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model Create pixelization_model.py Nov 30, 2018
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teaser
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README.md
test.py
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train.py Create train.py Nov 30, 2018
train.sh

README.md

Deep-Unsupervised-Pixelization

Paper

Deep Unsupervised Pixelization and Supplementary Material.
Chu Han^, Qiang Wen^, Shengfeng He*, Qianshu Zhu, Yinjie Tan, Guoqiang Han, and Tien-Tsin Wong (^joint first authors).
ACM Transactions on Graphics (SIGGRAPH Asia 2018 issue), 2018.

Our teaser

Requirement

  • Python 3.5
  • PIL
  • Numpy
  • Pytorch 0.4.0
  • Ubuntu 16.04 LTS

Dataset

Training Dataset

We collect 900 clip arts and 900 pixel arts for trianing our method. The folders named trainA and trainB contain the clip arts and pixel arts respectively here.

Testing Dataset

Create the folders testA and testB in the directory ./samples/. Note that testA and testB contain the clip arts to be pixelized and pixel arts to be depixelized respectively.

Training

  • To train a model:
python3 ./train.py --dataroot ./samples --resize_or_crop crop --gpu_ids 0

or you can directly:

$ bash ./train.sh

You can check the losses of models in the file ./checkpoints_pixelization/loss_log.txt.
More training flags in the files ./options/base_options.py and ./options/train_options.py.

Testing

  • After training, all models have been saved in the directory ./checkpoints_pixelization/.
  • To test a model:
python3 ./test.py --dataroot ./samples --no_dropout --resize_or_crop crop --gpu_ids 0 --how_many 1 --which_epoch 200

or you can directly:

$ bash ./test.sh

More testing flags in the file ./options/base_options.py.
All testing results will be shown in the directory ./results_pixelization/.

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