easytorch 3.5.2

Easy Neural Network Experiments with pytorch

A very lightweight framework on top of PyTorch with full functionality.

---

• Introduces two extra multi-processing handles for blazing fast training by extending the easytorch.ETDataset class:

  • Multi-threaded data pre-loading.
  • Disk caching for faster access.

from easytorch import ETDataset

class MyDataset(ETDataset):
    def load_index(self, file):
        """(Optional) Load/Process something and add to diskcache as:
                self.diskcahe.add(file, value)"""
        """This method runs in multiple processes by default"""
    
        self.indices.append([file, 'something_extra'])

    def __getitem__(self, index):
        file = self.indices[index]
        """(Optional) Retrieve from diskcache as self.diskcache.get(file)"""

        image =  # Todo # Load file/Image. 
        label =  # Todo # Load corresponding label.
        
        # Extra preprocessing, if needed.
        # Apply transforms, if needed.

        return image, label

Installation

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

• MNIST
• Retinal fundus image transfer learning tasks.
• Covid-19 chest x-ray classification.
• DCGAN.

Lets start something simple like MNIST digit classification:

from easytorch import EasyTorch, ETTrainer, ConfusionMatrix, ETMeter
from torchvision import datasets, transforms
import torch.nn.functional as F
import torch
from examples.models import MNISTNet

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])


class MNISTTrainer(ETTrainer):
    def _init_nn_model(self):
        self.nn['model'] = MNISTNet()

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

        out = self.nn['model'](inputs)
        loss = F.nll_loss(out, labels)
        _, pred = torch.max(out, 1)
        
        meter = self.new_meter()
        meter.averages.add(loss.item(), len(inputs))
        meter.metrics['cfm'].add(pred, labels.float())

        return {'loss': loss, 'meter': meter, 'predictions': pred}

    def init_experiment_cache(self):
        self.cache['log_header'] = 'Loss|Accuracy,F1,Precision,Recall'
        self.cache.update(monitor_metric='f1', metric_direction='maximize')

    def new_meter(self):
        return ETMeter(
            cfm=ConfusionMatrix(num_classes=10)
        )


if __name__ == "__main__":
    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}, 'validation': {'dataset': val_dataset}}
    runner = EasyTorch(phase='train', batch_size=512,
                       epochs=10, gpus=[0], dataloader_args=dataloader_args)
    runner.run(MNISTTrainer)

---

General use case:

1. Define your trainer

from easytorch import ETTrainer, Prf1a, ETMeter, AUCROCMetrics


class MyTrainer(ETTrainer):

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

    def iteration(self, batch):
        """Handle a single batch"""
        """Must have loss and meter"""
        meter = self.new_meter()
        ...
        return {'loss': ..., 'meter': ..., 'predictions': ...}

    def new_meter(self):
       return ETMeter(
            num_averages=1,
            prf1a=Prf1a(),
            auc=AUCROCMetrics()
        )

    def init_cache(self):
        """Will plot Loss in one plot, and Accuracy,F1_score in another."""
        self.cache['log_header'] = 'Loss|Accuracy,F1_score'
        
        """Model selection using validation set if present"""
        self.cache.update(monitor_metric='f1', metric_direction='maximize')

• Method new_meter() returns ETMeter that takes any implementation of easytorch.meter.ETMetrics. Provided ones:
  • easytorch.metrics.Prf1a() for binary classification that computes accuracy,f1,precision,recall, overlap/IOU.
  • easytorch.metrics.ConfusionMatrix(num_classes=...) for multiclass classification that also computes global accuracy,f1,precision,recall.
  • easytorch.metrics.AUCROCMetrics for binary ROC-AUC score.

2. Define specification for your datasets:

• EasyTorch automatically splits the training data in data_source as specified by split_ratio
  • train, validation, and test as data_source=[0.7, 0.15, 0.15], or just train, validation [0.8, 0.2] OR
  • Custom splits in txt files:
    • data_source = "/some/path/*.txt", where it tries to load 'train.txt', 'validation.txt', and 'test.txt' if phase is train only 'test.txt' if phase is test
  • data_source = "some/path/split.json", where each split key has list of files as:
    • {'train': [], 'validation' :[], 'test':[]}
  • just glob as data_source = "some/path/**/*.txt", must also provide split_ratio if phase = train

3. Entry point (say main.py)

from easytorch import EasyTorch

runner = EasyTorch(phase="train", batch_size=4, epochs=21,
                   num_channel=1, num_class=2,
                   split_ratio=[0.6, 0.2, 0.2])

#OR

python main.py -ph train -b 4 -e 50 -nc 3 -spl 0.8 0.1 0.1

Note: See easytorch.config.init.py for full list of args

OR

runner = EasyTorch(yaml_config="path/toyaml/file/with/args/as/in/easytorch.confi/default_confi.yaml")

if __name__ == "__main__":
    runner.run(MyTrainer, MyDataset) # To train an individual models for each datasets. 

Run from the command line:

python main.py -ph train -b 4 -e 21 -spl 0.6 0.2 0.2

Note: directly given(EasyTorch constructor) args precedes command line arguments. See below for a list of default arguments.

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All the best! Cheers! 🎉

Cite the following papers if you use this library:

@article{deepdyn_10.3389/fcomp.2020.00035,
	title        = {Dynamic Deep Networks for Retinal Vessel Segmentation},
	author       = {Khanal, Aashis and Estrada, Rolando},
	year         = 2020,
	journal      = {Frontiers in Computer Science},
	volume       = 2,
	pages        = 35,
	doi          = {10.3389/fcomp.2020.00035},
	issn         = {2624-9898}
}

@misc{2202.02382,
        Author       = {Aashis Khanal and Saeid Motevali and Rolando Estrada},
        Title        = {Fully Automated Tree Topology Estimation and Artery-Vein Classification},
        Year         = {2022},
        Eprint       = {arXiv:2202.02382},
}

Feature Higlights:

• DataHandle that is always available, and decoupled from other modules enabling easy customization (ETDataHandle).
  • Use custom & complex data handling mechanism.
• 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 arguments/generated data will be saved in logs.json file after the experiment finishes.
• Gradient accumulation, automatic logging/plotting, model checkpointing
• Multiple metrics implementation at easytorch.metrics: Precision, Recall, Accuracy, Overlap, F1, ROC-AUC, Confusion matrix ..more features
• For advanced training with multiple networks, and complex training steps, click here:
• Implement custom metrics as here.