Easy Neural Network Experiments with pytorch
Project description
EasyTorch setup
- Install pytorch and torchvision from Pytorch official website
- pip install easytorch
Higlights
- A convenient framework to easily setup neural network experiments.
- Minimal configuration to setup a newu experimenton new dataset:
- Only need to initialize neural network architecture, if needed.
- Create a python dictionary pointing to data ,ground truth, and mask directory(dataspecs.py).
- Automatic k-fold cross validation.
- Automatic logging and model checkpointing.
- Works an all sort of classification and regression task.
- GPU enabled metrics like precision, recall, f1, overlap, and confusion matrix with maximum GPU utilization.
- Ability to combine all dataset with correct dataspecs. Combining dataset and running experiments is hassle free.
Link to a full working example
Sample usecase as follows:
import argparse
import dataspecs as dspec
from easytorch.utils.defaultargs import ap
from easytorch.runs import run, pooled_run
from classification import MyTrainer, MyDataset
ap = argparse.ArgumentParser(parents=[ap], add_help=False)
dataspecs = [dspec.AV_WIDE, dspec.VEVIO]
if __name__ == "__main__":
run(ap, dataspecs, MyTrainer, MyDataset)
pooled_run(ap, dataspecs, MyTrainer, MyDataset)
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} }
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