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
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
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.
Dataloaders for FlyingChairs, FlyingThings, ChairsSDHom and ImagesFromFolder are available in datasets.py.
L1 and L2 losses with multi-scale support are available in losses.py.
# get flownet2-pytorch source
git clone https://github.com/NVIDIA/flownet2-pytorch.git
cd flownet2-pytorch
# install custom layers
bash install.sh
Currently, the code supports python 3
- numpy
- PyTorch ( == 0.4.1, for <= 0.4.0 see branch python36-PyTorch0.4)
- scipy
- scikit-image
- tensorboardX
- colorama, tqdm, setproctitle
We've included caffe pre-trained models. Should you use these pre-trained weights, please adhere to the license agreements.
- FlowNet2[620MB]
- FlowNet2-C[149MB]
- FlowNet2-CS[297MB]
- FlowNet2-CSS[445MB]
- FlowNet2-CSS-ft-sd[445MB]
- FlowNet2-S[148MB]
- FlowNet2-SD[173MB]
# 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
# Example on MPISintel Final and Clean, with L1Loss on FlowNet2 model
python 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
If you find this implementation useful in your work, please acknowledge it appropriately and cite the paper:
@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}}
}
Code (in Caffe and Pytorch): PWC-Net
Paper : PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume.
Parts of this code were derived, as noted in the code, from ClementPinard/FlowNetPytorch.
