easytorch 1.2

Easy Neural Network Experiments with pytorch

EasyTorch is a quick and easy way to start running pytorch experiments

As a phd student, I could not lose time on boilerplate neural network setups, so I started this sort-of-general framework to run experiments quickly. It consist of rich utilities useful for image manipulation as my research is focused on biomedical images. I would be more than happy if it becomes useful to any one getting started with neural netowrks.

Installation

1. Install latest pytorch and torchvision from Pytorch official website
2. pip install easytorch

Link to a full working example

Higlights

• A convenient framework to easily setup neural network experiments.
• Minimal configuration to setup a new experimenton new dataset:
• Use your choice of Neural Network architecture.
• Create a python dictionary pointing to data ,ground truth, and mask directory(dataspecs.py).
• Automatic k-fold cross validation.
• Automatic logging/plotting, and model checkpointing.
• Works on all sort of neural network related task.
• GPU enabled metrics like precision, recall, f1, overlap, and confusion matrix with maximum GPU utilization.
• Ability to automatically combine multiple datasets without having to move the data from original location.

Sample use case as follows:

import argparse
from easytorch.utils.defaultargs import ap
import dataspecs as dspec

from easytorch import EasyTorch
from classification import MyTrainer, MyDataset

ap = argparse.ArgumentParser(parents=[ap], add_help=False)
dataspecs = [dspec.DRIVE, dspec.STARE]
runner = EasyTorch(ap, dataspecs)

if __name__ == "__main__":
    runner.run(MyDataset, MyTrainer)
    runner.run_pooled(MyDataset, MyTrainer)

Default arguments (Can be extended to add your custom arguments. Please check the example)

• -nch/--num_channel [3]
  • Number of input channels
• -ncl/--num_class [2]
  • Number of output classes
• -b/--batch_size [32]
• -e/--epochs [51]
• -lr/--learning_rate [0.001]
• -gpus/--gpus [0, 1]
  • List of gpus to be used. Eg. [0], [1], [0, 1]
• -pin/--pin-memory [True]
• -nw/--num_workers [2]
  • Number of workers for data loading so that cpu can keep-up with GPU speed when loading mini-batches.
• -p/--phase [Required]
  • Which phase to run. Possible values are 'train', and 'test'. Train runs all training., validation, and test phase. Whereas, test phase only runs test phase.
• -data/--dataset_dir [dataset]
  • base path of the dataset where data_dir, labels, masks, and splits are.
• -lim/--load-limit
  • Specifies limit on dataset to load for debug purpose. Because sometimes we want to load, maybe 5 or 10, images to test the pipeline before we run full training.
• -log/--log_dir [net_logs]
  • Path where the results: plots, model checkpoint, etc are saved.
• -pt/--pretrained_path [None]
  • Full path to a previously saved best model if one wishes to run test on any other model than the one in log_dir.
• -d/--debug [True]
  • enable/disable debug.
• -s/--seed [random]
  • Custom seed to initialize model.
• -f/--force [False]
  • Overrides existing plots and results if true.
• -r/--model_scale [1]
  • Parameter to scale model breath.
• -sp/--load_sparse [False]
  • Load all data from one image in single DataLoader so that it is easy to combine later to form a whole image.

Training+Validation+Test

* $python main.py -p train -nch 3 -e 3 -b 2 -sp True

Only Test

* $python main.py -p test -nch 3 -e 3 -b 2 -sp True

References

Please cite us if you use this framework(easytorch) as follows: @misc{easytorch, author = {Khanal, Aashis}, title = {Quick Neural Network Experimentation}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, url = {https://github.com/sraashis/easytorch} }