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https://github.com/yinboc/liif
Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)
https://github.com/yinboc/liif
implicit-neural-representation machine-learning pytorch super-resolution
Last synced: 12 days ago
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Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)
- Host: GitHub
- URL: https://github.com/yinboc/liif
- Owner: yinboc
- License: bsd-3-clause
- Created: 2020-12-16T17:04:04.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-08-21T17:43:06.000Z (about 3 years ago)
- Last Synced: 2024-10-01T14:04:11.825Z (about 1 month ago)
- Topics: implicit-neural-representation, machine-learning, pytorch, super-resolution
- Language: Python
- Homepage: https://yinboc.github.io/liif/
- Size: 63.5 KB
- Stars: 1,258
- Watchers: 22
- Forks: 145
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-implicit-representations - Learning Continuous Image Representation with Local Implicit Image Function
README
# LIIF
This repository contains the official implementation for LIIF introduced in the following paper:
[**Learning Continuous Image Representation with Local Implicit Image Function**](https://arxiv.org/abs/2012.09161)
[Yinbo Chen](https://yinboc.github.io/), [Sifei Liu](https://www.sifeiliu.net/), [Xiaolong Wang](https://xiaolonw.github.io/)
CVPR 2021 (Oral)
The project page with video is at https://yinboc.github.io/liif/.
### Citation
If you find our work useful in your research, please cite:
```
@inproceedings{chen2021learning,
title={Learning continuous image representation with local implicit image function},
author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8628--8638},
year={2021}
}
```
### Environment
- Python 3
- Pytorch 1.6.0
- TensorboardX
- yaml, numpy, tqdm, imageio
## Quick Start
1. Download a DIV2K pre-trained model.
Model|File size|Download
:-:|:-:|:-:
EDSR-baseline-LIIF|18M|[Dropbox](https://www.dropbox.com/s/6f402wcn4v83w2v/edsr-baseline-liif.pth?dl=0) | [Google Drive](https://drive.google.com/file/d/1wBHSrgPLOHL_QVhPAIAcDC30KSJLf67x/view?usp=sharing)
RDN-LIIF|256M|[Dropbox](https://www.dropbox.com/s/mzha6ll9kb9bwy0/rdn-liif.pth?dl=0) | [Google Drive](https://drive.google.com/file/d/1xaAx6lBVVw_PJ3YVp02h3k4HuOAXcUkt/view?usp=sharing)
2. Convert your image to LIIF and present it in a given resolution (with GPU 0, `[MODEL_PATH]` denotes the `.pth` file)
```
python demo.py --input xxx.png --model [MODEL_PATH] --resolution [HEIGHT],[WIDTH] --output output.png --gpu 0
```
## Reproducing Experiments
### Data
`mkdir load` for putting the dataset folders.
- **DIV2K**: `mkdir` and `cd` into `load/div2k`. Download HR images and bicubic validation LR images from [DIV2K website](https://data.vision.ee.ethz.ch/cvl/DIV2K/) (i.e. [Train_HR](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip), [Valid_HR](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip), [Valid_LR_X2](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip), [Valid_LR_X3](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X3.zip), [Valid_LR_X4](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X4.zip)). `unzip` these files to get the image folders.
- **benchmark datasets**: `cd` into `load/`. Download and `tar -xf` the [benchmark datasets](https://cv.snu.ac.kr/research/EDSR/benchmark.tar) (provided by [this repo](https://github.com/thstkdgus35/EDSR-PyTorch)), get a `load/benchmark` folder with sub-folders `Set5/, Set14/, B100/, Urban100/`.
- **celebAHQ**: `mkdir load/celebAHQ` and `cp scripts/resize.py load/celebAHQ/`, then `cd load/celebAHQ/`. Download and `unzip` data1024x1024.zip from the [Google Drive link](https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P?usp=sharing) (provided by [this repo](github.com/suvojit-0x55aa/celebA-HQ-dataset-download)). Run `python resize.py` and get image folders `256/, 128/, 64/, 32/`. Download the [split.json](https://www.dropbox.com/s/2qeijojdjzvp3b9/split.json?dl=0).
### Running the code
**0. Preliminaries**
- For `train_liif.py` or `test.py`, use `--gpu [GPU]` to specify the GPUs (e.g. `--gpu 0` or `--gpu 0,1`).
- For `train_liif.py`, by default, the save folder is at `save/_[CONFIG_NAME]`. We can use `--name` to specify a name if needed.
- For dataset args in configs, `cache: in_memory` denotes pre-loading into memory (may require large memory, e.g. ~40GB for DIV2K), `cache: bin` denotes creating binary files (in a sibling folder) for the first time, `cache: none` denotes direct loading. We can modify it according to the hardware resources before running the training scripts.
**1. DIV2K experiments**
**Train**: `python train_liif.py --config configs/train-div2k/train_edsr-baseline-liif.yaml` (with EDSR-baseline backbone, for RDN replace `edsr-baseline` with `rdn`). We use 1 GPU for training EDSR-baseline-LIIF and 4 GPUs for RDN-LIIF.
**Test**: `bash scripts/test-div2k.sh [MODEL_PATH] [GPU]` for div2k validation set, `bash scripts/test-benchmark.sh [MODEL_PATH] [GPU]` for benchmark datasets. `[MODEL_PATH]` is the path to a `.pth` file, we use `epoch-last.pth` in corresponding save folder.
**2. celebAHQ experiments**
**Train**: `python train_liif.py --config configs/train-celebAHQ/[CONFIG_NAME].yaml`.
**Test**: `python test.py --config configs/test/test-celebAHQ-32-256.yaml --model [MODEL_PATH]` (or `test-celebAHQ-64-128.yaml` for another task). We use `epoch-best.pth` in corresponding save folder.