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Training/test Tips

  • Flags: see options/train_options.py and options/base_options.py for the training flags; see options/test_options.py and options/base_options.py for the test flags. There are some model-specific flags as well, which are added in the model files, such as --lambda_A option in model/cycle_gan_model.py. The default values of these options are also adjusted in the model files.
  • CPU/GPU (default --gpu_ids 0): set--gpu_ids -1 to use CPU mode; set --gpu_ids 0,1,2 for multi-GPU mode. You need a large batch size (e.g. --batchSize 32) to benefit from multiple GPUs.
  • Visualization: during training, the current results can be viewed using two methods. First, if you set --display_id > 0, the results and loss plot will appear on a local graphics web server launched by visdom. To do this, you should have visdom installed and a server running by the command python -m visdom.server. The default server URL is http://localhost:8097. display_id corresponds to the window ID that is displayed on the visdom server. The visdom display functionality is turned on by default. To avoid the extra overhead of communicating with visdom set --display_id -1. Second, the intermediate results are saved to [opt.checkpoints_dir]/[opt.name]/web/ as an HTML file. To avoid this, set --no_html.
  • Preprocessing: images can be resized and cropped in different ways using --resize_or_crop option. The default option 'resize_and_crop' resizes the image to be of size (opt.loadSize, opt.loadSize) and does a random crop of size (opt.fineSize, opt.fineSize). 'crop' skips the resizing step and only performs random cropping. 'scale_width' resizes the image to have width opt.fineSize while keeping the aspect ratio. 'scale_width_and_crop' first resizes the image to have width opt.loadSize and then does random cropping of size (opt.fineSize, opt.fineSize). 'none' tries to skip all these preprocessing steps. However, if the image size is not a multiple of some number depending on the number of downsamplings of the generator, you will get an error because the size of the output image may be different from the size of the input image. Therefore, 'none' option still tries to adjust the image size to be a multiple of 4. You might need a bigger adjustment if you change the generator architecture. Please see data/base_datset.py do see how all these were implemented.
  • Fine-tuning/Resume training: to fine-tune a pre-trained model, or resume the previous training, use the --continue_train flag. The program will then load the model based on which_epoch. By default, the program will initialize the epoch count as 1. Set --epoch_count <int> to specify a different starting epoch count.