Awesome
MegCup 2022 Team Feedforward
This repository is the official MegEngine implementation of the 3rd place solution (Team Feedforward) in 2022 MegCup RAW image denoising.
Method
We propose a FeedBack-based Restormer (FBRestormer) for lightweight denoising, the number of parameters in this model is smaller than 100K!
<img src='assets/arch.png' width=45% height=45%></img> <img src='assets/SGFM.png' width=45% height=45%></img>
Notice That, the restormer blocks in our architecture are modified by replacing the depth-wise 3x3 convlution in the GDFN with depth-wise 5x5 convlution which is also dilated with dilation equals 2.
The simple gated fusion module is used for feedback connections.
Environment
Conda
$ conda create -f ./env.yaml
$ conda activate megcup
Docker
<font color=red>TBD</font>
Usage
$ python test.py --data-path DATA_PATH # The test input data path.
--checkpoint CHEKPOINT # The checkpoint need to be loaded.
[--batch-size BATCH_SIZE] # OPTIONAL: Batch size for the dataloader, DEFAULT: 1
[--num-workers NUM_WORKERS] # OPTIONAL: Number of workers for the dataloader, DEFAULT: 0
[--output PATH] # OPTIONAL: The path to output the final binary file, DEFAULT: '.'
Example:
$ cp PATH/DATA .
$ python test.py --data-path ./DATA --checkpoint ./feedback_restormer.mge