Paligemma_Processing.py

from typing import Dict, List, Tuple, Optional, Union, Iterable
import numpy as np
from PIL import Image
import torch
IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5] #RGB
IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5]

def add_image_tokens_to_prompt(prefix_prompt, bos_token, image_seq_len, image_token): #bos -> beginning of sentence (same for eos)

    return f"{image_token * image_seq_len}{bos_token}{prefix_prompt}\n" #\n should be tokenized differently, but as per HF it should be like this only.
    
def resize(
    image: Image,
    size: Tuple[int, int],
    resample: Image.Resampling = None,
    reducing_gap: Optional[int] = None,
) -> np.ndarray:
    height, width = size
    resized_image = image.resize(
        (width, height), resample=resample, reducing_gap=reducing_gap
    )
    return resized_image

def rescale(
        image: np.ndarray, scale: float, dtype: np.dtype = np.float32
) -> np.ndarray:
    rescaled_image = image * scale
    rescaled_image = rescaled_image.astype(dtype)
    return rescaled_image

def normalize (
        image: np.ndarray,
        mean: Union[float, Iterable[float]],
        std: Union[float, Iterable[float]],
) -> np.ndarray:
    mean = np.array(mean, dtype=image.dtype)
    std = np.array(std, dtype=image.dtype)
    image = (image - mean) / std
    return image

def process_images(
    images: List[Image.Image],
    size: Dict[str, int] = None,
    resample: Image.Resampling = None,
    rescale_factor: float = None,
    image_mean: Optional[Union[float, List[float]]] = None,
    image_std: Optional[Union[float, List[float]]] = None,
) -> List[np.ndarray]:
    height, width = size[0], size[1]
    images = [
        resize(image=image, size = (height, width), resample=resample) for image in images
    ]

    images = [np.array(image) for image in images]

    images = [rescale(image, scale=rescale_factor) for image in images]

    images = [normalize(image, mean=image_mean, std=image_std) for image in images]

    images = [image.transpose(2, 0, 1) for image in images]
    return images

#Gemma_Tokenizer
class PaliGemmaProcessor:

    IMAGE_TOKEN = "<image>"
    
    def __init__(self, tokenizer, num_image_token: int, image_size: int):
        super().__init__()

        self.image_seq_length = num_image_token
        self.image_size = image_size

        tokens_to_add = {"additional_special_tokens": [self.IMAGE_TOKEN]}
        tokenizer.add_special_tokens(tokens_to_add)
        EXTRA_TOKENS = [
            f"<loc{i:04d}>" for i in range(1024) #LOCATION TOKENS
        ]
        EXTRA_TOKENS += [
            f"<img{i:03d}>" for i in range(128) #IMAGE TOKENS
        ]
        tokenizer.add_tokens(EXTRA_TOKENS)
        self.image_token_id = tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)

        tokenizer.add_bos_token = False
        tokenizer.add_eos_token = False

        self.tokenizer = tokenizer

    def __call__(
            self,
            text: List[str],
            images: List[Image.Image],
            padding: str = "longest",
            truncation: bool = True,
    ) -> dict:
        assert len(images) == 1 and len(text) == 1, f"Received {len(images)} images for {len(text)} prompts."

        pixel_values = process_images(
            images,
            size= (self.image_size, self.image_size),
            resample= Image.Resampling.BICUBIC,
            rescale_factor = 1/ 255.0,
            image_mean = IMAGENET_STANDARD_MEAN,
            image_std = IMAGENET_STANDARD_STD,
        )

        pixel_values = np.stack(pixel_values, axis=0) #converted the pixel values into the array
        pixel_values = torch.tensor(pixel_values) #converted into tensor values

        input_strings = [
            add_image_tokens_to_prompt(
                prefix_prompt = prompt,
                bos_token=self.tokenizer.bos_token,
                image_seq_len=self.image_seq_length,
                image_token=self.IMAGE_TOKEN,
            )
            for prompt in text
        ]

        inputs = self.tokenizer(
            input_strings,
            return_tensors="pt",
            padding=padding,
            truncation=truncation,
        )

        return_data = {"pixel_values": pixel_values, **inputs}

        return return_data