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StatAssist & GradBoost: Style Transfer

StatAssist & GradBoost: A Study on Optimal INT8 Quantization-aware Training from Scratch

modified version of the original code from: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.

Getting Started

CycleGAN train/test

  • Download a CycleGAN dataset (e.g. maps):
bash ./datasets/download_cyclegan_dataset.sh maps
  • To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
  • Train a model:
#!./scripts/train_cyclegan.sh
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan

To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html.

  • Test the model:
#!./scripts/test_cyclegan.sh
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
  • The test results will be saved to a html file here: ./results/maps_cyclegan/latest_test/index.html.

pix2pix train/test

  • Download a pix2pix dataset (e.g.facades):
bash ./datasets/download_pix2pix_dataset.sh facades
  • To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
  • Train a model:
#!./scripts/train_pix2pix.sh
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA

To see more intermediate results, check out ./checkpoints/facades_pix2pix/web/index.html.

  • Test the model (bash ./scripts/test_pix2pix.sh):
#!./scripts/test_pix2pix.sh
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
  • The test results will be saved to a html file here: ./results/facades_pix2pix/test_latest/index.html. You can find more scripts at scripts directory.
  • To train and test pix2pix-based colorization models, please add --model colorization and --dataset_mode colorization. See our training tips for more details.