Fastorch

A pytorch project for fast runing deep learning and iterating version.

Files

run.py # main function for runing
trainer.py # trainer, train and test model
config.yaml # global config file, loaded in run.py
dataset.py # define personal dataset, used in run.py with dataloader
loss.py # define personal loss, used in train.py
net.py # define personal network, used in run.py
utils.py # define other functions

Usage

# define your dataset, loss, net, trainer and config file
ssh run.sh

Change Log

2022-12-28

• solve problem with bool command line arguments always being recognized as True

2022-11-09

• add load_in_memory for dataset for speed training
• remove some above functions (Doesn't work well in the actual version iteration):
  • save version project file for each version
    • type of saved files is defined by save_version_file_patterns in config file
    • if load_epoch in config file is set false, save files in runs/latest_project and runs/version/project
    • if load_epoch in config file is set epoch name in saved model file
      • save latest files in runs/latest_project.
      • load files from runs/version/project for testing
      • restore latest files from runs/latest_project.

2022-07-05

• save version project file for each version
  • type of saved files is defined by save_version_file_patterns in config file
  • if load_epoch in config file is set false, save files in runs/latest_project and runs/version/project
  • if load_epoch in config file is set epoch name in saved model file
    • save latest files in runs/latest_project
    • load files from runs/version/project for testing
    • restore latest files from runs/latest_project
• save tensorboard file, running log, config file, model state dict for each version
  • use tensorboard --logdir=runs for visualization
  • load model parameters from saved model files
  • read runing log for training output
• easily change train parameters
  • random_seed: set random seed for each training
  • epochs: set total training epochs
  • batch_size: set batch size
  • num_workers: set number of processes
  • lr: set learning rate
  • device_ids: gpu device ids, use cpu if none; if its number greater than 1, use data parallel
  • valid_every_epochs: epochs of validing model
  • early_stop_epochs: epochs of early stop, set negative number for not using
  • start_save_model_epochs: greater than it will save model
  • save_model_interval_epochs: interval epochs for saving model
• add parameters in config file to args, so it can use args.xxx to use these parameters easily