python main.py --help

Network architectures

Custom layers

Data Loaders

Loss Functions

Installation

# get flownet2-pytorch sourcegit clone https://github.com/NVIDIA/flownet2-pytorch.gitcd flownet2-pytorch# install custom layersbash install.sh

Python requirements

Converted Caffe Pre-trained Models

Inference

# Example on MPISintel Clean   python main.py --inference --model FlowNet2 --save_flow --inference_dataset MpiSintelClean \--inference_dataset_root /path/to/mpi-sintel/clean/dataset \--resume /path/to/checkpoints

Training and validation

# Example on MPISintel Final and Clean, with L1Loss on FlowNet2 modelpython main.py --batch_size 8 --model FlowNet2 --loss=L1Loss --optimizer=Adam --optimizer_lr=1e-4 \--training_dataset MpiSintelFinal --training_dataset_root /path/to/mpi-sintel/final/dataset  \--validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset# Example on MPISintel Final and Clean, with MultiScale loss on FlowNet2C model python main.py --batch_size 8 --model FlowNet2C --optimizer=Adam --optimizer_lr=1e-4 --loss=MultiScale --loss_norm=L1 \--loss_numScales=5 --loss_startScale=4 --optimizer_lr=1e-4 --crop_size 384 512 \--training_dataset FlyingChairs --training_dataset_root /path/to/flying-chairs/dataset  \--validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset

Results on MPI-Sintel

Reference

@InProceedings{IMKDB17,  author       = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",  title        = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",  month        = "Jul",  year         = "2017",  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"}

@misc{flownet2-pytorch,  author = {Fitsum Reda and Robert Pottorff and Jon Barker and Bryan Catanzaro},  title = {flownet2-pytorch: Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks},  year = {2017},  publisher = {GitHub},  journal = {GitHub repository},  howpublished = {\url{https://github.com/NVIDIA/flownet2-pytorch}}}

Related Optical Flow Work from Nvidia

Acknowledgments