easytorch 1.6.8

Easy Neural Network Experiments with pytorch

A quick and easy way to start running pytorch experiments within few minutes.

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Installation

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

'How to use?' you ask!

1. Define your trainer

from easytorch import ETTrainer


class MyTrainer(ETTrainer):
  def __init__(self, args):
    super().__init__(args)

  def _init_nn_model(self):
    self.nn['model'] = NeuralNetModel(self.args['num_channel'], self.args['num_class'])

  def iteration(self, batch):
    inputs = batch['input'].to(self.device['gpu']).float()
    labels = batch['label'].to(self.device['gpu']).long()

    out = self.nn['model'](inputs)
    loss = F.cross_entropy(out, labels)
    out = F.softmax(out, 1)

    _, pred = torch.max(out, 1)
    sc = self.new_metrics()
    sc.add(pred, labels)

    avg = self.new_averages()
    avg.add(loss.item(), len(inputs))

    return {'loss': loss, 'averages': avg, 'output': out, 'metrics': sc, 'predictions': pred}

2. Use custom or pytorch based Datasets class.

Define specification for your datasets:

import os
sep = os.sep
MYDATA = {
    'name': 'mydata',
    'data_dir': 'OTHERDATA' + sep + 'images',
    'label_dir': 'MYDATA' + sep + 'manual',
    'label_getter': lambda file_name: file_name.split('_')[0] + 'label.csv'
}

MyOTHERDATA = {
    'name': 'otherdata',
    'data_dir': 'OTHERDATA' + sep + 'images',
    'label_dir': 'OTHERDATA' + sep + 'manual',
    'label_getter': lambda file_name: file_name.split('_')[0] + 'label.csv'
}

Define how to load each data item

class MyDataset(ETDataset):
    def __init__(self, **kw):
        super().__init__(**kw)

    def __getitem__(self, index):
        dataset_name, file = self.indices[index]
        dataspec = self.dataspecs[dataset_name]
        
        """
        All the info. (data_dir, label_dir, label_getter...) defined above will be in dataspec.
        """
        image = #Todo # Load file/Image. 
        label = #Todo # Load corresponding label.
        # Extra preprocessing, if needed.
        # Apply transforms.
        
        return {'indices': self.indices[index],
                'input': image,
                'label': label}
    @property
    def transforms(self):
        return torchvision.transforms.Compose(["""List of transforms"""])

3. Entry point

from easytorch.etargs import ap
from easytorch import EasyTorch
dataspecs = [MYDATA, MYOTHERDATA]
runner = EasyTorch(dataspecs, ap)

if __name__ == "__main__":
    # Run for each datasets.
    runner.run(MyDataset, MyTrainer)
    ## Automatically combine the data of all dataspecs and run.
    runner.run_pooled(MyDataset, MyTrainer)

4. Run

Training+Validation+Test

* python main.py -ph train -e 51 -b 16

Only Test

* python main.py -ph test -e 51 -b 16

---

Complete Examples

• Vessel segmentation with U-NET example.
• Healthy/Pneumonia/Covid-19 chest x-ray (Multi-label/Binary)classification example.
• DCGAN Example.

Feature Higlights

• For advanced training with multiple networks, and complex training steps click here:
• Implement custom metrics as here.
• To track multiple averages(e.g. multiple losses) use easytorch.metrics.ETAverages(num_averages=...)
• Minimal configuration to setup a new experiment.
• Use your choice of Neural Network architecture.
• Automatic k-fold cross validation/Auto dataset split.
• Automatic logging/plotting, and model checkpointing. ..more features

Default arguments[default-value]. Easily add custom arguments.

• -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]
  • List of gpus to be used. Eg. [0], [1], [0, 1]
• -pin/--pin-memory [True]
• -nw/--num_workers [4]
  • Number of workers for data loading so that cpu can keep-up with GPU speed when loading mini-batches.
• -ph/--phase [Required]
  • Which phase to run. Possible values are 'train', and 'test'.
• -data/--dataset_dir [dataset]
  • base path of the dataset where data_dir, labels, masks, and splits are.
• -lim/--load-limit[inf]
  • Specifies limit on dataset to load for debug purpose for pipeline debugging.
• ...see more

All the best! for whatever you are working on. Cheers!

Please star or cite if you find it useful.

@misc{easytorch,
  author = {Khanal, Aashis},
  title = {Easy Torch}
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://github.com/sraashis/easytorch}
}