flownet2-pytorch

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks.

Multiple GPU training is supported, and the code provides examples for training or inference on MPI-Sintel clean and final datasets. The same commands can be used for training or inference with other datasets. See below for more detail.

Inference using fp16 (half-precision) is also supported.

For more help, type

python main.py --help

Network architectures

Below are the different flownet neural network architectures that are provided.

A batchnorm version for each network is also available.

FlowNet2S
FlowNet2C
FlowNet2CS
FlowNet2CSS
FlowNet2SD
FlowNet2

Custom layers

FlowNet2 or FlowNet2C* achitectures rely on custom layers Resample2d or Correlation.

A pytorch implementation of these layers with cuda kernels are available at ./networks.

Note : Currently, half precision kernels are not available for these layers.

Data Loaders

Dataloaders for FlyingChairs, FlyingThings, ChairsSDHom and ImagesFromFolder are available in datasets.py.

Loss Functions

L1 and L2 losses with multi-scale support are available in losses.py.

Installation

# get flownet2-pytorch source
git clone https://github.com/NVIDIA/flownet2-pytorch.git
cd flownet2-pytorch

# install custom layers
bash install.sh

Python requirements

Currently, the code supports python 3

numpy
PyTorch ( == 0.4.1, for

README