easytorch 2.4.83

Easy Neural Network Experiments with pytorch

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

---

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}
  
   def init_experiment_cache(self):
      r"""What scores you want to plot."""
      r"""By default it plots Loss,Accuracy,F1,Precision,Recall."""
      self.cache['log_header'] = 'Loss,Accuracy,F1,Precision,Recall'

      r"""This is for best model selection: """
      r"""It tells which metrics to monitor and either to maximize(F1 score), minimize(MSE)"""
      self.cache.update(monitor_metric='f1', metric_direction='maximize')

2. Use custom or pytorch based Datasets class.

Define specification for your datasets:

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

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

Define how to load each data item by using EasyTorch's base ETDataset class to get extra benefits like limiting, pooling data...

from easytorch import ETDataset
import torchvision
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"""])

(If one proceeds with the above (by overriding the ETDataset), can skip directly to 3.)

Or, one can use any other custom datasets as follows:

from easytorch import EasyTorch
class MyExperiment(EasyTorch):
 def _load_dataset(self, split_key, split_file, dspec:dict, dataset_cls=None):
    return ...

Here, the framework will:

• call _load_dataset(...) with every data split key (train, test, validation).
• So, we just need to write logic to load data for a given key, and return the dataset object.
• Now, just use class MyExperiment class instead of EasyTorch in the entrypoint.

For more advanced cases, one can override the following and directly specify each datasets(train/validation/test):

• The framework will internally call _load_dataset(...) from each of the following methods with corresponding split_key
• So only implement the following if you absolutely have to. Otherwise, implementing _load_dataset() will be enough in most of the cases.

from easytorch import EasyTorch
class MyExperiment(EasyTorch):
  
    def _get_train_dataset(self, split_file, dspec: dict, dataset_cls=None):
        return ...
    
    def _get_validation_dataset(self, split_file, dspec: dict, dataset_cls=None):
        return ...
    
    def _get_test_dataset(self, split_file, dspec: dict, dataset_cls=None):
      return ...
        

3. Entry point

from easytorch import EasyTorch
runner = EasyTorch([MYDATA, MyOTHERDATA],
                   phase="train", batch_size=4, epochs=21,
                   num_channel=1, num_class=2)

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

---

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.
• 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.

• -ph/--phase [Required]
  • Which phase to run? 'train' (runs all train, validation, test steps) OR 'test' (runs only test step).
• -b/--batch_size [4]
• -ep/--epochs [51]
• -lr/--learning_rate [0.001]
• -gpus/--gpus [0]
  • List of gpus to be used. Eg. [0], [1], [0, 1]
• -nw/--num_workers [4]
  • Number of workers for data loading so that cpu can keep-up with GPU speed when loading mini-batches.
• -lim/--load-limit[max]
  • Specifies a limit on images/files to load for debug purpose for pipeline debugging.
• -nf/--num_folds [None]
  • Number of folds in k-fold cross validation(Integer value like 5, 10).
• -spl/--split_ratio [0.6 0.2 0.2]
  • Split ratio for train, validation, test set if two items given| train, test if three items given| train only if one item given.
• ...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}
}