capability and arguments supported by each capability here (highly recommended).

Every node in the Runefile will have inputs and outputs (except the capability node, which only has output because it's the starting of our pipeline). You make a pipeline for data flow by connecting output of node-1 to the input of node-2.

Model#

We have already chosen our model. Now, it's time to define our model node in the Runefile. First, let's give our model node a name. I have given it person_detection as this the name of our model. You can change it to something you find suitable. Next, we will provide a path to our model file in the model and connect the image node to the model node by setting input to image. Finally, we will select output type to u8 and output dimensions to [1, 1, 1, 3], which we got through model-info. The model will return score for three labels:

  • unknown
  • person_prob
  • not_person_prob
person_detection:
model: "./person_detection.tflite"
inputs:
- image
outputs:
- type: U8
dimensions: [1, 1, 1, 3]

We have got the output, and we would like to clean it by transforming the output data so that it could be converted to a valid format. Next, we will use a proc-block to perform these transformations.

Proc-Block#

A procedural block (aka "proc block") is a Rust crate that exposes functionality for transforming data from one form to another. We have created a list of proc-block for you. You can find it here. Proc block stages are differentiated using a proc-block: hotg-ai/proc-blocks#........ property.

Common properties supported by proc blocks are:

  • args
  • inputs
  • outputs

We have got the output score for all three classes from our model. We want to find out which class has the highest score so that later we can print that class name as output. To get the index of class with the highest confidence value, we will use most_confident_indices proc-block (A proc block which, when given a list of confidences, will return the indices of the top N most confident values). Let's start by setting our proc-block node name to most_confident_indices. Next set following property:

  1. proc-block: hotg-ai/proc-block#most_confident_indices -> path of the proc-block set to it's location in the github repo
  2. Connect the output of the model node to the input of the proc-block.
  3. Define outputs:
  • type: we will get an index that is a positive integer so you can set it to u8, u16, u32 etc.
  • dimensions: It's a multiclass classification model, so we have set it to [1]. If you are using a multilabel classification model, you can change it to [2] , [3] or at your convenience.
  1. args is used to return a count of indices from the top N most confident values. You will have to set the count same as the output dimension.

Now, let's write proc-block node using above information

most_confident_index:
proc-block: "hotg-ai/proc-blocks#most_confident_indices"
inputs:
- person_detection
outputs:
- type: U32
dimensions: [1]
args:
count: 1

Till now, we have got an index of the most confident index. Next, we would like to assign a label to this index. For this, we will use the label proc-block (A proc block which, when given a set of indices, will return their associated labels). We will follow the above approach and will connect output of the most_confident_indices node to the input of our label node. In args property, we will add all the labels of our model. We will get string as output, so we will have to set the output type to utf8.

label:
proc-block: "hotg-ai/proc-blocks#label"
inputs:
- most_confident_index
outputs:
- type: UTF8
dimensions: [1]
args:
labels:
- unknown
- person_prob
- not_person_prob

out#

Finally, connect the output of the label proc-block node to the input of serial node.

serial:
out: SERIAL
inputs:
- label

If you have followed the above steps correctly, you have got your Runefile.

Build the Rune#

First, create an empty Runefile.yml and write the above code in the Runefile.

cd /workspace/tutorials/lessons/lesson-2
touch Runefile.yml

Ok, so you have written your Runefile.yml Let's now compile and test what you have so far.

Run the below command to build the rune

rune build Runefile.yml

It will create a lesson-2.rune for you.

Test your Rune and see the output you are getting. Run the below command in the terminal.

rune run lesson-2.rune --image image_grayscale.png