A simple template project using PyTorch which can be modified to fit many deep learning projects.
This repo contains train and test code. E.g.
python main.py
The default arguments list is shown below:
usage: train.py [-h] [-b BATCH_SIZE] [-e EPOCHS] [--resume RESUME]
[--verbosity VERBOSITY] [--save-dir SAVE_DIR]
[--save-freq SAVE_FREQ] [--data-dir DATA_DIR]
[--validation-split VALIDATION_SPLIT] [--no-cuda]
PyTorch Template
├── base/ - abstract base classes
│ ├── base_data_loader.py - abstract base class for data loaders.
│ ├── etc.
│ └── base_trainer.py - abstract base class for trainers
│
├── data_loader/ - anything about data loading goes here
│ └── data_loader.py
│
├── data/ - dir containing saved models as well as log files
│
├── logger/ - for training process logging (generating Tensorboard readable files)
│ └── logger.py
│
├── model/ - models, losses, and metrics
│ ├── modules/ - submodules of your model
│ ├── loss.py
│ └── metric.py
│ └── model.py
|
├── test/ - test modules to make sure they work properly
│ └── test.py
│
├── trainer/ - trainers for your project
│ └── trainer.py
|
├── tester/ - tester for your project
│ └── tester.py
│
└── utils
├── utils.py
└── ...
If you need to change the loss function or metrics, first import those function in main.py, then modify this part:
loss = my_loss
metrics = [my_metric]The metrics and loss are going to be automatically added to the log file.
If you have multiple metrics in your project, just add it to the metrics list:
loss = my_loss
metrics = [my_metric, my_metric2]Now the logging shows two metrics.