Metadata-Version: 2.1
Name: easytorch
Version: 0.5
Summary: Easy Neural Network Experiments with pytorch
Home-page: https://github.com/sraashis/easytorch
Author: Aashis Khana1
Author-email: sraashis@gmail.com
License: MIT
Description: **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 pytorch and torchvision from [Pytorch official website](https://pytorch.org/)
        2. pip install easytorch
        
        ### [Link to a full working example](https://github.com/sraashis/easytorchexample)
        
        ### Higlights
        * A convenient framework to easily setup neural network experiments.
        * Minimal configuration to setup a newu 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 all the dataset with correct dataspecs and run on your favourite architecture.
        
        Sample use case as follows:
        ```python
        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.DRIVE, dspec.STARE]
        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}
        }
            
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
