Dynamic dependencies

• Inference can now be performed without Horovod on a single GPU (training and evaluation still require Horovod).
• Improved saving functionalities during evaluation, including saving RGB images, inverse depth map visualizations, and depth maps as .npz arrays and .png images.

CVPR 2020 release

Complete set of features described in our CVPR 2020 oral: 3D Packing for Self-Supervised Monocular Depth Estimation.

Additions from v0.1.0:

• Velocity loss (see VelSupModel)
• PackNetSlim01 (faster version of PackNet)
• Support for multi-camera loading on the DGP dataset
• Support for fp16 at inference time

First release of training code

v0.1.0

First release of training code (#11)