Structured Uncertainty in the Observation Space of Variational Autoencoders

This repository contains the code for the paper

J. Langley, M. Monteiro, C, Jones, N. Pawlowski, B. Glocker. Structured Uncertainty in the Observation Space of Variational Autoencoders. Transactions on Machine Learning Research. 2022 [OpenReview]

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Interactive Editor Usage Example

Installation

A virtual environment is recommended for this project. Create and activate a virtual environment as follows

python3 -m venv venv
source venv/bin/activate

Install required packages:

pip install -r requirements.txt

Caveat

If running fails with a CUDA Runtime Error: make sure you have installed the correct torch binary compiled with your CUDA version.

• You can find the CUDA version by running

  nvidia-smi

• You can find the version of CUDA torch was compiled with by running the following python

  import torch
  torch.version.cuda

If the do not match (at least in major version number), you will need to install the correct version of torch (and torchvision).

To do so run the following command:

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

With the correct torch (and torchvision) versions and and the right CUDA-compiled version. For example, the above command installs torch and torchvision versions 1.8.0 and 0.9.0 respectively with CUDA-compiled 11.1.

Now check torch.version.cuda matches (in at least major version number) nvidia-smi.

Good to go!

Training

python main.py

For options run python src/main.py --help to show the following:

usage: main.py [-h] [-g] [-f64] [-dnv] [-d] [-t] [-s] [-e EPOCHS]
               [-b BATCH_SIZE] [-lr LEARNING_RATE] [-f LATENT_FEATURES]
               [-r RANK] [-kl TARGET_KL] [-var TARGET_VAR] [-p PATCH_SIZE]
               [-pre PRE_TRAIN] [--grayscale] [-cp CHECKPOINTS]
               [--resume RESUME]

Train and evaluate network

optional arguments:
  -h, --help            show this help message and exit
  -g, --gpu             Evaluate using GPU if available
  -f64, --float64       Evaluate using 64-bit floats. 32 otherwise
  -dnv, --dist-no-val   Disable distribution validation checks
  -d, --deterministic   Set deterministic/repeatable computation
  -t, --title           Enable title prompt
  -s, --save            Save model_dict upon run completion
  -e EPOCHS, --epochs EPOCHS
                        Number of epochs to train for
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        Batch size
  -lr LEARNING_RATE, --learning-rate LEARNING_RATE
                        Learning rate
  -f LATENT_FEATURES, --latent-features LATENT_FEATURES
                        Dimensionality of latent space
  -r RANK, --rank RANK  Rank of the decoder covariance
  -kl TARGET_KL, --target-kl TARGET_KL
                        Target KL loss (per batch item)
  -var TARGET_VAR, --target-var TARGET_VAR
                        Target mean variance of the output distribution
  -p PATCH_SIZE, --patch-size PATCH_SIZE
                        Size of the ground truth patch used for correction in the visualiser
  -pre PRE_TRAIN, --pre-train PRE_TRAIN
                        Number of epochs to pre-train the model for
  --grayscale           Run in grayscale
  -cp CHECKPOINTS, --checkpoints CHECKPOINTS
                        Epoch interval between checkpoints. No checkpoints
                        otherwise
  --resume RESUME       ID of a previous run to resume running (if specified).
                        Resuming with modified args should be attempted with
                        caution

Checkpoints

Use the above described --checkpoints argument, this program will save checkpoints at a user-specified interval of epochs. Resuming a checkpointed run can be easily done by specifying the --resume argument with the respective ID.

Please note that resuming a checkpointed run does not remember the previous program arguments and should be called with the same arguments. Resuming a run with different arguments has been implemented, but should only be attempted with caution and expertise.

When a run that has been checkpointed finishes the final time, it is possible to make tensorboard see the individual checkpointed runs as one continuous run. To do this, combine the events.out.tfevents.... under one subdirectory within the runs folder.

Logging

This project uses tensorboard for all logging

tensorboard --logdir runs --samples_per_plugin images=9999