data_preprocessing.py

import os
import re
import tokenizers
import torch
import torchvision
import torchvision.transforms as T
import tqdm
import PIL
from torch.utils.data import Dataset, DataLoader


directory = "/external2/dkkoshman/repos/ML2TransformerApp/data/"

class TexImageDataset(Dataset):
    """Image to tex dataset."""

    def __init__(self, root_dir, image_preprocessing=None, tex_preprocessing=None):
        """
        Args:
            root_dir (string): Directory with all the images and tex files.
            transform (callable, optional): Optional transform to be applied
                on a sample.
                
            image_preprocessing: callable image preprocessing
            
            tex_preprocessing: callable tex preprocessing
        """
        
        torch.multiprocessing.set_sharing_strategy('file_system')
        self.root_dir = root_dir
        filenames = sorted(
            set(os.path.splitext(filename)[0] for filename in os.listdir(root_dir) if filename.endswith('png'))
        )
        self.data = []
        
        for filename in tqdm.tqdm(filenames):
            tex_path = self.root_dir + filename + '.tex'
            image_path = self.root_dir + filename + '.png'
            
            with open(tex_path) as file:
                tex = file.read()
            if tex_preprocessing:
                tex = tex_preprocessing(tex)
                
            image = torchvision.io.read_image(image_path)
            if image_preprocessing:
                image = image_preprocessing(image)
                
            self.data.append((image, tex))

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

    def __getitem__(self, idx):
        image, tex = self.data[idx]
        return {"image": image, "tex": tex}
    
    
class StandardizeImage(object):
    """Pad and crop image to a given size, invert and normalize"""

    def __init__(self, width=1024, height=128):
        self.transform = T.Compose((
            T.Resize(height),
            T.Grayscale(),
            T.functional.invert,
            T.CenterCrop((height, width))
        ))

    def __call__(self, image):
        image = self.transform(image)
        return image
    
    
class RandomTransformImage(object):
    """Standardize image and randomly augment"""

    def __init__(self, standardize, random_magnitude=5):
        self.brighten = T.ColorJitter(brightness=(1/random_magnitude, 1 + 1/random_magnitude))
        self.standardize = standardize
        self.rand_aug = T.RandAugment(magnitude=random_magnitude)

    def __call__(self, image):
        image = self.brighten(image)
        image = self.standardize(image)
        image = image.contiguous()
        image = self.rand_aug(image)
        return image


def generate_tex_tokenizer(texs):
    """Returns a tokeniser trained on tex strings from dataset"""
    
    tokenizer = tokenizers.Tokenizer(tokenizers.models.BPE(unk_token="[UNK]"))
    tokenizer_trainer = tokenizers.trainers.BpeTrainer(
        vocab_size=300,
        special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
    )
    tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Whitespace()
    tokenizer.train_from_iterator(texs, trainer=tokenizer_trainer)
    tokenizer.post_processor = tokenizers.processors.TemplateProcessing(
        single="$A [SEP]",
        special_tokens=[("[SEP]", tokenizer.token_to_id("[SEP]"))]
    )
    tokenizer.enable_padding(pad_id=tokenizer.token_to_id("[PAD]"), pad_token="[PAD]")
    
    return tokenizer