Skip to content

Latest commit

 

History

History
executable file
·
8 lines (5 loc) · 1.54 KB

tips.md

File metadata and controls

executable file
·
8 lines (5 loc) · 1.54 KB
Raw

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. The default values of these options are somtimes 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. --batch_size 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.

  • 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.