Based on the implementation by F. Chollet found here:

• https://keras.io/examples/generative/vae/
• https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py

This project uses Python 3.7 and Tensorflow 2.3.0, but work is in progress to update to the most latest version of Tensorflow. Environments are tracked using Anaconda, and an assortment of cross-platform environment scripts are provided under the environments directory. The earliest working environment is provided under the vae37.yml configuration file. An environment script for the latest Tensorflow version is provided under environment_latest.yaml. Tests are written to ensure the environments are working as expected.

Prior to Tensorflow 2.6, packages for tensorflow, tensorflow-gpu, tensorboard, keras, and build tools and drivers were installed using a mixture of conda dependencies and pip dependencies. Post 2.6, tensorflow-gpu and tensorboard are privided via a pip install of tensorflow. Installation of cudatoolkit and cdnn are still handled with conda, and keras must be installed separately with pip matching the tensorflow version.

We use nosetests to ensure that the environment is installed correctly and the code is running as expected.

To run unit tests, execute the following:

nosetests -s

This will execute the test scripts found in the tests. Adding the -s flag to suppress standard output.

When adding a new class, module, and function, it is important to write tests for it under the tests directory. Each test script will be prepended with test_ for the nosetests to find. For now, the most simple way to implement a test is to use the unittest package. An example test_example.py is shown below:

import unittest

class ExampleTest(unittest.TestCase):
    def test_a_is_not_none(self):
        a = 1
        self.assertIsNotNone(a)
    def test_a_is_one(self):
        a = 1
        self.assertIsEqual(a, 1)
    def test_import_sys(self):
        import sys
        self.assertIsNotNone(sys)

When writing tests, we want to make sure each test function tests exactly one thing. There should be exactly one test assertion for each function, and not a mix of assertions. For each method in a unittest.TestCase class, we prepend the method with test_<testname>, similar to how we name the python script itself. Each method takes a self object of the unittest.TestCase inherited class, and assertions are called directly on self. Standard assert also works. When we get into the mix of testing numpy arrays, we must import Numpy's testing package numpy.testing and use its methods on arrays and matrices.

Example data may be found in the data/ folder (extract the included data.zip folder). Network currently accepts pandas data frames as .csv files, and a custom .txt file format (example also provided). Legacy code in the RGB_Dataset() class is capable of parsing .json files formatted after the MSCOCO data structure, but this has not really been developed. Pandas data frames are the way to go.

Images are downsampled to 128x128x3, and contain a single object instance. Images were pre-padded to maintain aspect ratio for best results.

python train_vae.py --labels=VAE_exampleDataFrame.csv

Default values are found in the main.py argument parser.

python test_vae.py --encoderPath=PATH\TO\ENCODER\FOLDER --decoderPath=PATH\TO\DECODER\FOLDER --labels=PATH\TO\CSV

python -m tensorboard.main --logdir=PATH\TO\LOG_DIR