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Training vNext

This document covers the most basic instructions to get a training session going on an in development version of the system that uses a new data management paradigm.

The instructions assume you are familiar with the original manual instructions here and it's woodblock dataset. We also assume your are working with a Training environment (most likely SSHing into a deployed Azure DSVM

Consider this to be a pre-release version with instructions that will evolve quickly as more automation gets built.

Set up legacy config.ini

  • Go to the config.ini in your training environment
  • Update the values for the following keys:
    • python_file_directory
    • data_dir
    • train_dir
    • inference_output_dir
    • tf_models_location
    • download_location
    • user_folders (Must be set to false for the moment)
    • classes
    • filetype

For instance on an Azure DSVM the values could be

python_file_directory=/data/home/abrig/repos/models/research/active-learning-detect/train_vnext

data_dir=/data/home/abrig/actlrn-data

train_dir=/data/home/abrig/actlrn-data/training

inference_output_dir=knots_inference_graphs

tf_models_location=/data/home/abrig/repos/models/research

download_location=/data/home/abrig/actlrn-downloads

user_folders=False

classes=knot,defect

filetype=*.png

Note: The value for config.ini KEY python_file_directory must be the absolute file path

Set up CLI

Follow the instructions here to make sure your CLI configuration file exists and is set up. Most importantly you should have your CLI config associated with the ALCONFGI environment variable

Set up data dependencies

In a command window navigate to the root (one level up from here) of the repo.

Note: If you have previosuly followed the original manual instructions here your Active Learning repo will be within your Tensorflow repo (e.g ~/repos/models/research/active-learning-detect/). The DSVM deployment script will automate this setup for you.

Run the following command

python -m train_vnext.training start -c ~/repos/models/research/active-learning-detect/config.ini

This command will

  • Verify the config.ini file
  • Download the all the images,
  • Create the human annotated image labels as a CSV
  • Create a list of "in progress" images as a CSV
  • Create the PASCAL VOC file based on the classification name values ("classes" key) in config.ini

Note: You may receive errors about missing configuration keys if your config.ini is not setup correctly

Execute training

Next we will navigate to the training_vnext directory and run the command

sh active_learning_train.sh ~/repos/models/research/active-learning-detect/config.ini

This process can take 10 mins or longer to execute.

Save training session

Navigate back to the root of your Active Learning repo and execute:

python -m train_vnext.training save -c ~/repos/models/research/active-learning-detect/config.ini

This command will

  • Persist the training session's inference graph in cloud storage
  • Log a new training session to the database
  • Log all performance and prediction datat from the training session to the database

Review training performance

To quickly review training and class performance over time use the following queries directly against your deployed database

Class performance over time

SELECT 
    c.classificationname,
    p.avgperf, 
    t.classperfavg
FROM training_info t 
join class_performance p on t.trainingid = p.trainingid
join classification_info c on c.classificationid = p.classificationid
order by t.createddtim desc;

Least confident classifications

SELECT 
    p.imageid, 
    c.classificationname,
    p.boxconfidence, 
    p.imageconfidence 
FROM Prediction_Labels p
join classification_info c on c.classificationid = p.classificationid
where trainingid = (SELECT MAX(trainingid) FROM training_info) 
order by boxconfidence ASC;

Most confident classifications

SELECT 
    p.imageid, 
    c.classificationname,
    p.boxconfidence, 
    p.imageconfidence 
FROM Prediction_Labels p
join classification_info c on c.classificationid = p.classificationid
where trainingid = (SELECT MAX(trainingid) FROM training_info) 
order by boxconfidence DESC;