deepseek_vl/models/image_processing_vlm.py

# Copyright (c) 2023-2024 DeepSeek.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

from typing import List, Tuple, Union

import numpy as np
import torch
import torchvision
import torchvision.transforms.functional
from PIL import Image
from transformers import AutoImageProcessor, PretrainedConfig
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_utils import to_numpy_array
from transformers.utils import logging

logger = logging.get_logger(__name__)

ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
IMAGENET_MEAN = (0.48145466, 0.4578275, 0.40821073)
IMAGENET_STD = (0.26862954, 0.26130258, 0.27577711)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)


def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result


class VLMImageProcessorConfig(PretrainedConfig):
    model_type = "deepseek_vlm"
    image_size: int
    min_size: int
    image_mean: Union[Tuple[float, float, float], List[float]]
    image_std: Union[Tuple[float, float, float], List[float]]
    rescale_factor: float
    do_normalize: bool

    def __init__(
        self,
        image_size: int,
        min_size: int = 14,
        image_mean: Union[Tuple[float, float, float], List[float]] = (
            0.48145466,
            0.4578275,
            0.40821073,
        ),
        image_std: Union[Tuple[float, float, float], List[float]] = (
            0.26862954,
            0.26130258,
            0.27577711,
        ),
        rescale_factor: float = 1.0 / 255.0,
        do_normalize: bool = True,
        **kwargs,
    ):
        self.image_size = image_size
        self.min_size = min_size
        self.image_mean = image_mean
        self.image_std = image_std
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize

        super().__init__(**kwargs)


class VLMImageProcessor(BaseImageProcessor):
    model_input_names = ["pixel_values"]

    def __init__(
        self,
        image_size: int,
        min_size: int = 14,
        image_mean: Union[Tuple[float, float, float], List[float]] = (
            0.48145466,
            0.4578275,
            0.40821073,
        ),
        image_std: Union[Tuple[float, float, float], List[float]] = (
            0.26862954,
            0.26130258,
            0.27577711,
        ),
        rescale_factor: float = 1.0 / 255.0,
        do_normalize: bool = True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.image_size = image_size
        self.rescale_factor = rescale_factor
        self.image_mean = image_mean
        self.image_std = image_std
        self.min_size = min_size
        self.do_normalize = do_normalize

        if image_mean is None:
            self.background_color = (127, 127, 127)
        else:
            self.background_color = tuple([int(x * 255) for x in image_mean])

    def resize(self, pil_img: Image) -> np.ndarray:
        """

        Args:
            pil_img (PIL.Image): [H, W, 3] in PIL.Image in RGB

        Returns:
            x (np.ndarray): [3, self.image_size, self.image_size]
        """

        width, height = pil_img.size
        max_size = max(width, height)

        size = [
            max(int(height / max_size * self.image_size), self.min_size),
            max(int(width / max_size * self.image_size), self.min_size),
        ]

        if width <= 0 or height <= 0 or size[0] <= 0 or size[1] <= 0:
            print(f"orig size = {pil_img.size}, new size = {size}")
            raise ValueError("Invalid size!")

        pil_img = torchvision.transforms.functional.resize(
            pil_img,
            size,
            interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC,
            antialias=True,
        )

        pil_img = expand2square(pil_img, self.background_color)
        x = to_numpy_array(pil_img)

        # [H, W, 3] -> [3, H, W]
        x = np.transpose(x, (2, 0, 1))

        return x

    def preprocess(self, images, return_tensors: str = "pt", **kwargs) -> BatchFeature:
        # resize and pad to [self.image_size, self.image_size]
        # then convert from [H, W, 3] to [3, H, W]
        images: List[np.ndarray] = [self.resize(image) for image in images]

        # resacle from [0, 255] -> [0, 1]
        images = [
            self.rescale(
                image=image,
                scale=self.rescale_factor,
                input_data_format="channels_first",
            )
            for image in images
        ]

        # normalize
        if self.do_normalize:
            images = [
                self.normalize(
                    image=image,
                    mean=self.image_mean,
                    std=self.image_std,
                    input_data_format="channels_first",
                )
                for image in images
            ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)

    @property
    def default_shape(self):
        return [3, self.image_size, self.image_size]


AutoImageProcessor.register(VLMImageProcessorConfig, VLMImageProcessor)


if __name__ == "__main__":
    image_processor = VLMImageProcessor(
        image_size=1024,
        image_mean=IMAGENET_INCEPTION_MEAN,
        image_std=IMAGENET_INCEPTION_STD,
        do_normalize=True,
    )