Official PyTorch Implementation of the CVPR 2022 Paper
Project | Paper | arXiv | RealMCVSR Dataset
Reference-based Video Super-Resolution (RefVSR)This repo contains training and evaluation code for the following paper:
Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
Junyong Lee, Myeonghee Lee, Sunghyun Cho, and Seungyong Lee
POSTECH
IEEE Computer Vision and Pattern Recognition (CVPR) 2022
Getting Started
Prerequisites
Tested environment
1. Environment setup
$ git clone https://github.com/codeslake/RefVSR.git
$ cd RefVSR
$ conda create -y name RefVSR python 3.8 && conda activate RefVSR
# Install pytorch
$ conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
# Install requirements
$ ./install/install_cudnn113.sh
It is recommended to install PyTorch >= 1.10.0 with CUDA11.3 for running small models using Pytorch AMP, because PyTorch < 1.10.0 is known to have a problem in running amp with
torch.nn.functional.grid_sample()
needed for inter-frame alignment.
For the other models, PyTorch 1.8.0 is verified. To install requirements with PyTorch 1.8.0, run
./install/install_cudnn102.sh
for CUDA10.2 or./install/install_cudnn111.sh
for CUDA11.1
2. Dataset
Download and unzip the proposed RealMCVSR dataset under [DATA_OFFSET]
:
[DATA_OFFSET]
└── RealMCVSR
├── train # a training set
│ ├── HR # videos in original resolution
│ │ ├── T # telephoto videos
│ │ │ ├── 0002 # a video clip
│ │ │ │ ├── 0000.png # a video frame
│ │ │ │ └── ...
│ │ │ └── ...
│ │ ├── UW # ultra-wide-angle videos
│ │ └── W # wide-angle videos
│ ├── LRx2 # 2x downsampled videos
│ └── LRx4 # 4x downsampled videos
├── test # a testing set
└── valid # a validation set
[DATA_OFFSET]
can be modified with--data_offset
option in the evaluation script.
3. Pre-trained models
Download pretrained weights (Google Drive | Dropbox) under ./ckpt/
:
RefVSR
├── ...
├── ./ckpt
│ ├── edvr.pytorch # weights of EDVR modules used for training Ours-IR
│ ├── SPyNet.pytorch # weights of SpyNet used for inter-frame alignment
│ ├── RefVSR_small_L1.pytorch # weights of Ours-small-L1
│ ├── RefVSR_small_MFID.pytorch # weights of Ours-small
│ ├── RefVSR_small_MFID_8K.pytorch # weights of Ours-small-8K
│ ├── RefVSR_L1.pytorch # weights of Ours-L1
│ ├── RefVSR_MFID.pytorch # weights of Ours
│ ├── RefVSR_MFID_8K.pytorch.pytorch # weights of Ours-8K
│ ├── RefVSR_IR_MFID.pytorch # weights of Ours-IR
│ └── RefVSR_IR_L1.pytorch # weights of Ours-IR-L1
└── ...
For the testing and training of your own model, it is recommended to go through wiki pages for
logging and details of testing and training scripts before running the scripts.
Testing models of CVPR 2022
Evaluation script
CUDA_VISIBLE_DEVICES=0 python -B run.py \
--mode _RefVSR_MFID_8K \ # name of the model to evaluate
--config config_RefVSR_MFID_8K \ # name of the configuration file in ./configs
--data RealMCVSR \ # name of the dataset
--ckpt_abs_name ckpt/RefVSR_MFID_8K.pytorch \ # absolute path for the checkpoint
--data_offset /data1/junyonglee \ # offset path for the dataset (e.g., [DATA_OFFSET]/RealMCVSR)
--output_offset ./result # offset path for the outputs
Real-world 4x video super-resolution (HD to 8K resolution)
# Evaluating the model 'Ours' (Fig. 8 in the main paper).
$ ./scripts_eval/eval_RefVSR_MFID_8K.sh
# Evaluating the model 'Ours-small'.
$ ./scripts_eval/eval_amp_RefVSR_small_MFID_8K.sh
For the model
Ours
, we use Nvidia Quadro 8000 (48GB) in practice.
For the model
Ours-small
,
- We use Nvidia GeForce RTX 3090 (24GB) in practice.
- It is the model
Ours-small
in Table 2 further trained with the adaptation stage.- The model requires PyTorch >= 1.10.0 with CUDA 11.3 for using PyTorch AMP.
Quantitative evaluation (models trained with the pre-training stage)
## Table 2 in the main paper
# Ours
$ ./scripts_eval/eval_RefVSR_MFID.sh
# Ours-l1
$ ./scripts_eval/eval_RefVSR_L1.sh
# Ours-small
$ ./scripts_eval/eval_amp_RefVSR_small_MFID.sh
# Ours-small-l1
$ ./scripts_eval/eval_amp_RefVSR_small_L1.sh
# Ours-IR
$ ./scripts_eval/eval_RefVSR_IR_MFID.sh
# Ours-IR-l1
$ ./scripts_eval/eval_RefVSR_IR_L1.sh
For all models, we use Nvidia GeForce RTX 3090 (24GB) in practice.
To obtain quantitative results measured with the varying FoV ranges as shown in Table 3 of the main paper, modify the script and specify
--eval_mode FOV
.
Training models with the proposed two-stage training strategy
The pre-training stage (Sec. 4.1)
# To train the model 'Ours':
$ ./scripts_train/train_RefVSR_MFID.sh
# To train the model 'Ours-small':
$ ./scripts_train/train_amp_RefVSR_small_MFID.sh
For both models, we use Nvidia GeForce RTX 3090 (24GB) in practice.
Be sure to modify the script file and set proper GPU devices, number of GPUs, and batch size by modifying
CUDA_VISIBLE_DEVICES
,--nproc_per_node
and-b
options, respectively.
- We use the total batch size of 4, the multiplication of numbers in options
--nproc_per_node
and-b
.
The adaptation stage (Sec. 4.2)
-
Set the path of the checkpoint of a model trained with the pre-training stage.
For the modelOurs-small
, for example,$ vim ./scripts_train/train_amp_RefVSR_small_MFID_8K.sh
#!/bin/bash py3clean ./ CUDA_VISIBLE_DEVICES=0,1 ... ... -ra [LOG_OFFSET]/RefVSR_CVPR2022/amp_RefVSR_small_MFID/checkpoint/train/epoch/ckpt/amp_RefVSR_small_MFID_00xxx.pytorch ...
Checkpoint path is
[LOG_OFFSET]/RefVSR_CVPR2022/[mode]/checkpoint/train/epoch/[mode]_00xxx.pytorch
.- PSNR is recorded in
[LOG_OFFSET]/RefVSR_CVPR2022/[mode]/checkpoint/train/epoch/checkpoint.txt
. [LOG_OFFSET]
can be modified withconfig.log_offset
in./configs/config.py
.[mode]
is the name of the model assigned with--mode
in the script used for the pre-training stage.
- PSNR is recorded in
-
Start the adaptation stage.
# Training the model 'Ours'. $ ./scripts_train/train_RefVSR_MFID_8K.sh # Training the model 'Ours-small'. $ ./scripts_train/train_amp_RefVSR_small_MFID_8K.sh
For the model
Ours
, we use Nvidia Quadro 8000 (48GB) in practice.For the model
Ours-small
, we use Nvidia GeForce RTX 3090 (24GB) in practice.Be sure to modify the script file to set proper GPU devices, number of GPUs, and batch size by modifying
CUDA_VISIBLE_DEVICES
,--nproc_per_node
and-b
options, respectively.- We use the total batch size of 2, the multiplication of numbers in options
--nproc_per_node
and-b
.
- We use the total batch size of 2, the multiplication of numbers in options
Training models with L1 loss
# To train the model 'Ours-l1':
$ ./scripts_train/train_RefVSR_L1.sh
# To train the model 'Ours-small-l1':
$ ./scripts_train/train_amp_RefVSR_small_L1.sh
# To train the model 'Ours-IR-l1':
$ ./scripts_train/train_amp_RefVSR_small_L1.sh
For all models, we use Nvidia GeForce RTX 3090 (24GB) in practice.
Be sure to modify the script file and set proper GPU devices, number of GPUs, and batch size by modifying
CUDA_VISIBLE_DEVICES
,--nproc_per_node
and-b
options, respectively.
- We use the total batch size of 8, the multiplication of numbers in options
--nproc_per_node
and-b
.
Wiki
Contact
Open an issue for any inquiries. You may also have contact with [email protected]
License
This software is being made available under the terms in the LICENSE file. Any exemptions to these terms require a license from the Pohang University of Science and Technology.
Acknowledgment
We thank the authors of BasicVSR and DCSR for sharing their code.
BibTeX
@InProceedings{Lee2022RefVSR,
author = {Junyong Lee and Myeonghee Lee and Sunghyun Cho and Seungyong Lee},
title = {Reference-based Video Super-Resolution Using Multi-Camera Video Triplets},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}