pfnet-research/chainer-pix2pix pfnet-research/chainer-pix2pix

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Repository Details

chainer implementation of pix2pix

chainer-pix2pix

chainer implementation of pix2pix https://phillipi.github.io/pix2pix/

The Japanese readme can be found here.

Example result on CMP facade dataset

From the left side: input, output, ground_truth

usage

  1. pip install -r requirements.txt
  2. Download the facade dataset (base set) http://cmp.felk.cvut.cz/~tylecr1/facade/
  3. python train_facade.py -g [GPU ID, e.g. 0] -i [dataset root directory] --out [output directory] --snapshot_interval 10000
  4. Wait a few hours...
  • --out stores snapshots of the model and example images at an interval defined by --snapshot_interval
  • If the model size is large, you can reduce --snapshot_interval to save resources.

Using other datasets

  • Gather image pairs (e.g. label + photo). Several hundred pairs are required for good results.
  • Create a copy of facade_dataset.py for your dataset. The function get_example should be written so that it returns the i-th image pair a tuple of numpy arrays i.e. (input, output).
  • It maybe necessary to update the loss function in updater.py.
  • Likewise, make a copy of facade_visualizer.py and modify to visualize the dataset.
  • In train_facade.py change in_ch and out_ch to the correct input and output channels for your data.

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