image_preprocessing_molmo.py

"""Image processor class for Molmo"""
"""NOTE: This is a modified version of the original image_preprocessing_molmo.py script that removes all tensorflow-related dependencies."""
from typing import List, Optional, Union, Mapping

import numpy as np
import einops
import torch
import torchvision.transforms
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import convert_image_dtype

from transformers.image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ImageInput,
    is_valid_image,
)
from transformers.processing_utils import ImagesKwargs
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.utils import TensorType, is_vision_available, logging


logger = logging.get_logger(__name__)


def make_batched_images(images) -> List[List[ImageInput]]:
    """
    Accepts images in list or nested list format, and makes a list of images for preprocessing.

    Args:
        images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
            The input image.

    Returns:
        list: A list of images.
    """
    if isinstance(images, (list, tuple)) and isinstance(images[0], (list, tuple)) and is_valid_image(images[0][0]):
        return [img for img_list in images for img in img_list]

    elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
        return images

    elif is_valid_image(images):
        return [images]

    raise ValueError(f"Could not make batched images from {images}")


def pad_to_bounding_box(
    image, offset_height, offset_width, target_height,
    target_width, value=0
):
    height, width = image.shape[:2]
    after_padding_width = target_width - offset_width - width
    after_padding_height = target_height - offset_height - height
    return np.pad(image, [
        [offset_height, after_padding_height],
        [offset_width, after_padding_width],
        [0, 0]
    ], constant_values=value)


def normalize_image(image, offset, scale):
    image -= np.array(offset, dtype=np.float32)[None, None, :]
    image /= np.array(scale, dtype=np.float32)[None, None, :]
    return image


def resize_and_pad(
    image: np.ndarray,
    desired_output_size: List[int],
    resize_method: str = "bilinear",
    pad_value: float = 0,
    normalize: bool = True,
    image_mean: Optional[List[float]] = OPENAI_CLIP_MEAN,
    image_std: Optional[List[float]] = OPENAI_CLIP_STD,
) -> (np.ndarray, np.ndarray):
    """
    Resize and pad the image to the desired output size.

    Args:
        image (np.ndarray): Input image as a NumPy array.
        desired_output_size (List[int]): Desired output size as [height, width].
        resize_method (str, optional): Resize interpolation method. Defaults to "bilinear".
        pad_value (float, optional): Padding value. Defaults to 0.
        normalize (bool, optional): Whether to normalize the image. Defaults to True.
        image_mean (Optional[List[float]], optional): Mean for normalization. Defaults to OPENAI_CLIP_MEAN.
        image_std (Optional[List[float]], optional): Standard deviation for normalization. Defaults to OPENAI_CLIP_STD.

    Returns:
        Tuple[np.ndarray, np.ndarray]: Resized and padded image, and image mask.
    """
    desired_height, desired_width = desired_output_size
    height, width = image.shape[:2]

    # Calculate scaling factors and determine the scaling factor to maintain aspect ratio
    scale_y = desired_height / height
    scale_x = desired_width / width
    scale = min(scale_x, scale_y)
    scaled_height = int(height * scale)
    scaled_width = int(width * scale)

    # Convert the image to a PyTorch tensor and normalize to [0, 1]
    image_tensor = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0

    # Define the interpolation mode
    if resize_method.lower() == "bilinear":
        interpolation = InterpolationMode.BILINEAR
    elif resize_method.lower() == "nearest":
        interpolation = InterpolationMode.NEAREST
    elif resize_method.lower() == "bicubic":
        interpolation = InterpolationMode.BICUBIC
    elif resize_method.lower() == "lanczos":
        interpolation = InterpolationMode.LANCZOS
    else:
        raise ValueError(f"Unsupported resize method: {resize_method}")

    # Resize the image
    resized_image = torchvision.transforms.Resize(
        [scaled_height, scaled_width],
        interpolation=interpolation,
        antialias=True
    )(image_tensor)

    # Clip the image to ensure values are within [0, 1]
    resized_image = torch.clamp(resized_image, 0.0, 1.0)

    # Convert back to NumPy
    resized_image_np = resized_image.permute(1, 2, 0).numpy()

    # Calculate padding
    top_pad = (desired_height - scaled_height) // 2
    bottom_pad = desired_height - scaled_height - top_pad
    left_pad = (desired_width - scaled_width) // 2
    right_pad = desired_width - scaled_width - left_pad

    # Pad the image using NumPy
    padded_image = np.pad(
        resized_image_np,
        pad_width=((top_pad, bottom_pad), (left_pad, right_pad), (0, 0)),
        mode='constant',
        constant_values=pad_value
    )

    # Create the image mask
    image_mask = np.pad(
        np.ones((scaled_height, scaled_width), dtype=bool),
        pad_width=((top_pad, bottom_pad), (left_pad, right_pad)),
        mode='constant',
        constant_values=False
    )

    if normalize:
        padded_image = normalize_image(padded_image, offset=image_mean, scale=image_std)

    return padded_image, image_mask



def select_tiling(h, w, patch_size, max_num_patches):
    """Decide how best to divide in image of size [w, h] in up to max_num_patches of size patch_size"""
    original_size = np.stack([h, w])  # [1, 2]
    original_res = h * w
    tilings = []
    for i in range(1, max_num_patches+1):
        for j in range(1, max_num_patches+1):
            if i*j <= max_num_patches:
                tilings.append((i, j))
    # sort so argmin and argmax favour smaller tilings in the event of a tie
    tilings.sort(key=lambda x: (x[0]*x[1], x[0]))
    candidate_tilings = np.array(tilings, dtype=np.int32)  # [n_resolutions, 2]
    candidate_resolutions = candidate_tilings * patch_size  # [n_resolutions, 2]

    # How much we would need to scale the image to fit exactly in each tiling
    original_size = np.stack([h, w], dtype=np.float32)  # [1, 2]
    required_scale_d = candidate_resolutions.astype(np.float32) / original_size
    required_scale = np.min(required_scale_d, axis=-1, keepdims=True)  # [n_resolutions, 1]
    if np.all(required_scale < 1):
        # We are forced to downscale, so try to minimize the amount of downscaling
        ix = np.argmax(required_scale)
    else:
        # Pick the resolution that required the least upscaling so that it most closely fits the image
        required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
        ix = np.argmin(required_scale)
    return candidate_tilings[ix]


class MolmoImagesKwargs(ImagesKwargs, total=False):
    max_crops: Optional[int]
    overlap_margins: Optional[List[int]]
    base_image_input_size: Optional[List[int]]
    image_token_length_w: Optional[int]
    image_token_length_h: Optional[int]
    image_patch_size: Optional[int]
    image_padding_mask: Optional[bool]


class MolmoImageProcessor(BaseImageProcessor):
    """Preprocess images and multi-model inputs"""

    def __init__(
        self,
        max_crops: int = 12,
        overlap_margins: List[int] = (4, 4),
        base_image_input_size: List[int] = (336, 336),
        image_token_length_w: int = 12,
        image_token_length_h: int = 12,
        image_patch_size: int = 14,
        image_padding_mask: bool = True,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.max_crops = max_crops
        self.overlap_margins = overlap_margins
        self.base_image_input_size = base_image_input_size
        self.image_token_length_w = image_token_length_w
        self.image_token_length_h = image_token_length_h
        self.image_patch_size = image_patch_size
        self.image_padding_mask = image_padding_mask
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD

    def image_to_patches_and_tokens(
        self,
        image: ImageInput,
        image_patch_token_id: int,
        image_col_token_id: int,
        image_start_token_id: int,
        image_end_token_id: int,
        max_crops: Optional[int] = None,
        overlap_margins: Optional[List[int]] = None,
        base_image_input_size: Optional[Union[int, List[int]]] = None,
        image_token_length_w: Optional[int] = None,
        image_token_length_h: Optional[int] = None,
        image_patch_size: Optional[int] = None,
    ):
        """Preprocesses an image

        Returns:
            crops: (n_crops, n_patches, patch_dim) individual crops, `n_crops` might
                   change between images but the other dimension are fixed
            tokens: (n_tokens,) int32 tokens, pad tokens indicating where to insert the
                                patch features, might include other special tokens as well
            patch_ordering: (n_crops, n_tokens_per_crop) order image features should be inserted
                            into the `tokens`, negative values indicates patches features to exclude
            padding_mask: (n_crops, n_patches) what percent of each crop is padding, be None
                          if the image mask is not being used.
        """
        if isinstance(base_image_input_size, int):
            base_image_input_size = (base_image_input_size, base_image_input_size)

        base_image_input_d = image_patch_size
        tokens_per_image = image_token_length_w * image_token_length_h
        image_base_patch_w = base_image_input_size[1] // base_image_input_d
        image_base_patch_h = base_image_input_size[0] // base_image_input_d

        original_image_h, original_image_w = image.shape[:2]
        crop_size = base_image_input_size[0]

        # Discard this many patches from the (left/top, right/bottom) of crops
        left_margin, right_margin = overlap_margins
        # left_margin, right_margin = 2, 2
        assert left_margin % 2 == 0  # Required for compatibility with 2x2 pooling
        total_margin_pixels = base_image_input_d*(right_margin + left_margin)  # pixels removed per dim
        crop_patches = base_image_input_size[0] // base_image_input_d  # patches per crop dim
        crop_window_patches = crop_patches - (right_margin + left_margin)  # usable patches
        crop_window_size = crop_window_patches * base_image_input_d
        tiling = select_tiling(
            original_image_h - total_margin_pixels,
            original_image_w - total_margin_pixels,
            crop_window_size,
            max_crops
        )
        src, img_mask = resize_and_pad(
            image,
            [tiling[0]*crop_window_size+total_margin_pixels, tiling[1]*crop_window_size+total_margin_pixels]
        )

        # Now we have to split the image into crops, while keeping track of how each patch in the
        # each crop should be ordered in the global image, this require a lot of tricky booking
        n_crops = tiling[0] * tiling[1]
        patches_arr = []
        mask_arr = []
        patch_ordering_arr = []

        # We assume 2x2 pooling, but can allow padding the right/bottom with extra
        # patches if the number of patches per side is not even
        assert (crop_patches+1)//2 == image_token_length_h
        assert (crop_patches+1)//2 == image_token_length_w
        on = 0
        on_patch = 0
        for i in range(tiling[0]):
            y0 = i*crop_window_size
            if i == 0:
                crop_y0 = 0
            else:
                crop_y0 = left_margin // 2

            crop_h = image_base_patch_h - (right_margin + left_margin)
            if i == 0:
                crop_h += left_margin
            if i == (tiling[0]-1):
                crop_h += right_margin
            for j in range(tiling[1]):
                x0 = j*crop_window_size
                if j == 0:
                    crop_x0 = 0
                else:
                    crop_x0 = left_margin // 2

                crop_w = image_base_patch_w - (right_margin + left_margin)
                if j == 0:
                    crop_w += left_margin
                if j == (tiling[1]-1):
                    crop_w += right_margin

                pooled_w = (crop_w + 1) // 2
                pooled_h = (crop_h + 1) // 2
                patch_ordering_arr.append(
                    pad_to_bounding_box(
                        np.reshape(np.arange(on, on+pooled_h*pooled_w, dtype=np.int32), (pooled_h, pooled_w, 1)),
                        crop_y0, crop_x0, image_token_length_h, image_token_length_w, value=-1
                    )[:, :, 0]
                )
                patches_arr.append(src[y0:y0+crop_size, x0:x0+crop_size])
                mask_arr.append(img_mask[y0:y0+crop_size, x0:x0+crop_size])

                on += pooled_h*pooled_w
                on_patch += 1
        patches = np.stack(patches_arr)
        patch_ordering = np.stack(patch_ordering_arr)
        img_mask = np.stack(mask_arr)

        # Switch to [n_crops, n_patches, pixels_per_patch] format
        image_layout_impatch_w, image_layout_impatch_h = tiling[0], tiling[1]
        patches = einops.rearrange(
            patches, 'p (h dh) (w dw) c -> p (h w) (dh dw c)',
            dh=base_image_input_d,
            dw=base_image_input_d,
            h=image_base_patch_h,
            w=image_base_patch_w
        )
        img_mask = einops.rearrange(
            img_mask, 'p (h dh) (w dw) -> p (h w) (dh dw)',
            dh=base_image_input_d,
            dw=base_image_input_d,
            h=image_base_patch_h,
            w=image_base_patch_w
        )

        img_mask = img_mask.astype(np.float32).mean(axis=-1)
        patch_ordering = np.reshape(patch_ordering, [-1])
        valid = patch_ordering >= 0

        # Transpose order, to get left-to-right order instead of crop-by-crop order
        patch_ordering_rh = np.reshape(
            patch_ordering,
            [tiling[0], tiling[1], image_token_length_h, image_token_length_w]
        )
        patch_ordering_rh = np.transpose(patch_ordering_rh, [0, 2, 1, 3])
        patch_ordering_rh = np.reshape(patch_ordering_rh, [-1])

        # The transpose will screw up which patches are masked, project the
        # new order into sparse structure of `patch_ordering` to fix this
        patch_ordering[valid] = patch_ordering_rh[patch_ordering_rh >= 0]

        # Now build the output tokens
        h = tiling[0] * crop_window_patches + (right_margin+left_margin)
        w = tiling[1] * crop_window_patches + (right_margin+left_margin)
        per_row = np.full(
            ((w+1)//2,),
            image_patch_token_id,
        )
        per_row = np.concatenate([per_row, [image_col_token_id]], 0)

        joint = np.tile(per_row, [(h+1)//2])
        joint = [
            [image_start_token_id],
            joint,
            [image_end_token_id]
        ]

        # Finally do the same for the global image
        resized, _ = resize_and_pad(image, base_image_input_size)
        resized = einops.rearrange(
            resized, '(h dh) (w dw) c -> (h w) (dh dw c)',
            dh=base_image_input_d,
            dw=base_image_input_d,
            h=image_base_patch_h,
            w=image_base_patch_w
        )
        patches = np.concatenate([np.expand_dims(resized, 0), patches], 0)

        # Global image goes first, so the order of patches in previous crops gets increased
        patch_ordering = np.where(
            patch_ordering >= 0,
            patch_ordering + tokens_per_image,
            -1
        )
        patch_ordering = np.concatenate([np.arange(0, tokens_per_image), patch_ordering], 0)
        per_row = np.full(
            (image_token_length_w,),
            image_patch_token_id,
        )
        per_row = np.concatenate([per_row, [image_col_token_id]], 0)
        extra_tokens = np.tile(per_row, [image_token_length_h])
        joint = [
                    [image_start_token_id],
                    extra_tokens,
                    [image_end_token_id],
                ] + joint

        joint = np.concatenate(joint, 0)
        img_mask = np.pad(img_mask, [[0, 1], [0, 0]], constant_values=-1)
        return patches, joint, patch_ordering, img_mask

    def build_image_input_idx(
        self,
        image_tokens: np.ndarray,
        patch_order: np.ndarray,
        image_patch_token_id: int,
        no_image: Optional[bool] = None,
        image_token_length_w: Optional[int] = None,
        image_token_length_h: Optional[int] = None,
    ):
        """Converts `patch_order` into a mapping of token_id -> patch_id"""

        tokens_per_image = image_token_length_w * image_token_length_h
        if no_image is not None and no_image:
            return np.zeros((0, tokens_per_image), np.int32)

        # Indices to insert the patches
        image_input_idx = image_tokens == image_patch_token_id
        image_input_idx = np.nonzero(image_input_idx)[0].astype(np.int32)

        if patch_order is not None:
            n_tokens = image_input_idx.shape[0]
            patch_order = np.reshape(patch_order, [-1])
            n_patches = patch_order.shape[0]

            valid = patch_order >= 0
            n_valid_patches = valid.sum()
            assert len(image_input_idx) == n_valid_patches

            sorted_patch_ixs = np.zeros([n_tokens], np.int32)
            sorted_patch_ixs[patch_order[valid]] = np.arange(n_valid_patches, dtype=np.int32)

            # Project the inverted mapping into same sparse structure
            sorted_patch_ixs_ex = np.full(np.shape(patch_order), -1)
            sorted_patch_ixs_ex[valid] = sorted_patch_ixs

            # Do the gather and then re-masked outputs that were masked in `sorted_patch_ixs`
            valid = (sorted_patch_ixs_ex >= 0).astype(np.int32)
            image_input_idx = image_input_idx[sorted_patch_ixs_ex*valid]
            image_input_idx = image_input_idx*valid - 100*(1 - valid)
            image_input_idx = np.reshape(image_input_idx, [-1, tokens_per_image])
        return image_input_idx

    def preprocess(
        self,
        image: np.ndarray,
        image_patch_token_id: int,
        image_col_token_id: int,
        image_start_token_id: int,
        image_end_token_id: int,
        max_crops: Optional[int] = None,
        overlap_margins: Optional[List[int]] = None,
        base_image_input_size: Optional[Union[int, List[int]]] = None,
        image_token_length_w: Optional[int] = None,
        image_token_length_h: Optional[int] = None,
        image_patch_size: Optional[int] = None,
        **kwargs,
    ):
        """Preprocesses a single image"""

        max_crops = max_crops or self.max_crops
        overlap_margins = overlap_margins or self.overlap_margins
        base_image_input_size = base_image_input_size or self.base_image_input_size
        image_token_length_w = image_token_length_w or self.image_token_length_w
        image_token_length_h = image_token_length_h or self.image_token_length_h
        image_patch_size = image_patch_size or self.image_patch_size

        crops, image_tokens, patch_ordering, img_mask = self.image_to_patches_and_tokens(
            image,
            image_patch_token_id,
            image_col_token_id,
            image_start_token_id,
            image_end_token_id,
            max_crops,
            overlap_margins,
            base_image_input_size,
            image_token_length_w,
            image_token_length_h,
            image_patch_size,
        )
        patch_idx = self.build_image_input_idx(
            image_tokens,
            patch_ordering,
            image_patch_token_id,
            image_token_length_w=image_token_length_w,
            image_token_length_h=image_token_length_h,
        )
        return crops, image_tokens, patch_idx, img_mask

    def multimodal_preprocess(
        self,
        images: np.ndarray,
        tokens: List[int],
        image_idx: np.ndarray,
        sequence_length: int,
        image_patch_token_id: int,
        image_col_token_id: int,
        image_start_token_id: int,
        image_end_token_id: int,
        **kwargs,
    ):
        """Merge images and text tokens into multi-modal features for the model

        :param images: images to use as input
        :param tokens: input text tokens
        :param image_idx: where to insert the images into `tokens`
        :params image_patch_token_id: id to use of tokens that will contain image features
        :params image_col_token_id: token id for image column special tokens
        :params image_start_token_id: token id for image start special tokens
        :params image_end_token_id: token id for image end special tokens
        :params kwargs: override preprocessor default args
        """
        max_total_crops = kwargs.get("max_crops") or self.max_crops
        image_token_length_w = kwargs.get("image_token_length_w") or self.image_token_length_w
        image_token_length_h = kwargs.get("image_token_length_h") or self.image_token_length_h
        image_patch_size = kwargs.get("image_patch_size") or self.image_patch_size
        base_image_input_size = kwargs.get("base_image_input_size") or self.base_image_input_size
        image_num_patch = (
            base_image_input_size[0] // image_patch_size,
            base_image_input_size[1] // image_patch_size,
        )
        image_padding_mask = kwargs.get("image_padding_mask") or self.image_padding_mask

        tokens_per_image = image_token_length_w * image_token_length_h
        n_pixels = image_patch_size * image_patch_size * 3
        n_patches = image_num_patch[0] * image_num_patch[1]

        if images is None:
            return {
                "input_ids": tokens,
                "images": None,
                "image_input_idx": None
            }
        else:
            n = len(images)
            all_crops = []
            all_image_idx = []
            out_tokens = []
            all_crop_masks = []

            for ix in range(n):
                token_ix = image_idx[ix]
                crops, image_tokens, patch_idx, img_mask = self.preprocess(
                    images[ix],
                    image_patch_token_id,
                    image_col_token_id,
                    image_start_token_id,
                    image_end_token_id,
                    **kwargs,
                )

                if token_ix == -1:  # -1 is an image inserted at the very start
                    start = 0
                    token_ix = 0
                    end = 0
                else:
                    start = 0 if ix == 0 else image_idx[ix-1] + 1
                    end = token_ix + 1

                all_image_idx.append(patch_idx + token_ix)
                all_crops.append(crops)
                out_tokens.append(tokens[start:token_ix])
                out_tokens.append(image_tokens)
                if ix == (n - 1):
                    out_tokens.append(tokens[end:])
                if image_padding_mask:
                    all_crop_masks.append(img_mask)

            input_ids = np.concatenate(out_tokens, 0)
            images = np.concatenate(all_crops, 0)
            image_input_idx = np.concatenate(all_image_idx, 0)
            if image_padding_mask:
                image_masks = np.concatenate(all_crop_masks, 0)
            else:
                image_masks = None

        out = {
            "input_ids": input_ids,
            "images": images,
            "image_input_idx": image_input_idx
        }
        if image_masks is not None:
            out["image_masks"] = image_masks
        return out


MolmoImageProcessor.register_for_auto_class()