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PWCNet reimplement using paddlepaddle DyGraph

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume.

Environment

cenntos7
paddle develop version (after 20191201, recommended commit id: cdba41af4dfd7d58cf90) install from source
python3.7
SciPy 1.1.0

code will update for paddle v1.7 later.

Compile correlation op

cd correlation_op
sh make.sh

Datasets

1.Please download the FlyingChairs dataset and FlyingChairs_train_val.txt from https://lmb.informatik.uni-freiburg.de/resources/datasets

Or you can use ./data/download.sh to download datasets.

We split the data to train and val by using FlyingChairs_train_val.txt with 1 for train and 2 for val.

Inference

Note that the paddle models pwc_net_paddle.pdparams and pwc_net_chairs_paddle.pdparams are transferred from the pytorch pth files pwc_net.pth.tar and pwc_net_chairs.pth.tar.

Run

python infer.py
Input img1 Input img2
prediction with pwc_net_paddle.pdparams prediction with pwc_net_chairs_paddle.pdparams

First Train with L2 loss

A single gpu is supported. Multi gpus will be supported later.

You should check parameters in my_args.py as you like.

And change them in train.sh.

--data_root
--train_val_txt
--batch_size

Then run

./train.sh

Some results during training can be seen

./img1.png
./img2.png
./hsv_pd.png # ground truth
./hsv_predict.png # output of model

Finetune with L1 loss

finetune from your best pretrain model by adding --pretrained your_best_model_name eg. --pretrained epoch_7_pwc_net_paddle

Run

./finetune.sh

Note

This code reimplement PWCNet like the code of https://github.com/NVlabs/PWC-Net If you want to want to train like the paper

@InProceedings{Sun2018PWC-Net,
  author    = {Deqing Sun and Xiaodong Yang and Ming-Yu Liu and Jan Kautz},
  title     = {{PWC-Net}: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume},
  booktitle = CVPR,
  year      = {2018},
}

Please use all the datasets in ./data/download.sh if you like. And use the code in ./data/datasets.py.

Reference works

https://github.com/NVlabs/PWC-Net
https://github.com/ClementPinard/FlowNetPytorch
https://github.com/NVIDIA/flownet2-pytorch/blob/master/datasets.py