Open tomspilman opened 5 years ago
Thank you for using pixcaler!
You mean the data set used for training the model on the demo website, right?
Sorry, some images in the data set are not allowed to redistribute these copies. So you need to download these images manually.
fsmvx_characv.zip
(character images), please visit https://razor-edge.work/material/fsmchcv/ and click the link "素材の一括ダウンロード" (it means "download all materials")
fsmvx_characv.zip
python -m pixcaler.tool.transparent-chartip /path/to/dataset_redistributable-part/chartip /path/to/fsmvx_characv/*/*.png
I have some additional notices if you are going to train your custom model,
pip install cupy
python -m pixcaler.train -g [your GPU ID] --composite -i /path/to/dataset/directory -b 4
lam2
at https://github.com/mitaki28/pixcaler/blob/dd569451624952c78b6b3044a3e92ae84094f711/pixcaler/updater.py#L45
lam2
make created models to generate clearer, but more unstable images. For my data set, 1/4 seems to be good for generating clearer image.Thanks @mitaki28 ... we're looking into this now.
Have you considered using actual 8-bit graphics from the 90s that came in both high and low-resolution versions? There are a few examples of this, like the following (I'm cross-posting this from comments in the waifu2x project):
More stuff can be probably obtained from other PC titles that supported both high and low resolution back then. Of course these would not be redistributable but perhaps they would allow to achieve greater precision in results?
Hi, http://mitaki28.info/pixcaler/dataset/dataset_redistributable-part.tar.gz is broken (404), can you re-upload ?, thank you
Oh, sorry. I wrongly remove dataset. Now I re-uploaded them.
Hi, @mitaki28
I encountered a pip error when installing chainercv using python3.5 venv, the error is No local packages or working download links found for numpy
, but I run pip install numpy
it can be installed.
The fsmvx_characv.zip download link is broken, do you have a backup of this pack? Due to this problem, I changed some code see below
Remove: self.chartips = list((self.data_dir/'chartip').glob("*.png"))
Remove: if r < 0.5:
Remove: with Image.open(str(self.chartips[np.random.randint(len(self.chartips))])) as img:
Remove: front = img_to_chw_array(img)
It can be run after modification, but aborted (core dumped) once without any errors.
(It reminded me Installation of cv2 is recommended for faster computation.
, then I run pip install opencv-contrib-python
to install cv2)
I feel confused, then remove your requirements.txt and venv, restore to default shell, and install all python package manually. This is my new requirements.txt
matplotlib==3.4.2
chainer==7.8.0
chainerui==0.11.0
chainercv==0.13.1
But I still failed. I think pixcaler can run, but I am missing some sample files for training the model.
This is my run command: CHAINER_DEBUG=1 python -m pixcaler.train --composite -i image/dataset -b 4
Error Info:
GPU: -1
# Minibatch-size: 4
# epoch: 50
# upscale learning with automatically generated images
10 tiles loaded
3 objs loaded
29 fonts loaded
10 tiles loaded
3 objs loaded
29 fonts loaded
# start training
Exception in main training loop: NaN is detected on backward computation of Convolution2DFunction
Traceback (most recent call last):
File "/home/sources/.local/lib/python3.9/site-packages/chainer/training/trainer.py", line 343, in run
update()
File "/home/sources/.local/lib/python3.9/site-packages/chainer/training/updaters/standard_updater.py", line 240, in update
self.update_core()
File "/home/sources/src/pixcaler/pixcaler/updater.py", line 72, in update_core
self.loss_gen(gen, x_out, t_out, y_fake).backward()
File "/home/sources/.local/lib/python3.9/site-packages/chainer/variable.py", line 1581, in backward
_backprop._backprop_to_all(
File "/home/sources/.local/lib/python3.9/site-packages/chainer/_backprop.py", line 225, in _backprop_to_all
_backprop_utils.backprop_step(
File "/home/sources/.local/lib/python3.9/site-packages/chainer/_backprop_utils.py", line 206, in backprop_step
raise RuntimeError(
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/sources/src/pixcaler/pixcaler/train.py", line 178, in <module>
main()
File "/home/sources/src/pixcaler/pixcaler/train.py", line 175, in main
trainer.run()
File "/home/sources/.local/lib/python3.9/site-packages/chainer/training/trainer.py", line 376, in run
six.reraise(*exc_info)
File "/usr/lib/python3.9/site-packages/six.py", line 719, in reraise
raise value
File "/home/sources/.local/lib/python3.9/site-packages/chainer/training/trainer.py", line 343, in run
update()
File "/home/sources/.local/lib/python3.9/site-packages/chainer/training/updaters/standard_updater.py", line 240, in update
self.update_core()
File "/home/sources/src/pixcaler/pixcaler/updater.py", line 72, in update_core
self.loss_gen(gen, x_out, t_out, y_fake).backward()
File "/home/sources/.local/lib/python3.9/site-packages/chainer/variable.py", line 1581, in backward
_backprop._backprop_to_all(
File "/home/sources/.local/lib/python3.9/site-packages/chainer/_backprop.py", line 225, in _backprop_to_all
_backprop_utils.backprop_step(
File "/home/sources/.local/lib/python3.9/site-packages/chainer/_backprop_utils.py", line 206, in backprop_step
raise RuntimeError(
RuntimeError: NaN is detected on backward computation of Convolution2DFunction
Dataset:
tree image/dataset
image/dataset
├── font
│ ├── mplus-1c-black.ttf
│ ├── mplus-1c-bold.ttf
│ ├── mplus-1c-heavy.ttf
│ ├── mplus-1c-medium.ttf
│ ├── mplus-1c-regular.ttf
│ ├── mplus-1m-bold.ttf
│ ├── mplus-1m-medium.ttf
│ ├── mplus-1mn-bold.ttf
│ ├── mplus-1mn-medium.ttf
│ ├── mplus-1mn-regular.ttf
│ ├── mplus-1m-regular.ttf
│ ├── mplus-1p-black.ttf
│ ├── mplus-1p-bold.ttf
│ ├── mplus-1p-heavy.ttf
│ ├── mplus-1p-medium.ttf
│ ├── mplus-1p-regular.ttf
│ ├── mplus-2c-black.ttf
│ ├── mplus-2c-bold.ttf
│ ├── mplus-2c-heavy.ttf
│ ├── mplus-2c-medium.ttf
│ ├── mplus-2c-regular.ttf
│ ├── mplus-2m-bold.ttf
│ ├── mplus-2m-medium.ttf
│ ├── mplus-2m-regular.ttf
│ ├── mplus-2p-black.ttf
│ ├── mplus-2p-bold.ttf
│ ├── mplus-2p-heavy.ttf
│ ├── mplus-2p-medium.ttf
│ └── mplus-2p-regular.ttf
├── obj
│ ├── arabian2.png
│ ├── arabia.png
│ └── kasi5.png
├── test
└── tile
├── akumu_kabe.png
├── akumu_yuka.png
├── arabiaInside_A2.png
├── ArabianOutside_A2.png
├── arabianOutside_A3.png
├── ArabianOutside_A4.png
├── kabe.png
├── kasi12.png
├── kasi.png
└── yuka.png
Environment: Arch Linux Python 3.9.6 Python35 3.5.10 No NVIDIA GPU
Hi @mitaki28 .
Would it be possible to share your data set used on the demo website?
We're looking into an alternative to waifu2x for certain types of images and pixcaler generates better results.