image_processing_tvlt.py

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Image processor class for TVLT."""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


logger = logging.get_logger(__name__)


def make_batched(videos) -> List[List[ImageInput]]:
    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)):
        return videos

    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
        videos_dim = np.array(videos[0]).ndim
        if videos_dim == 3:
            return [videos]
        elif videos_dim == 4:
            return videos

    elif is_valid_image(videos):
        videos_dim = np.array(videos).ndim
        if videos_dim == 3:
            return [[videos]]
        elif videos_dim == 4:
            return [videos]
        elif videos_dim == 5:
            return videos

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


class TvltImageProcessor(BaseImageProcessor):
    r"""
    Constructs a TVLT image processor.

    This processor can be used to prepare either videos or images for the model by converting images to 1-frame videos.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
            `do_resize` parameter in the `preprocess` method.
        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
            Size of the output image after resizing. The shortest edge of the image will be resized to
            `size["shortest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by
            `size` in the `preprocess` method.
        patch_size (`List[int]` *optional*, defaults to [16,16]):
            The patch size of image patch embedding.
        num_frames (`int` *optional*, defaults to 8):
            The maximum number of video frames.
        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
            `preprocess` method.
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop`
            parameter in the `preprocess` method.
        crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
            Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the
            `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
            parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to 1/255):
            Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter
            in the `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
    """

    model_input_names = [
        "pixel_values",
        "pixel_mask",
        "pixel_values_mixed",
        "pixel_mask_mixed",
    ]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        patch_size: List[int] = [16, 16],
        num_frames: int = 8,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_center_crop: bool = True,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = IMAGENET_STANDARD_MEAN,
        image_std: Optional[Union[float, List[float]]] = IMAGENET_STANDARD_STD,
        init_mask_generator=False,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 224}
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        self.do_resize = do_resize
        self.size = size
        self.patch_size = patch_size
        self.num_frames = num_frames
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will
                have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the output image will have its
                shortest edge of length `s` while keeping the aspect ratio of the original image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                Resampling filter to use when resiizing the image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        size = get_size_dict(size, default_to_square=False)
        if "shortest_edge" in size:
            output_size = get_resize_output_image_size(image, size["shortest_edge"], default_to_square=False)
        elif "height" in size and "width" in size:
            output_size = (size["height"], size["width"])
        else:
            raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
        return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)

    def center_crop(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `size` along any
        edge, the image is padded with 0's and then center cropped.

        Args:
            image (`np.ndarray`):
                Image to center crop.
            size (`Dict[str, int]`):
                Size of the output image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}")
        return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)

    def rescale(
        self,
        image: np.ndarray,
        scale: Union[int, float],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ):
        """
        Rescale an image by a scale factor. image = image * scale.

        Args:
            image (`np.ndarray`):
                Image to rescale.
            scale (`int` or `float`):
                Scale to apply to the image.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        return rescale(image, scale=scale, data_format=data_format, **kwargs)

    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.normalize
    def normalize(
        self,
        image: np.ndarray,
        mean: Union[float, List[float]],
        std: Union[float, List[float]],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Normalize an image. image = (image - image_mean) / image_std.

        Args:
            image (`np.ndarray`):
                Image to normalize.
            mean (`float` or `List[float]`):
                Image mean to use for normalization.
            std (`float` or `List[float]`):
                Image standard deviation to use for normalization.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

        Returns:
            `np.ndarray`: The normalized image.
        """
        return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)

    def _preprocess_image(
        self,
        image: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
    ) -> np.ndarray:
        """Preprocesses a single image."""
        if do_resize and size is None or resample is None:
            raise ValueError("Size and resample must be specified if do_resize is True.")

        if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

        # All transformations expect numpy arrays.
        image = to_numpy_array(image)

        if do_resize:
            image = self.resize(image=image, size=size, resample=resample)

        if do_center_crop:
            image = self.center_crop(image, size=crop_size)

        if do_rescale:
            image = self.rescale(image=image, scale=rescale_factor)

        if do_normalize:
            image = self.normalize(image=image, mean=image_mean, std=image_std)
        image = to_channel_dimension_format(image, data_format)
        return image

    def preprocess(
        self,
        videos: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        patch_size: List[int] = None,
        num_frames: int = None,
        resample: PILImageResampling = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        is_mixed: bool = False,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an videos or image or batch of videos or images.

        Args:
            videos (`ImageInput`):
                Images or videos to preprocess.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Size of the image after applying resize.
            patch_size (`List[int]` *optional*, defaults to self.patch_size):
                The patch size of image patch embedding.
            num_frames (`int` *optional*, defaults to self.num_frames):
                The maximum number of video frames.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
                has an effect if `do_resize` is set to `True`.
            do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`):
                Whether to centre crop the image.
            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
                Size of the image after applying the centre crop.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation.
            is_mixed (`bool`, *optional*):
                If the input video has negative samples.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                    - Unset: Use the inferred channel dimension format of the input image.

        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
              width).

            - **pixel_mask** -- Pixel masks to be fed to a model, of shape (batch_size, num_pixel_patches).

            - **pixel_values_mixed** -- Pixel values with both postive or negative to be fed to a model, of shape
              (batch_size, num_channels, height, width).

            - **pixel_mask_mixed** -- Pixel masks with both postive or negative to be fed to a model, of shape
              (batch_size, num_pixel_patches).
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        resample = resample if resample is not None else self.resample
        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std

        size = size if size is not None else self.size
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else self.crop_size
        crop_size = get_size_dict(crop_size, param_name="crop_size")
        patch_size = patch_size if patch_size is not None else self.patch_size
        num_frames = num_frames if patch_size is not None else self.num_frames

        if not valid_images(videos):
            raise ValueError(
                "Invalid image or video type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        videos = make_batched(videos)

        # Check number of frames is fewer than maximum frames
        for video in videos:
            if len(video) > self.num_frames:
                raise ValueError(
                    f"number of frames must not be greater than the maximum frames of the model {self.num_frames}."
                )

        max_num_frames = max([len(video) for video in videos])
        num_patches_per_image = (size["shortest_edge"] // patch_size[0]) ** 2
        video_masks = np.array(
            [
                len(video) * num_patches_per_image * [1] + (max_num_frames - len(video)) * num_patches_per_image * [0]
                for video in videos
            ]
        )

        videos = [
            [
                self._preprocess_image(
                    image=img,
                    do_resize=do_resize,
                    size=size,
                    resample=resample,
                    do_center_crop=do_center_crop,
                    crop_size=crop_size,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    data_format=data_format,
                )
                for img in video
            ]
            for video in videos
        ]

        # If videos contain both positive/negative, use mixed key for video-audio matching task
        if is_mixed:
            data = {"pixel_values_mixed": videos, "pixel_mask_mixed": video_masks}
        else:
            data = {"pixel_values": videos, "pixel_mask": video_masks}

        return BatchFeature(data=data, tensor_type=return_tensors)