75 lines
4.5 KiB
Markdown
75 lines
4.5 KiB
Markdown
# Training super-resolution networks with ESRGAN
|
|
|
|
[SRGAN](https://arxiv.org/abs/1609.04802) is a landmark SR technique. It is quickly approaching "seminal" status because of how many papers
|
|
use some or all of the techniques originally introduced in this paper. [ESRGAN](https://arxiv.org/abs/1809.00219) is a followup
|
|
paper by the same authors which strictly improves the results of SRGAN.
|
|
|
|
After considerable trial and error, I recommend an additional set of modifications to ESRGAN to
|
|
improve training performance and reduce artifacts:
|
|
|
|
* Gradient penalty loss on the discriminator keeps the discriminator gradients to the generator in check.
|
|
* Adding noise of 1/255 to the discriminator prevents it from using the fixed input range of HR images for discrimination. (e.g. - natural HR images can only have values in increments of 1/255, while the generator has continuous outputs. The discriminator can cheat by using this fact.)
|
|
* Adding GroupNorm to the discriminator layers. This further stabilizes gradients without the downsides of BatchNorm.
|
|
* Adding a translational loss to the generator term. This loss works by computing using the generator to compute two HQ images
|
|
during each training pass from random sub-patches of the original image. A L1 loss is then computed across the shared
|
|
region of the two outputs with a very high gain. I found this to be tremendously helpful in reducing GAN artifacts
|
|
as it forces the generator to be self-consistent.
|
|
* Use a vanilla GAN. The ESRGAN paper promotes the use of RAGAN but I found its effect on result qualit to be minimal
|
|
with the above modifications. In some cases, it can actually be harmful because it drives strange training
|
|
dynamics on the discriminator. For example, I've observed the output of the discriminator to sometimes
|
|
"explode" when using RAGAN because it does not force a fixed output value. It is also more computationally expensive
|
|
to compute.
|
|
|
|
The examples below have all of these modifications added. I've also provided a reference file that
|
|
should be closer to the original ESRGAN implementation, `train_div2k_esrgan_reference.yml`.
|
|
|
|
## Training ESRGAN
|
|
|
|
DLAS can train and use ESRGAN models end-to-end. These docs will show you how.
|
|
|
|
### Dataset Preparation
|
|
|
|
Start by assembling your dataset. The ESRGAN paper uses the [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and
|
|
[Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) datasets. These include a small set of high-resolution
|
|
images. ESRGAN is trained on small sub-patches of those images. Generate these patches using the instructions found
|
|
in 'Generating a chunked dataset' [here](https://github.com/neonbjb/DL-Art-School/blob/gan_lab/codes/data/README.md).
|
|
|
|
Consider creating a validation set at the same time. These can just be a few medium-resolution, high-quality
|
|
images. DLAS will downsample them for you and send them through your network for validation.
|
|
|
|
### Training the model
|
|
|
|
Use the train_div2k_esrgan.yml configuration file in this directory as a template to train your
|
|
ESRGAN. Search the file for `<--` to find options that will need to be adjusted for your installation.
|
|
|
|
Train with:
|
|
`python train.py -opt train_div2k_esrgan.yml`
|
|
|
|
Note that this configuration trains an RRDB network with an L1 pixel loss only for the first 100k
|
|
steps. I recommend you save the model at step 100k (this is done by default, just copy the file
|
|
out of the experiments/train_div2k_esrgan/models directory once it hits step 100k) so that you
|
|
do not need to repeat this training in future experiments.
|
|
|
|
## Using an ESRGAN model
|
|
|
|
### Image SR
|
|
|
|
You can apply a pre-trained ESRGAN model against a set of images using the code in `test.py`.
|
|
Documentation for this script is forthcoming but basically you feed it your training configuration
|
|
file with the `pretrain_model_generator` option set properly and your folder with test images
|
|
pointed to in the datasets section in lieu of the validation set.
|
|
|
|
### Video SR
|
|
|
|
I've put together a script that strips a video into its constituent frames, applies an ESRGAN
|
|
model to each frame one a time, then recombines the frames back into videos (without sound).
|
|
You will need to use ffmpeg to stitch the videos back together and add sound, but this is
|
|
trivial.
|
|
|
|
This script is called `process_video.py` and it takes a special configuration file. A sample
|
|
config is provided in `rrdb_process_video.yml` in this directory. Further documentation on this
|
|
procedure is forthcoming.
|
|
|
|
Fun fact: the foundations of DLAS lie in the (now defunct) MMSR github repo, which was
|
|
primarily an implementation of ESRGAN.
|