Metadata-Version: 2.1
Name: easytorch
Version: 0.2
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 setup
        1. Install pytorch and torchvision from [Pytorch official website](https://pytorch.org/)
        2.  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](https://github.com/sraashis/easytorchexample)
        Sample usecase 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.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}
        }
            
        
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
