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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 10 |
1 files changed, 8 insertions, 2 deletions
@@ -44,6 +44,12 @@ In CVPR 2017. ## Getting Started ### Installation - Install PyTorch and dependencies from http://pytorch.org/ +- Install Torch vision from the source. +```bash +git clone https://github.com/pytorch/vision +cd vision +python setup.py install +``` - Install python libraries [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate). ```bash pip install visdom @@ -81,13 +87,13 @@ bash ./datasets/download_pix2pix_dataset.sh facades - Train a model: ```bash #!./scripts/train_pix2pix.sh -python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --lambda_A 100 --align_data --use_dropout --no_lsgan +python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --lambda_A 100 --dataset_mode aligned --use_dropout --no_lsgan ``` - To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097. To see more intermediate results, check out `./checkpoints/facades_pix2pix/web/index.html` - Test the model (`bash ./scripts/test_pix2pix.sh`): ```bash #!./scripts/test_pix2pix.sh -python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --align_data +python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --dataset_mode aligned ``` The test results will be saved to a html file here: `./results/facades_pix2pix/latest_val/index.html`. |