Note

This Python sample is available only with our Professional plan.

Training Pipeline of Optical Flow

This Python script allows you to train a self-supervised Unet Flow Regression Model with preprocessed event-based data in h5 format.

Expected Output

Flow training results:

  • checkpoints (models at different training stages)

  • log files

  • video demos on test dataset

An example of the video demo is shown below:

Setup & requirements

To run the script, you need:

  • path to the output directory

  • path to the training dataset:

    • a folder containing 2 sub folders, named train and val. Each subfolder should contain one or multiple h5 files.

Note

The current model is trained in a self-supervised fashion, but you can easily adapt it to a supervised fashion if you have flow labels at hand. This feature will be officially published in an upcoming release.

How to start

To run the script:

Linux

python3 train_flow.py /path/to/output /path/to/dataset

Windows

python train_flow.py /path/to/output /path/to/dataset

Note

Add an optional argument --feature-extractor to select your specific Unet architecture. We provide four Unet variants:

  • eminet (default): Unet Regression Model with depth-wise separable recurrent convolution for minimal footprint

  • eminet_non_sep: eminet without depth-wise separable convolution

  • midinet: Unet Regression Model with Squeeze excitation layers

  • midinet2: midinet with a fine-tuned middle block (convRNN with tunable depth + residual connection)

To find the full list of options, run:

Linux

python3 train_flow.py -h

Windows

python train_flow.py -h

Warning

In case of “CUDA out of memory” error, you can try to reduce the batch-size, num-tbins or height-width parameters.