# Get unsupervised video interpolation source codes
git clone https://github.com/NVIDIA/unsupervised-video-interpolation.git
cd unsupervised-video-interpolation
mkdir pretrained_models

# Build Docker Image
docker build -t unsupervised-video-interpolation -f Dockerfile .

python3 train.py --help

#Example creation of interpolated videos, where we interleave low FPS input frames with one or more interpolated intermediate frames.
python3 eval.py --model CycleHJSuperSloMo --num_interp 7 --flow_scale 2 --val_file ${/path/to/input/sequences} \
    --name ${video_name} --save ${/path/to/output/folder} --post_fix ${output_image_tag} \
    --resume ${/path/to/pre-trained/model} --write_video

# Evaluation of model trained with CC alone on Adobe-30fps dataset
# PSNR: 34.47, SSIM: 0.946, IE: 5.50
python3 eval.py --model CycleHJSuperSloMo --num_interp 1 --flow_scale 1 --val_file /path/to/ucf/root \
    --resume ./pretrained_models/fully_unsupervised_adobe30fps.pth

# Evaluation of model trained with CC alone on Battlefield-30fps dataset
# PSNR: 34.55, SSIM: 0.947, IE: 5.38
python3 eval.py --model CycleHJSuperSloMo --num_interp 1 --flow_scale 1 --val_file /path/to/ucf/root \
    --resume ./pretrained_models/fully_unsupervised_battlefield30fps.pth

# Evaluation of model trained with CC alone on SlowFlow-30fps train split
# PSNR: 32.35, SSIM: 0.886, IE: 6.78
python3 eval.py --model CycleHJSuperSloMo --num_interp 7 --flow_scale 2 --val_file /path/to/SlowFlow/val \
    --resume ./pretrained_models/unsupervised_random2slowflow.pth

# Evaluation of model finetuned with CC+PS losses on SlowFlow-30fps train split.
# Model pre-trained with supervision on Adobe-240fps.
# PSNR: 33.05, SSIM: 0.890, IE: 6.62
python3 eval.py --model CycleHJSuperSloMo --num_interp 7 --flow_scale 2 --val_file /path/to/SlowFlow/val \
    --resume ./pretrained_models/unsupervised_adobe2slowflow.pth

# Evaluation of model finetuned with CC+PS losses on SlowFlow-30fps train split.
# Model pre-trained with supervision on Adobe+YouTube-240fps.
# PSNR: 33.20, SSIM: 0.891, IE: 6.56
python3 eval.py --model CycleHJSuperSloMo --num_interp 7 --flow_scale 2 --val_file /path/to/SlowFlow/val \
    --resume ./pretrained_models/unsupervised_adobe+youtube2slowflow.pth

We simply use the same commands used for SlowFlow, but setting `--num_interp 41`
and the corresponding `--resume *2sintel.pth` pre-trained models should lead to the number we presented in our papers.

# Evaluation of model trained with Paird-GT on Adobe-240fps dataset
# PSNR: 34.63, SSIM: 0.946, IE: 5.48
python3 eval.py --model HJSuperSloMo --num_interp 1 --flow_scale 1 --val_file /path/to/ucf/root \
    --resume ./pretrained_models/baseline_superslomo_adobe.pth

# Evaluation of model trained with paird-GT on Adobe-240fps dataset
# PSNR: 32.84, SSIM: 0.887, IE: 6.67
python3 eval.py --model HJSuperSloMo --num_interp 7 --flow_scale 2 --val_file /path/to/SlowFlow/val \
    --resume ./pretrained_models/baseline_superslomo_adobe.pth

# Evaluation of model trained with paird-GT on Adobe+YouTube-240fps dataset
# PSNR: 33.13, SSIM: 0.889, IE: 6.63
python3 eval.py --model HJSuperSloMo --num_interp 7 --flow_scale 2 --val_file /path/to/SlowFlow/val \
    --resume ./pretrained_models/baseline_superslomo_adobe+youtube.pth

# CC alone: Fully unsupervised training on SlowFlow and evaluation on SlowFlow
# SlowFlow/val target PSNR: 32.35, SSIM: 0.886, IE: 6.78
python3 -m torch.distributed.launch --nproc_per_node=16 train.py --model CycleHJSuperSloMo \
    --flow_scale 2.0 --batch_size 2 --crop_size 384 384 --print_freq 1 --dataset CycleVideoInterp \
    --step_size 1 --sample_rate 0 --num_interp 7 --val_num_interp 7 --skip_aug --save_freq 20 --start_epoch 0 \
    --train_file /path/to/SlowFlow/train --val_file SlowFlow/val --name unsupervised_slowflow --save /path/to/output 

# --nproc_per_node=16, we use a total of 16 V100 GPUs over two nodes.

# CC + PS: Unsupervised fine-tuning on SlowFlow with a baseline model pre-trained on Adobe+YouTube-240fps.
# SlowFlow/val target PSNR: 33.20, SSIM: 0.891, IE: 6.56
python3 -m torch.distributed.launch --nproc_per_node=16 train.py --model CycleHJSuperSloMo \
    --flow_scale 2.0 --batch_size 2 --crop_size 384 384 --print_freq 1 --dataset CycleVideoInterp \
    --step_size 1 --sample_rate 0 --num_interp 7 --val_num_interp 7 --skip_aug --save_freq 20 --start_epoch 0 \
    --train_file /path/to/SlowFlow/train --val_file /path/to/SlowFlow/val --name finetune_slowflow \
    --save /path/to/output --resume ./pretrained_models/baseline_superslomo_adobe+youtube.pth

# Supervised baseline training on Adobe240-fps and evaluation on SlowFlow
# SlowFlow/val target PSNR: 32.84, SSIM: 0.887, IE: 6.67
python3 -m torch.distributed.launch --nproc_per_node=16 train.py --model HJSuperSloMo \
    --flow_scale 2.0 --batch_size 2 --crop_size 352 352 --print_freq 1 --dataset VideoInterp \
    --num_interp 7 --val_num_interp 7 --skip_aug --save_freq 20 --start_epoch 0 --stride 32 \
    --train_file /path/to/Adobe-240fps/train --val_file /path/to/SlowFlow/val --name supervised_adobe \
    --save /path/to/output

@InProceedings{Reda_2019_ICCV,
author = {Fitsum A Reda and Deqing Sun and Aysegul Dundar and Mohammad Shoeybi and Guilin Liu and Kevin J Shih and Andrew Tao and Jan Kautz and Bryan Catanzaro},
title = {Unsupervised Video Interpolation Using Cycle Consistency},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019},
url={https://nv-adlr.github.io/publication/2019-UnsupervisedVideoInterpolation}
}