easytorch 2.9.0

Easy Neural Network Experiments with pytorch

A complete and robust pytorch prototyping framework with no learning curve.

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Installation

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

'How to use?' you ask!

• Minimalist MNIST
• Retinal blood vessel segmentation with U-Net.
• Covid-19 chest x-ray classification.
• DCGAN.

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Feature Higlights

• Minimal configuration to setup any simple/complex experiment (Single GPU, DP, and DDP usage).
• DataHandle that is always available, and decoupled from other modules enabling easy customization (ETDataHandle).
  • Use custom & complex data handling mechanism.
  • Load folder datasets.
  • Load recursively large datasets with multiple threads.
• Full support to split images into patches and rejoin/merge them to get back the complete prediction image like in U-Net(Usually needed when input images are large, and of different shapes) (Thanks to sparse data loaders).
• Limit data loading- Limit data to debug the pipeline without moving data from the original place (Thanks to load_limit)
• Heterogeneous datasets handling-One can use many folders of dataset by just defining dataspecs and use in single experiment(Thanks to pooled run).
• Automatic k-fold cross validation/Auto dataset split (Example: num_folds=10, or split_ratio=[0.6, 0.2, 0.2])
• Simple lightweight logger/plotter.
  • Plot: set log_header = 'Loss,F1,Accuracy' to plot in same plot or set log_header = 'Loss|F1,Accuracy' to plot Loss in one plot, and F1,Accuracy in another plot.
  • Logs: all logs/arguments will be in logs.json file after the experiment finishes.
• Gradient accumulation, automatic logging/plotting, model checkpointing ..more features
• For advanced training with multiple networks, and complex training steps, click here:
• Implement custom metrics as here.

General use case:

1. Define your trainer

from easytorch import ETTrainer, Prf1a, ConfusionMatrix


class MyTrainer(ETTrainer):

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

    def iteration(self, batch):
        inputs = batch[0].to(self.device['gpu']).float()
        labels = batch[1].to(self.device['gpu']).long()

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

        _, 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, 'metrics': sc, 'predictions': pred}

    def new_metrics(self):
        return Prf1a()

    def init_experiment_cache(self):
        self.cache['log_header'] = 'Loss|Accuracy,F1_score'  # Will plot Loss in one plot, and Accuracy,F1 in another.
        self.cache.update(monitor_metric='f1', metric_direction='maximize')  # Model selection

• Method new_metrics() uses:
  • Prf1a() for binary classification that computes accuracy,f1,precision,recall.
  • Or ConfusionMatrix(num_classes=...) for multiclass classification that also computes global accuracy,f1,precision,recall.
  • Or any custom implementation of easytorch.metrics.ETMetrics()

2. Use custom or pytorch based Datasets class.

Define specification for your datasets:

import os

def get_label1(x):
  return x.split('_')[0] + 'label.csv'

sep = os.sep
MYDATA = {
    'name': 'mydata',
    'data_dir': 'MYDATA' + sep + 'images',
    'label_dir': 'MYDATA' + sep + 'labels',
    'split_dir': 'MYDATA' + sep + 'splits', """For custom splits."""
    'label_getter': get_label1
}

def get_label2(x):
  return x.split('_')[0] + 'label.csv'

MyOTHERDATA = {
    'name': 'otherdata',
    'data_dir': 'OTHERDATA' + sep + 'images',
    'label_dir': 'OTHERDATA' + sep + 'labels',
    'label_getter': get_label2
}

• EasyTorch automatically splits the data/images in 'data_dir' of dataspec as specified (split_ratio, or num_folds in EasyTorch Module as below), and runs accordingly.
• One can also provide custom splits(json files with train, validation, test data list) in the directory specified by split_dir in dataspec.
• Additional options in dataspecs:
  • Load from sub-folders, "sub_folders": ["class0", "class1", ... "class_K"]
  • Load recursively, "recursive": True
  • Filter by an extension, "extension": "png"
• Example:

DRIVE = {
    'name': 'DRIVE',
    'data_dir': 'DRIVE' + sep + 'images',
    'label_dir': 'DRIVE' + sep + 'manual',
    'mask_dir': 'DRIVE' + sep + 'mask',
    'split_dir': 'DRIVE' + sep + 'splits',
    'label_getter': get_label_drive,
    'mask_getter': get_mask_drive,
    'sub_folders': ['None', 'Mild', 'Severe', "Proliferative"],
    'extension': '.tif',
    'recursive': True
}

Define how to load each data item by using EasyTorch's base ETDataset class to get extra benefits like limit loading for debugging, pooling data, super-fast pre-processing with multiple processes, and many more ...

from easytorch import ETDataset
import torchvision


class MyDataset(ETDataset):
    def __init__(self, **kw):
        super().__init__(**kw)
        
    def load_index(self, dataset_name, file):
      """1. This method is a pre-processing step for all the files in the specified folders in dataspec."""
      """2. It is parallelized and uses num_workers number of processes to pre-load, preprocess data enabling us"""
      """   to perform such operations super fast"""
      """3. It is a pre-training step. So, a different mechanism then num_worker in data_loader object"""
      """   Example: any pre-processing masking, cropping patches for uneven images in U-Net"""
      pass

    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 image, label

3. Entry point

from easytorch import EasyTorch

runner = EasyTorch([MYDATA, MyOTHERDATA],
                   phase="train", batch_size=4, epochs=21,
                   num_channel=1, num_class=2,
                   split_ratio=[0.6, 0.2, 0.2])  # or num_folds=5 (exclusive with split_ratio)

if __name__ == "__main__":
    """Runs experiment for each dataspec items in the same order"""
    runner.run(MyTrainer, MyDataset)
  
    """Runs by pooling all dataspecs as a single experiment"""
    # runner.run_pooled(MyTrainer, MyDataset)

One of: custom splits (specified by split_dir key in dataspec), num_folds, or split_ratio must be given unless providing custom dataset objects as:

(From MNIST example above):

train_dataset = datasets.MNIST('../data', train=True, download=True,
                               transform=transform)
val_dataset = datasets.MNIST('../data', train=False,
                             transform=transform)
              
dataloader_args = {
  'train': {'dataset': train_dataset, "drop_last":True},
  'validation': {'dataset': val_dataset, "batch_size":256}
}
runner = EasyTorch(phase='train',
                   batch_size=128, epochs=5, gpus=[0],
                   dataloader_args=dataloader_args)
runner.run(MNISTTrainer)

---

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 [11]
• -lr/--learning_rate [0.001]
• -gpus/--gpus [0]
  • List of gpus to be used. Eg. [0], [1], [0, 1]
• -nw/--num_workers [0]
  • Number of workers for data loading so that cpu can keep-up with GPU speed when loading mini-batches.
• -lim/--load-limit[None]
  • 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 [None]
  • 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 (ddp args)

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}
}