processing.py

from fractions import Fraction

from transformers import LlavaNextProcessor
from transformers.image_processing_utils import select_best_resolution



class Granite4VisionProcessor(LlavaNextProcessor):
    model_type = "granite4_vision"

    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        patch_size=None,
        vision_feature_select_strategy=None,
        chat_template=None,
        image_token="<image>",  # set the default and let users change if they have peculiar special tokens in rare cases
        num_additional_image_tokens=0,
        downsample_rate=None,
        **kwargs,
    ):
        super().__init__(image_processor=image_processor, 
                         tokenizer=tokenizer,
                         patch_size=patch_size,
                         vision_feature_select_strategy=vision_feature_select_strategy,
                         chat_template=chat_template,
                         image_token=image_token,
                         num_additional_image_tokens=num_additional_image_tokens,
                         )
        self.downsample_rate = downsample_rate

    def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
        image_grid_pinpoints = self.image_processor.image_grid_pinpoints

        height_best_resolution, width_best_resolution = select_best_resolution(
            [orig_height, orig_width], image_grid_pinpoints
        )
        scale_height, scale_width = height_best_resolution // height, width_best_resolution // width

        patches_height = height // self.patch_size
        patches_width = width // self.patch_size
        if self.downsample_rate is not None:
            # todo: maybe add an assertion that it divides nicely?
            ds_rate = Fraction(self.downsample_rate)
            patches_height = int(patches_height * ds_rate)
            patches_width = int(patches_width * ds_rate)
            
        unpadded_features, newline_features = self._get_unpadded_features(
            orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
        )
        # The base patch covers the entire image (+1 for the CLS)
        base_features = patches_height * patches_width + self.num_additional_image_tokens
        num_image_tokens = unpadded_features + newline_features + base_features
        return num_image_tokens