# Vision Transformer training with PyTorch Lightning, HuggingFace and lucidrains's vit-pytorch
## What's PyTorch Lightning
Fundamentally it's a wrapper for PyTorch training and testing code; it allows for encapsulation and organization of PyTorch models and corresponding associated components and functions, into Lightning Modules.

## PT Lightning Modules and PT Trainer:
PT Lightning Modules help organize all the components of a machine learning system into one single class, including the training, validation and testing, along with optimizers, (and optionally data) into one single class.

The PT Trainer takes the Lightning Module and helps abstract the loops and other mostly engineering aspects of the training and evaluation system, while keeping it easily accessible in case there needs to be any modification. Of particular noteworthiness it massively simplifies acceleration using CPU/GPU/TPUs and allows for easy parallel training, usage of mixed-precision, logging, and other (sometimes) cumbersome optimization tricks.

## Example: Pt Lightning Module
For a simple image classification problem using [lucidrains](https://github.com/lucidrains/vit-pytorch)'s implementation of the [Vision Transformer (ViT)](https://arxiv.org/abs/2010.11929) and an extended version of [HuggingFace](https://huggingface.co/)'s configuration wrapper:

# Installing libraries
!pip install pytorch-lightning
!pip install vit-pytorch
!pip install transformers
!pip install wandb
!pip install torchmetrics

# Importing
# Weights & Biases
import wandb
from pytorch_lightning.loggers import WandbLogger

# Pytorch modules
import torch
import torch.nn
import torchvision.models as models
from torch.nn import functional as F
from torch import nn
from torch.optim import Adam
from torch.utils.data import DataLoader, random_split

# Pytorch-Lightning
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
import pytorch_lightning as pl
import torchmetrics

from vit_pytorch import ViT

from transformers import ViTConfig

class ViTConfigExtended(ViTConfig):
    def __init__(self, hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        is_encoder_decoder=False,
        image_size=224,
        patch_size=16,
        num_channels=3,
        num_classes: int = 1000):
        super().__init__()
        self.num_classes = num_classes
        
configuration = ViTConfigExtended()

Vision Transformer Backbone class:

class VisionTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.model = ViT(
            image_size = config.image_size,
            patch_size = config.patch_size,
            num_classes = config.num_classes,
            dim = config.hidden_size,
            depth = config.num_hidden_layers,
            heads = config.num_attention_heads,
            mlp_dim = config.intermediate_size,
            dropout = config.hidden_dropout_prob,
            emb_dropout = config.attention_probs_dropout_prob
        )
    
    @torch.no_grad()
    def init_weights(self):
        def _init(m):
            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight)
                if hasattr(m, 'bias') and m.bias is not None:
                    nn.init.normal_(m.bias, std=1e-6)
            
        self.apply(_init)
        nn.init.constant_(self.model.fc.weight, 0)
        nn.init.constant_(self.model.fc.bias, 0)
    
    def forward(self, x):
        return self.model(x)

Lightning Module and Backbone classes:

class Backbone(torch.nn.Module):
    def __init__(self, model_type, config):
        super().__init__()
        if model_type == 'vit'
            self.model = VisionTransformer(config)
        
    def forward(self, x):
        return self.model(x)

class LitClassifier(pl.LightningModule):
    def __init__(self, backbone, learning_rate=1e-3):
        super().__init__()
        self.save_hyperparameters()
        self.backbone = backbone
        self.val_acc = torchmetrics.Accuracy()
        self.test_acc = torchmetrics.Accuracy()

    def forward(self, x):
        # use forward for inference/predictions
        embedding = self.backbone(x)
        return embedding

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.backbone(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('train_loss', loss, on_epoch=True, on_step=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.backbone(x)
        loss = F.cross_entropy(y_hat, y)
        self.val_acc(y_hat.softmax(dim=-1), y)
        metrics = {'val_acc': self.val_acc, 'val_loss': loss}
        self.log_dict(metrics, on_epoch=True, on_step=False)

    def test_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.backbone(x)
        loss = F.cross_entropy(y_hat, y)
        self.test_acc(y_hat.softmax(dim=-1), y)
        self.log('test_acc', self.test_acc, on_epoch=True, on_step=False)

    def configure_optimizers(self):
        # self.hparams available because we called self.save_hyperparameters()
        return torch.optim.Adam(self.parameters(), lr=1e-4)

    @staticmethod
    def add_model_specific_args(parent_parser):
        parser = ArgumentParser(parents=[parent_parser], add_help=False)
        parser.add_argument('--learning_rate', type=float, default=0.0001)
        return parser

## Example: PT Lightning DataModule

PT Lightning also helps with organizing datasets, transforms and dataloaders into self-containing DataModules. Here's an example with CIFAR-10 and CIFAR-100 DataModules:

from torch.utils.data import DataLoader, random_split

# Pytorch-Lightning
from pytorch_lightning import LightningDataModule

# Dataset
from torchvision.datasets import CIFAR10, CIFAR100
from torchvision import transforms

class CIFAR10DataModule(LightningDataModule):

    def __init__(self, data_dir='./data/', batch_size=256, image_size=32):
        super().__init__()
        self.data_dir = data_dir
        self.batch_size = batch_size
        self.image_size = image_size
        self.transform_train = transforms.Compose([
                    transforms.Resize((self.image_size+32, self.image_size+32)), 
                    transforms.RandomCrop((self.image_size, self.image_size)),
                    transforms.RandomHorizontalFlip(),
                    transforms.ColorJitter(brightness=0.1, 
                    contrast=0.1, saturation=0.1, hue=0.1),
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                            std=[0.5, 0.5, 0.5])])
        self.transform_eval = transforms.Compose([
                    transforms.Resize((self.image_size, self.image_size)), 
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5],
                    std=[0.5, 0.5, 0.5])])

    def prepare_data(self):
        '''called only once and on 1 GPU'''
        # download data
        CIFAR10(self.data_dir, train=True, download=True)
        CIFAR10(self.data_dir, train=False, download=True)

    def setup(self, stage=None):
        '''called on each GPU separately - stage defines if we are at fit or test step'''
        # we set up only relevant datasets when stage is specified (automatically set by Pytorch-Lightning)
        if stage == 'fit' or stage is None:
            dataset_train = CIFAR10(self.data_dir, train=True, transform=self.transform_train)
            no_train = int(len(cifar_train) * 0.9)
            no_val = len(cifar_train) - no_train
            self.dataset_train, self.dataset_val = random_split(dataset_train, [no_train, no_val])
            self.num_classes = len(dataset_train.classes)
        if stage == 'test' or stage is None:
            self.dataset_test = CIFAR10(self.data_dir, train=False, transform=self.transform_eval)
            self.num_classes = len(self.cifar_test.classes)

    def train_dataloader(self):
        '''returns training dataloader'''
        dataloader_train = DataLoader(self.dataset_train, batch_size=self.batch_size, num_workers=4)
        return dataloader_train

    def val_dataloader(self):
        '''returns validation dataloader'''
        dataloader_val = DataLoader(self.dataset_val, batch_size=self.batch_size, num_workers=4)
        return dataloader_val

    def test_dataloader(self):
        '''returns test dataloader'''
        dataloader_test = DataLoader(self.dataset_test, batch_size=self.batch_size, num_workers=4)
        return dataloader_test

class CIFAR100DataModule(CIFAR10DataModule):

    def __init__(self):
        super().__init__()

    def prepare_data(self):
        '''called only once and on 1 GPU'''
        # download data
        CIFAR100(self.data_dir, train=True, download=True)
        CIFAR100(self.data_dir, train=False, download=True)

    def setup(self, stage=None):
        '''called on each GPU separately - stage defines if we are at fit or test step'''
        # we set up only relevant datasets when stage is specified (automatically set by Pytorch-Lightning)
        if stage == 'fit' or stage is None:
            dataset_train = CIFAR10(self.data_dir, train=True, transform=self.transform_train)
            no_train = int(len(cifar_train) * 0.9)
            no_val = len(cifar_train) - no_train
            self.dataset_train, self.dataset_val = random_split(dataset_train, [no_train, no_val])
            self.num_classes = len(dataset_train.classes)
        if stage == 'test' or stage is None:
            self.dataset_test = CIFAR10(self.data_dir, train=False, transform=self.transform_eval)
            self.num_classes = len(self.cifar_test.classes)

## Example: Training with PT Trainer and Logging with W&B

We will use [Weights and Biases](https://wandb.ai/) for their logging service. We could otherwise use any other logger such as Tensorboard or MLflow:

pl.seed_everything(0)
wandb_logger = WandbLogger(project='ViT_experiments')

Preparing data:

# setup data
dm = CIFAR10DataModule(batch_size=32, image_size=configuration.image_size)

The next one is an optional step. It be useful if we don't know the size of our dataset beforehand since it setups the datamodule and therefore may return information such as image size and number of classes but in this case we don't need it since we already know it beforehand:

dm.prepare_data()
dm.setup('fit')

Setting up last few touches and Launching PT Trainer:

# setup model and trainer 
backbone = Backbone(model_type='vit', num_classes=configuration.num_classes)
model = LitClassifier(backbone)
if torch.cuda.is_available():
    trainer = pl.Trainer(gpus=1, max_epochs=10, logger=wandb_logger)
else:
    trainer = pl.Trainer(max_epochs=10, logger=wandb_logger)

# train, validate
trainer.fit(model, dm)

Testing and wrapping up experiments:

trainer.test()
wandb.finish()

## Additional examples

For reference here's a pure PyTorch dataset class based on ImageFolder: ImageNet DataModule:

import os

from torch.utils.data import DataLoader, random_split
from torchvision import transforms, datasets

class ImageNet:
    # https://github.com/pytorch/examples/tree/master/imagenet
    # https://www.kaggle.com/c/imagenet-object-localization-challenge/overview
    # to do: map the classes by making a class_to_idx or something dictionary
    def __init__(self, root, input_size=224, 
    split='train', transform=None):
        super().__init__()
        self.root = os.path.abspath(root)
        self.input_size = input_size
        self.split = split
        self.transform = transform

        if self.split == 'train':
            data_path = os.path.join(self.root, 'train')
            self.dataset = datasets.ImageFolder(root=data_path,
            transform=self.transform)
        else:
            data_path = os.path.join(self.root, 'val')
            self.dataset = datasets.ImageFolder(root=data_path,
            transform=self.transform)

        self.classes = self.dataset.class_to_idx
        self.no_classes = len(self.dataset.classes)

    def __len__(self):
        return len(self.dataset.data)

And here's the (roughly) equivalent version with PT Lightning DataModules:

import os

from torch.utils.data import DataLoader, random_split
from torchvision import transforms, datasets

class ImageNetDataModule(LightningDataModule):

    def __init__(self, data_dir: str, batch_size: int: 64, image_size: 224):
        super().__init__()
        self.data_dir = data_dir
        self.batch_size = batch_size
        self.image_size = image_size
        self.transform_train = transforms.Compose([
                    transforms.Resize((self.image_size+32, self.image_size+32)), 
                    transforms.RandomCrop((self.image_size, self.image_size)),
                    transforms.RandomHorizontalFlip(),
                    transforms.ColorJitter(brightness=0.1, 
                    contrast=0.1, saturation=0.1, hue=0.1),
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                            std=[0.5, 0.5, 0.5])])
        self.transform_eval = transforms.Compose([
                    transforms.Resize((self.image_size, self.image_size)), 
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5],
                    std=[0.5, 0.5, 0.5])])
        
    def setup(self, stage=None):
        '''called on each GPU separately - stage defines if we are at fit or test step'''
        # we set up only relevant datasets when stage is specified (automatically set by Pytorch-Lightning)
        if stage == 'fit' or stage is None:
            data_path = os.path.join(self.data_dir, 'train')
            dataset_train = datasets.ImageFolder(root=data_path,
            transform=self.transform_train)
            no_train = int(len(dataset_train) * 0.99)
            no_val = len(dataset_train) - no_train
            self.dataset_train, self.dataset_val = random_split(dataset_train, [no_train, no_val])
            self.num_classes = len(self.dataset_train.classes)
        if stage == 'test' or stage is None:
            data_path = os.path.join(self.data_dir, 'val')
            self.dataset_test = datasets.ImageFolder(root=data_path,
            transform=self.transform)
            self.num_classes = len(self.dataset_test.classes)
            
        self.classes = self.dataset.class_to_idx
        self.no_classes = len(self.dataset.classes)

    def train_dataloader(self):
        '''returns training dataloader'''
        dataloader_train = DataLoader(self.dataset_train, batch_size=self.batch_size, num_workers=4)
        return dataloader_train

    def val_dataloader(self):
        '''returns validation dataloader'''
        dataloader_val = DataLoader(self.dataset_val, batch_size=self.batch_size, num_workers=4)
        return dataloader_val

    def test_dataloader(self):
        '''returns test dataloader'''
        dataloader_test = DataLoader(self.dataset_test, batch_size=self.batch_size, num_workers=4)
        return dataloader_test

## Conclusion

This post goes through the basic workflow in transitioning from a pure PyTorch to a PyTorch + PyTorch Lightning one. It includes the main components of the PL Lightning: the LightningModule, the Trainer, and the DataModule. Examples were given for each of these components, and how to use them to wrap up a Vision Transformer for classification.

If you like this post, or have any questions, feel free to leave a comment or contact me on any of my socials, found at the bottom of my [Github Pages](https://arkel23.github.io/).