diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision.py new file mode 100644 index 0000000000000000000000000000000000000000..836a1984a522fdd5e6cf0db15aca94f0bffddc22 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision.py @@ -0,0 +1,786 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# 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 +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for LLaVa-Onevision.""" + +from collections.abc import Iterable +from typing import Optional, Union + +import numpy as np + +from ...image_processing_utils import ( + BaseImageProcessor, + BatchFeature, + get_patch_output_size, + get_size_dict, + select_best_resolution, +) +from ...image_transforms import ( + PaddingMode, + convert_to_rgb, + pad, + resize, + to_channel_dimension_format, +) +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + make_flat_list_of_images, + to_numpy_array, + valid_images, + validate_preprocess_arguments, +) +from ...utils import TensorType, is_vision_available, logging + + +logger = logging.get_logger(__name__) + + +if is_vision_available(): + from PIL import Image + + +# Copied from transformers.models.llava_next.image_processing_llava_next.divide_to_patches +def divide_to_patches(image: np.ndarray, patch_size: int, input_data_format) -> list[np.ndarray]: + """ + Divides an image into patches of a specified size. + + Args: + image (`np.ndarray`): + The input image. + patch_size (`int`): + The size of each patch. + input_data_format (`ChannelDimension` or `str`): + The channel dimension format of the input image. + + Returns: + list: A list of np.ndarray representing the patches. + """ + patches = [] + height, width = get_image_size(image, channel_dim=input_data_format) + for i in range(0, height, patch_size): + for j in range(0, width, patch_size): + if input_data_format == ChannelDimension.LAST: + patch = image[i : i + patch_size, j : j + patch_size] + else: + patch = image[:, i : i + patch_size, j : j + patch_size] + patches.append(patch) + + return patches + + +# Copied from transformers.models.llava_next.image_processing_llava_next.expand_to_square +def expand_to_square(image: np.ndarray, background_color, input_data_format) -> np.ndarray: + """ + Expands an image to a square by adding a background color. + """ + + height, width = get_image_size(image, channel_dim=input_data_format) + if width == height: + return image + elif width > height: + result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color + result[(width - height) // 2 : (width - height) // 2 + height, :] = image + return result + else: + result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color + result[:, (height - width) // 2 : (height - width) // 2 + width] = image + return result + + +class LlavaOnevisionImageProcessor(BaseImageProcessor): + r""" + Constructs a LLaVa-Onevision image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame. + + 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 + `do_resize` in the `preprocess` method. + size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): + Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` + method. + image_grid_pinpoints (`List` *optional*, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`): + A list of possible resolutions to use for processing high resolution images. The best resolution is selected + based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` + method. Not used for processing videos. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): + Resampling filter to use if resizing the image. Can be overridden by `resample` 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 `do_rescale` in + the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` + method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. + image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): + 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 `[0.26862954, 0.26130258, 0.27577711]`): + 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. + Can be overridden by the `image_std` parameter in the `preprocess` method. + do_pad (`bool`, *optional*, defaults to `True`): + Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest + number of patches in the batch. Padding will be applied to the bottom and right with zeros. + do_convert_rgb (`bool`, *optional*, defaults to `True`): + Whether to convert the image to RGB. + """ + + model_input_names = ["pixel_values", "image_sizes", "batch_num_images"] + + def __init__( + self, + do_resize: bool = True, + size: Optional[dict[str, int]] = None, + image_grid_pinpoints: Optional[list] = None, + resample: PILImageResampling = PILImageResampling.BICUBIC, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, list[float]]] = None, + image_std: Optional[Union[float, list[float]]] = None, + do_pad: Optional[bool] = True, + do_convert_rgb: bool = True, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"height": 384, "width": 384} + size = get_size_dict(size, default_to_square=False) + image_grid_pinpoints = ( + image_grid_pinpoints + if image_grid_pinpoints is not None + else [ + [384, 384], + [384, 768], + [384, 1152], + [384, 1536], + [384, 1920], + [384, 2304], + [768, 384], + [768, 768], + [768, 1152], + [768, 1536], + [768, 1920], + [768, 2304], + [1152, 384], + [1152, 768], + [1152, 1152], + [1152, 1536], + [1152, 1920], + [1152, 2304], + [1536, 384], + [1536, 768], + [1536, 1152], + [1536, 1536], + [1536, 1920], + [1536, 2304], + [1920, 384], + [1920, 768], + [1920, 1152], + [1920, 1536], + [1920, 1920], + [1920, 2304], + [2304, 384], + [2304, 768], + [2304, 1152], + [2304, 1536], + [2304, 1920], + [2304, 2304], + ] + ) + + self.do_resize = do_resize + self.size = size + self.image_grid_pinpoints = image_grid_pinpoints + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + 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 + self.do_pad = do_pad + self.do_convert_rgb = do_convert_rgb + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.pad + def pad( + self, + image: np.ndarray, + padding: Union[int, tuple[int, int], Iterable[tuple[int, int]]], + mode: PaddingMode = PaddingMode.CONSTANT, + constant_values: Union[float, Iterable[float]] = 0.0, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """ + Pads the `image` with the specified `padding` and `mode`. Padding can be in the (`height`, `width`) + dimension of in the (`num_patches`) dimension. In the second case an iterable if tuples is expected + as input. + + Args: + image (`np.ndarray`): + The image to pad. + padding (`int` or `tuple[int, int]` or `Iterable[tuple[int, int]]`): + Padding to apply to the edges of the height, width axes. Can be one of three formats: + - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis. + - `((before, after),)` yields same before and after pad for height and width. + - `(pad,)` or int is a shortcut for before = after = pad width for all axes. + mode (`PaddingMode`): + The padding mode to use. Can be one of: + - `"constant"`: pads with a constant value. + - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the + vector along each axis. + - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis. + - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array. + constant_values (`float` or `Iterable[float]`, *optional*): + The value to use for the padding if `mode` is `"constant"`. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the output image. 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. + If unset, will use same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the input image. 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. + If unset, will use the inferred format of the input image. + + Returns: + `np.ndarray`: The padded image. + + """ + + # call the general `pad` if padding on `height/width`, otherwise it's the `num_patched` dim + if isinstance(padding, int) or len(padding) != 4: + return pad(image, padding, mode, constant_values, data_format, input_data_format) + + if input_data_format is None: + input_data_format = infer_channel_dimension_format(image) + if mode == PaddingMode.CONSTANT: + image = np.pad(image, padding, mode="constant", constant_values=constant_values) + elif mode == PaddingMode.REFLECT: + image = np.pad(image, padding, mode="reflect") + elif mode == PaddingMode.REPLICATE: + image = np.pad(image, padding, mode="edge") + elif mode == PaddingMode.SYMMETRIC: + image = np.pad(image, padding, mode="symmetric") + else: + raise ValueError(f"Invalid padding mode: {mode}") + image = ( + to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image + ) + return image + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._resize_for_patching + def _resize_for_patching( + self, image: np.ndarray, target_resolution: tuple, resample, input_data_format: ChannelDimension + ) -> np.ndarray: + """ + Resizes an image to a target resolution while maintaining aspect ratio. + + Args: + image (np.ndarray): + The input image. + target_resolution (tuple): + The target resolution (height, width) of the image. + resample (`PILImageResampling`): + Resampling filter to use if resizing the image. + input_data_format (`ChannelDimension` or `str`): + The channel dimension format of the input image. + + Returns: + np.ndarray: The resized and padded image. + """ + new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format) + + # Resize the image + resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format) + + return resized_image + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._get_padding_size + def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple): + original_height, original_width = original_resolution + target_height, target_width = target_resolution + paste_x, r_x = divmod(target_width - original_width, 2) + paste_y, r_y = divmod(target_height - original_height, 2) + return (paste_y, paste_y + r_y), (paste_x, paste_x + r_x) + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_patching + def _pad_for_patching( + self, image: np.ndarray, target_resolution: tuple, input_data_format: ChannelDimension + ) -> np.ndarray: + """ + Pad an image to a target resolution while maintaining aspect ratio. + """ + new_resolution = get_patch_output_size(image, target_resolution, input_data_format) + padding = self._get_padding_size(new_resolution, target_resolution) + + padded_image = self.pad(image, padding=padding) + + return padded_image + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.get_image_patches + def get_image_patches( + self, + image: np.ndarray, + grid_pinpoints, + size: tuple, + patch_size: int, + resample: PILImageResampling, + data_format: ChannelDimension, + input_data_format: ChannelDimension, + ) -> list[np.ndarray]: + """ + Process an image with variable resolutions by dividing it into patches. + + Args: + image (np.ndarray): + The input image to be processed. + grid_pinpoints (List): + A string representation of a list of possible resolutions. + size (`tuple`): + Size to resize the original image to. + patch_size (`int`): + Size of the patches to divide the image into. + resample (`PILImageResampling`): + Resampling filter to use if resizing the image. + data_format (`ChannelDimension` or `str`): + The channel dimension format for the output image. + input_data_format (`ChannelDimension` or `str`): + The channel dimension format of the input image. + + Returns: + list[np.ndarray]: A list of NumPy arrays containing the processed image patches. + """ + if not isinstance(grid_pinpoints, list): + raise TypeError("grid_pinpoints must be a list of possible resolutions.") + + possible_resolutions = grid_pinpoints + + image_size = get_image_size(image, channel_dim=input_data_format) + best_resolution = select_best_resolution(image_size, possible_resolutions) + resized_image = self._resize_for_patching( + image, best_resolution, resample=resample, input_data_format=input_data_format + ) + padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format) + + patches = divide_to_patches(padded_image, patch_size=patch_size, input_data_format=input_data_format) + + # make sure that all patches are in the input data format + patches = [ + to_channel_dimension_format(patch, channel_dim=data_format, input_channel_dim=input_data_format) + for patch in patches + ] + + resized_original_image = resize( + image, + size=size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + ) + + image_patches = [resized_original_image] + patches + + return image_patches + + # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_batching + def _pad_for_batching( + self, + pixel_values: list[np.ndarray], + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ): + """ + Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches. + + Args: + pixel_values (`list[np.ndarray]`): + An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`) + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the output image. 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. + If unset, will use same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the input image. 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. + If unset, will use the inferred format of the input image. + + Returns: + list[`np.ndarray`]: The padded images. + """ + max_patch = max(len(x) for x in pixel_values) + pixel_values = [ + self.pad( + image, + padding=((0, max_patch - image.shape[0]), (0, 0), (0, 0), (0, 0)), + data_format=data_format, + input_data_format=input_data_format, + ) + for image in pixel_values + ] + + return pixel_values + + # Copied from transformers.models.llava.image_processing_llava.LlavaImageProcessor.pad_to_square + def pad_to_square( + self, + image: np.ndarray, + background_color: Union[int, tuple[int, int, int]] = 0, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """ + Pads an image to a square based on the longest edge. + + Args: + image (`np.ndarray`): + The image to pad. + background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): + The color to use for the padding. Can be an integer for single channel or a + tuple of integers representing for multi-channel images. If passed as integer + in multi-channel mode, it will default to `0` in subsequent channels. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the output image. 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. + If unset, will use same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the input image. 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. + If unset, will use the inferred format of the input image. + + Returns: + `np.ndarray`: The padded image. + """ + height, width = get_image_size(image, input_data_format) + num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1] + + if height == width: + image = ( + to_channel_dimension_format(image, data_format, input_data_format) + if data_format is not None + else image + ) + return image + + max_dim = max(height, width) + + # Ensure background_color is the correct shape + if isinstance(background_color, int): + background_color = [background_color] + elif len(background_color) != num_channels: + raise ValueError( + f"background_color must have no more than {num_channels} elements to match the number of channels" + ) + + if input_data_format == ChannelDimension.FIRST: + result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype) + for i, color in enumerate(background_color): + result[i, :, :] = color + if width > height: + start = (max_dim - height) // 2 + result[:, start : start + height, :] = image + else: + start = (max_dim - width) // 2 + result[:, :, start : start + width] = image + else: + result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype) + for i, color in enumerate(background_color): + result[:, :, i] = color + if width > height: + start = (max_dim - height) // 2 + result[start : start + height, :, :] = image + else: + start = (max_dim - width) // 2 + result[:, start : start + width, :] = image + + image = ( + to_channel_dimension_format(result, data_format, input_data_format) if data_format is not None else result + ) + return image + + def _preprocess( + self, + images: ImageInput, + do_resize: Optional[bool] = None, + size: Optional[dict[str, int]] = None, + resample: Optional[PILImageResampling] = None, + do_rescale: Optional[bool] = None, + rescale_factor: Optional[float] = None, + do_normalize: Optional[bool] = None, + image_mean: Optional[Union[float, list[float]]] = None, + image_std: Optional[Union[float, list[float]]] = None, + do_convert_rgb: Optional[bool] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> Image.Image: + """ + Args: + images (`ImageInput`): + Batch of frames (one video) to preprocess. Expects a batch of frames with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + 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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. + resample (`int`, *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_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image. + 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 to use for normalization. Only has an effect if `do_normalize` is set to `True`. + image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to + `True`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. 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. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. 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. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + if do_resize: + images = [ + resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + for image in images + ] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + return images + + def preprocess( + self, + images: ImageInput, + do_resize: Optional[bool] = None, + size: Optional[dict[str, int]] = None, + image_grid_pinpoints: Optional[list] = None, + resample: Optional[PILImageResampling] = None, + do_rescale: Optional[bool] = None, + rescale_factor: Optional[float] = None, + do_normalize: Optional[bool] = None, + image_mean: Optional[Union[float, list[float]]] = None, + image_std: Optional[Union[float, list[float]]] = None, + do_pad: Optional[bool] = None, + do_convert_rgb: Optional[bool] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ): + """ + Args: + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + 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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. + image_grid_pinpoints (`List` *optional*, defaults to `self.image_grid_pinpoints`): + A list of possible resolutions to use for processing high resolution images. The best resolution is + selected based on the original size of the image. + resample (`int`, *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_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image. + 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 to use for normalization. Only has an effect if `do_normalize` is set to `True`. + image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to + `True`. + do_pad (`bool`, *optional*, defaults to `self.do_pad`): + Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest + number of patches in the batch. Padding will be applied to the bottom and right with zeros. + do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): + Whether to convert the image to RGB. + 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: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. 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. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + + """ + do_resize = do_resize if do_resize is not None else self.do_resize + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints + resample = resample if resample is not None else self.resample + 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 + do_pad = do_pad if do_pad is not None else self.do_pad + do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb + + if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)): + # if the first element is a list, we assume that all elements are lists + images = [x for x in images if x] # handle text-only case + batch_num_images = [len(x) for x in images] + elif isinstance(images, (tuple, list)): + # treat this as a single-image case for backward compatibility + batch_num_images = [1] * len(images) + else: + batch_num_images = [1] + # only single image patching is supported + need_patching = [n == 1 for n in batch_num_images for _ in range(n)] + + images = self.fetch_images(images) + images = make_flat_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_resize=do_resize, + size=size, + resample=resample, + ) + + if do_convert_rgb: + images = [convert_to_rgb(image) for image in images] + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if do_rescale and is_scaled_image(images[0]): + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + size_tuple = ( + (size["height"], size["width"]) + if "height" in size and "width" in size + else (size["shortest_edge"], size["shortest_edge"]) + ) + + processed_images = [] + image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images] + for i, image in enumerate(images): + if need_patching[i]: + # convert image into a list of patches + # we intentionally use the same data format as the input data format + image_patches = self.get_image_patches( + image, + image_grid_pinpoints, + size=size_tuple, + patch_size=size_tuple[0], + resample=resample, + data_format=input_data_format, + input_data_format=input_data_format, + ) + else: + padded_image = self.pad_to_square( + image=image, + background_color=tuple(int(x * 255) for x in self.image_mean), + input_data_format=input_data_format, + ) + image_patches = [padded_image] + + # preprocess patches + pixel_values = self._preprocess( + image_patches, + do_resize=do_resize, + size=size_tuple, + resample=resample, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + input_data_format=input_data_format, + ) + pixel_values = np.array(pixel_values) + processed_images.append(pixel_values) + + if do_pad: + processed_images = self._pad_for_batching(processed_images) + + return BatchFeature( + data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images}, + tensor_type=return_tensors, + ) + + +__all__ = ["LlavaOnevisionImageProcessor"] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..11872cb67bf3a8806c08c08ab698a99cbbfaa006 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py @@ -0,0 +1,346 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/llava_onevision/modular_llava_onevision.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_llava_onevision.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# coding=utf-8 +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# 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 +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional, Union + +import torch +from torchvision.transforms.v2 import functional as F + +from ...image_processing_utils import BatchFeature, get_patch_output_size, select_best_resolution +from ...image_processing_utils_fast import ( + BaseImageProcessorFast, + DefaultFastImageProcessorKwargs, + divide_to_patches, + group_images_by_shape, + reorder_images, +) +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...processing_utils import Unpack +from ...utils import TensorType, auto_docstring + + +class LlavaOnevisionFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): + """ + image_grid_pinpoints (`list[list[int]]`, *optional*): + A list of possible resolutions to use for processing high resolution images. The best resolution is selected + based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` + method. + """ + + image_grid_pinpoints: Optional[list[list[int]]] + + +@auto_docstring +class LlavaOnevisionImageProcessorFast(BaseImageProcessorFast): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 384, "width": 384} + default_to_square = False + crop_size = None + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_pad = True + image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304]] # fmt: skip + valid_kwargs = LlavaOnevisionFastImageProcessorKwargs + model_input_names = ["pixel_values", "image_sizes", "batch_num_images"] + + def __init__(self, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]) -> BatchFeature: + if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)): + # if the first element is a list, we assume that all elements are lists + batch_num_images = [len(x) for x in images] + elif isinstance(images, (tuple, list)): + # treat this as a single-image case for backward compatibility + batch_num_images = [1] * len(images) + else: + batch_num_images = [1] + kwargs["batch_num_images"] = batch_num_images + return super().preprocess(images, **kwargs) + + def _resize_for_patching( + self, + image: "torch.Tensor", + target_resolution: tuple, + interpolation: "F.InterpolationMode", + input_data_format: ChannelDimension, + ) -> "torch.Tensor": + """ + Resizes an image to a target resolution while maintaining aspect ratio. + + Args: + image ("torch.Tensor"): + The input image. + target_resolution (tuple): + The target resolution (height, width) of the image. + interpolation (`InterpolationMode`): + Resampling filter to use if resizing the image. + input_data_format (`ChannelDimension` or `str`): + The channel dimension format of the input image. + + Returns: + "torch.Tensor": The resized and padded image. + """ + new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format) + + # Resize the image + resized_image = self.resize( + image=image, + size=SizeDict(height=new_height, width=new_width), + interpolation=interpolation, + ) + + return resized_image + + def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple): + original_height, original_width = original_resolution + target_height, target_width = target_resolution + paste_x, r_x = divmod(target_width - original_width, 2) + paste_y, r_y = divmod(target_height - original_height, 2) + return [paste_x, paste_y, paste_x + r_x, paste_y + r_y] + + def _pad_for_patching( + self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension + ) -> "torch.Tensor": + """ + Pad an image to a target resolution while maintaining aspect ratio. + """ + new_resolution = get_patch_output_size(image, target_resolution, input_data_format) + padding = self._get_padding_size(new_resolution, target_resolution) + + padded_image = F.pad(image, padding=padding) + + return padded_image + + def _get_image_patches( + self, + image: "torch.Tensor", + grid_pinpoints, + size: tuple, + patch_size: int, + interpolation: "F.InterpolationMode", + ) -> list["torch.Tensor"]: + """ + Process an image with variable resolutions by dividing it into patches. + + Args: + image ("torch.Tensor"): + The input image to be processed. + grid_pinpoints (List): + A string representation of a list of possible resolutions. + size (`tuple`): + Size to resize the original image to. + patch_size (`int`): + Size of the patches to divide the image into. + interpolation (`"InterpolationMode"`): + Resampling filter to use if resizing the image. + + Returns: + list["torch.Tensor"]: A list of NumPy arrays containing the processed image patches. + """ + if not isinstance(grid_pinpoints, list): + raise TypeError("grid_pinpoints must be a list of possible resolutions.") + + possible_resolutions = grid_pinpoints + + image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST) + best_resolution = select_best_resolution(image_size, possible_resolutions) + resized_image = self._resize_for_patching( + image, best_resolution, interpolation=interpolation, input_data_format=ChannelDimension.FIRST + ) + padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=ChannelDimension.FIRST) + patches = divide_to_patches(padded_image, patch_size=patch_size) + resized_original_image = F.resize(image, size=size, interpolation=interpolation) + + image_patches = [resized_original_image] + patches + + return image_patches + + def _pad_for_batching( + self, + pixel_values: list["torch.Tensor"], + ) -> list["torch.Tensor"]: + """ + Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches. + + Args: + pixel_values (`list[torch.Tensor]`): + An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`) + + Returns: + list[`torch.Tensor`]: The padded images. + """ + max_patch = max(len(x) for x in pixel_values) + pixel_values = [ + torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]]) + for image in pixel_values + ] + + return pixel_values + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + image_grid_pinpoints: list[list[int]], + interpolation: Optional["F.InterpolationMode"], + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: Optional[Union[float, list[float]]], + image_std: Optional[Union[float, list[float]]], + do_pad: bool, + batch_num_images: list[int], + disable_grouping: Optional[bool], + return_tensors: Optional[Union[str, TensorType]], + **kwargs, + ) -> BatchFeature: + processed_images = [] + image_sizes = [] + + # only single image patching is supported + need_patching = [n == 1 for n in batch_num_images for _ in range(n)] + + # Determine the size tuple + if size and size.height and size.width: + size_tuple = (size.height, size.width) + else: + size_tuple = (size.shortest_edge, size.shortest_edge) + + # Determine the patch size + if crop_size and crop_size.height: + patch_size = crop_size.height + elif size and size.height: + patch_size = size.height + else: + patch_size = size.shortest_edge + + for i, image in enumerate(images): + if need_patching[i]: + image_patches = self._get_image_patches( + image, + image_grid_pinpoints, + size=size_tuple, + patch_size=patch_size, + interpolation=interpolation, + ) + else: + padded_image = self.pad_to_square( + images=image, background_color=tuple(int(x * 255) for x in self.image_mean) + ) + image_patches = [padded_image] + + # Group images by size for batched processing + processed_image_patches_grouped = {} + grouped_image_patches, grouped_image_patches_index = group_images_by_shape( + image_patches, disable_grouping=disable_grouping + ) + for shape, stacked_image_patches in grouped_image_patches.items(): + if do_resize: + stacked_image_patches = self.resize( + image=stacked_image_patches, + size=size, + interpolation=interpolation, + ) + if do_center_crop: + stacked_image_patches = self.center_crop(stacked_image_patches, crop_size) + # Fused rescale and normalize + stacked_image_patches = self.rescale_and_normalize( + stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_image_patches_grouped[shape] = stacked_image_patches + processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index) + processed_image_patches = ( + torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches + ) + processed_images.append(processed_image_patches) + image_sizes.append(get_image_size(image, ChannelDimension.FIRST)) + + if do_pad: + processed_images = self._pad_for_batching(processed_images) + processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images + return BatchFeature( + data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images}, + tensor_type=return_tensors, + ) + + # Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square + def pad_to_square( + self, + images: "torch.Tensor", + background_color: Union[int, tuple[int, int, int]] = 0, + ) -> "torch.Tensor": + """ + Pads an image to a square based on the longest edge. + + Args: + images (`np.ndarray`): + The images to pad. + background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): + The color to use for the padding. Can be an integer for single channel or a + tuple of integers representing for multi-channel images. If passed as integer + in multi-channel mode, it will default to `0` in subsequent channels. + Returns: + `torch.Tensor`: The padded images. + """ + height, width = get_image_size(images, ChannelDimension.FIRST) + + if height == width: + return images + + num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0] + if isinstance(background_color, int): + background_color = [background_color] + [0] * (num_channels - 1) + elif len(background_color) != num_channels: + raise ValueError( + f"background_color must have no more than {num_channels} elements to match the number of channels" + ) + + max_dim = max(height, width) + paste_x_left = (max_dim - width) // 2 + paste_y_left = (max_dim - height) // 2 + paste_x_right = max_dim - width - paste_x_left + paste_y_right = max_dim - height - paste_y_left + padded_images = F.pad( + images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color + ) + + return padded_images + + +__all__ = ["LlavaOnevisionImageProcessorFast"] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modeling_llava_onevision.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modeling_llava_onevision.py new file mode 100644 index 0000000000000000000000000000000000000000..727655374574c62ef2c351358dd28cd44b2ed32a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modeling_llava_onevision.py @@ -0,0 +1,960 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/llava_onevision/modular_llava_onevision.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_llava_onevision.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# coding=utf-8 +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# 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 +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import Optional, Union + +import numpy as np +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...image_processing_utils import select_best_resolution +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ..auto import AutoModel +from .configuration_llava_onevision import LlavaOnevisionConfig + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +class LlavaOnevisionModelOutputWithPast(BaseModelOutputWithPast): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`. + video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + image_hidden_states: Optional[torch.FloatTensor] = None + + video_hidden_states: Optional[torch.FloatTensor] = None + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for LlavaOnevision causal language model (or autoregressive) outputs. + """ +) +class LlavaOnevisionCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`. + video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + loss: Optional[torch.FloatTensor] = None + logits: Optional[torch.FloatTensor] = None + past_key_values: Optional[Cache] = None + hidden_states: Optional[tuple[torch.FloatTensor]] = None + attentions: Optional[tuple[torch.FloatTensor]] = None + image_hidden_states: Optional[torch.FloatTensor] = None + + video_hidden_states: Optional[torch.FloatTensor] = None + + +@auto_docstring +class LlavaOnevisionPreTrainedModel(PreTrainedModel): + config: LlavaOnevisionConfig + base_model_prefix = "" + supports_gradient_checkpointing = True + _no_split_modules = ["LlamaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + + _supports_flash_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_flex_attn = True + _supports_attention_backend = True + + def _init_weights(self, module): + std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range) + + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, LlavaOnevisionModel): + embed_std = 1 / math.sqrt(self.config.text_config.hidden_size) + module.image_newline.data.normal_(mean=0.0, std=embed_std) + + +class LlavaOnevisionMultiModalProjector(nn.Module): + def __init__(self, config: LlavaOnevisionConfig): + super().__init__() + # We have hidden_size * the number of vision feature layers + num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer) + self.linear_1 = nn.Linear( + config.vision_config.hidden_size * num_feature_layers, + config.text_config.hidden_size, + bias=config.multimodal_projector_bias, + ) + self.act = ACT2FN[config.projector_hidden_act] + self.linear_2 = nn.Linear( + config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias + ) + + def forward(self, image_features): + hidden_states = self.linear_1(image_features) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): + """ + Calculate the shape of the image patch grid after the preprocessing for images of any resolution. + + Args: + image_size (`tuple`): + The size of the input image in the format (width, height). + grid_pinpoints (`List`): + A list containing possible resolutions. Each item in the list should be a tuple or list + of the form `(height, width)`. + patch_size (`int`): + The size of each image patch. + + Returns: + tuple: The shape of the image patch grid in the format (width, height). + """ + if not isinstance(grid_pinpoints, list): + raise TypeError("grid_pinpoints should be a list of tuples or lists") + + # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate + if not isinstance(image_size, (list, tuple)): + if not isinstance(image_size, (torch.Tensor, np.ndarray)): + raise TypeError( + f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor" + ) + image_size = image_size.tolist() + + height, width = select_best_resolution(image_size, grid_pinpoints) + return height // patch_size, width // patch_size + + +def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int): + """ + Calculate the number of patches after the preprocessing for images of any resolution. + + Args: + image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`): + The size of the input image in the format (height, width). ? + grid_pinpoints (`List`): + A list containing possible resolutions. Each item in the list should be a tuple or list + of the form `(height, width)`. + patch_size (`int`): + The size of each image patch. + + Returns: + int: the number of patches + """ + if not isinstance(grid_pinpoints, list): + raise TypeError("grid_pinpoints should be a list of tuples or lists") + + # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate + if not isinstance(image_size, (list, tuple)): + if not isinstance(image_size, (torch.Tensor, np.ndarray)): + raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}") + image_size = image_size.tolist() + + best_resolution = select_best_resolution(image_size, grid_pinpoints) + height, width = best_resolution + num_patches = 0 + # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1 + for i in range(0, height, patch_size): + for j in range(0, width, patch_size): + num_patches += 1 + # add the base patch + num_patches += 1 + return num_patches + + +def unpad_image(tensor, original_size): + """ + Unpads a PyTorch tensor of a padded and resized image. + + Args: + tensor (`torch.Tensor`): + The image tensor, assumed to be of shape (num_channels, height, width). + original_size (`tuple`): + The original size of the image (height, width). + + Returns: + `torch.Tensor`: The unpadded image tensor. + """ + if not isinstance(original_size, (list, tuple)): + if not isinstance(original_size, (torch.Tensor, np.ndarray)): + raise TypeError( + f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor" + ) + original_size = original_size.tolist() + original_height, original_width = original_size + current_height, current_width = tensor.shape[1:] + + original_aspect_ratio = original_width / original_height + current_aspect_ratio = current_width / current_height + + if original_aspect_ratio > current_aspect_ratio: + scale_factor = current_width / original_width + new_height = int(round(original_height * scale_factor, 7)) + padding = (current_height - new_height) // 2 + unpadded_tensor = tensor[:, padding : current_height - padding, :] + else: + scale_factor = current_height / original_height + new_width = int(round(original_width * scale_factor, 7)) + padding = (current_width - new_width) // 2 + unpadded_tensor = tensor[:, :, padding : current_width - padding] + + return unpadded_tensor + + +@auto_docstring( + custom_intro=""" + The Llava-Next model which consists of a vision backbone and a language model without language modeling head. + """ +) +class LlavaOnevisionModel(LlavaOnevisionPreTrainedModel): + _checkpoint_conversion_mapping = {"language_model.model": "language_model"} + + def __init__(self, config): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config.vision_config) + + self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config) + embed_std = 1 / math.sqrt(config.text_config.hidden_size) + self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std) + + self.vocab_size = config.text_config.vocab_size + self.language_model = AutoModel.from_config(config.text_config) + self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def set_decoder(self, decoder): + self.language_model = decoder + + def get_decoder(self): + return self.language_model + + def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"): + """ + Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. + + Args: + image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) + List of image feature tensor, each contains all the visual feature of all patches. + image_sizes (`torch.Tensor` of shape `(num_images, 2)`) + Actual image size of each images (H, W). + image_newline (`torch.Tensor` of shape `(embed_dim)`) + New line embedding vector. + vision_aspect_ratio (`str`, *optional*, "anyres_max_9"): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + Returns: + image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) + feature_lens (`list[int]`) + token length of each image in image_features + """ + new_image_features = [] + feature_lens = [] + for image_idx, image_feature in enumerate(image_features): + if image_feature.shape[0] > 1: + base_image_feature = image_feature[0] + image_feature = image_feature[1:] + height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size + if height * width != base_image_feature.shape[0]: + raise ValueError("The number of patches is not consistent with the image size.") + num_patch_height, num_patch_width = get_anyres_image_grid_shape( + image_sizes[image_idx], + self.config.image_grid_pinpoints, + self.config.vision_config.image_size, + ) + image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) + image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() + image_feature = image_feature.flatten(1, 2).flatten(2, 3) + image_feature = unpad_image(image_feature, image_sizes[image_idx]) + max_num_patches = int(vision_aspect_ratio.strip("anyres_max_")) + channels, curr_height, curr_width = image_feature.shape + ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2)) + if ratio > 1.1: + image_feature = image_feature[None] + image_feature = nn.functional.interpolate( + image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear" + )[0] + if image_newline is not None: + image_feature = torch.cat( + ( + image_feature, + image_newline[:, None, None] + .expand(*image_feature.shape[:-1], 1) + .to(image_feature.device, image_feature.dtype), + ), + dim=-1, + ) + image_feature = image_feature.flatten(1, 2).transpose(0, 1) + image_feature = torch.cat((base_image_feature, image_feature), dim=0) + else: + image_feature = image_feature[0] + if image_newline is not None: + image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) + new_image_features.append(image_feature) + feature_lens.append(image_feature.size(0)) + feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device) + return new_image_features, feature_lens + + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_sizes: torch.Tensor, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + ): + """ + Obtains image last hidden states from the vision tower and apply multimodal projection. + + Args: + pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) + The tensors corresponding to the input images. + image_sizes (`torch.Tensor` of shape `(num_images, 2)`) + Actual image size of each images (H, W). + vision_feature_layer (`Union[int, list[int]]`): + The index of the layer to select the vision feature. If multiple indices are provided, + the vision feature of the corresponding indices will be concatenated to form the + vision features. + vision_feature_select_strategy (`str`): + The feature selection strategy used to select the vision feature from the vision backbone. + Can be one of `"default"` or `"full"` + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + Returns: + image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches + and are of shape `(num_patches, image_length, embed_dim)`). + """ + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + # ! infer image_num_patches from image_sizes + if batch_num_images is None: + # treat this as a single-image case for backward compatibility + need_patching = [True] * len(image_sizes) + else: + need_patching = [n == 1 for n in batch_num_images for _ in range(n)] + image_num_patches = [ + image_size_to_num_patches( + image_size=imsize, + grid_pinpoints=self.config.image_grid_pinpoints, + patch_size=self.config.vision_config.image_size, + ) + if should_patch + else 1 + for imsize, should_patch in zip(image_sizes, need_patching) + ] + if pixel_values.dim() == 5: + # stacked if input is (batch_size, num_patches, num_channels, height, width) + _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)] + pixel_values = torch.cat(_pixel_values_list, dim=0) + elif pixel_values.dim() != 4: + # otherwise has to be stacked from list of (num_patches, num_channels, height, width) + raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions") + + image_features = self.vision_tower(pixel_values, output_hidden_states=True) + # If we have one vision feature layer, return the corresponding hidden states, + # otherwise, select the hidden states of each feature layer and concatenate them + if isinstance(vision_feature_layer, int): + selected_image_feature = image_features.hidden_states[vision_feature_layer] + else: + hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] + selected_image_feature = torch.cat(hs_pool, dim=-1) + + if vision_feature_select_strategy == "default": + selected_image_feature = selected_image_feature[:, 1:] + image_features = self.multi_modal_projector(selected_image_feature) + image_features = torch.split(image_features, image_num_patches, dim=0) + + image_features, feature_lens = self.pack_image_features( + image_features, + image_sizes, + image_newline=self.image_newline, + vision_aspect_ratio=vision_aspect_ratio, + ) + return image_features + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: Optional[torch.FloatTensor] = None, + video_features: Optional[torch.FloatTensor] = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel(): + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}" + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel(): + raise ValueError( + f"Videos features and image tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}" + ) + + return special_image_mask, special_video_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_sizes_videos: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Union[tuple, LlavaOnevisionModelOutputWithPast]: + r""" + image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): + The sizes of the videos in the batch, being (height, width) for each frame in the video. + vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + # Images are processed with Anyres + if pixel_values is not None: + image_features = self.get_image_features( + pixel_values, + image_sizes, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + batch_num_images=batch_num_images, + ) + image_features = torch.cat(image_features, dim=0) + image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) + special_image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + # Video are simply embedded and further pooled to decrease seq len + if pixel_values_videos is not None: + video_features = self.get_video_features( + pixel_values_videos, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + ) + image_newline = ( + self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device) + ) + video_features = torch.cat((video_features, image_newline), dim=1) + video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype) + _, special_video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + cache_position=cache_position, + **kwargs, + ) + + return LlavaOnevisionModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + video_hidden_states=video_features if pixel_values_videos is not None else None, + ) + + def get_video_features( + self, + pixel_values: torch.FloatTensor, + vision_feature_layer: Union[int, list[int]], + vision_feature_select_strategy: str, + ): + """ + Obtains video last hidden states from the vision tower, apply multimodal projection and pooling. + + Args: + pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`) + The tensors corresponding to the input video. + vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`): + The index of the layer to select the vision feature. If multiple indices are provided, + the vision feature of the corresponding indices will be concatenated to form the + vision features. + vision_feature_select_strategy (`str`): + The feature selection strategy used to select the vision feature from the vision backbone. + Can be one of `"default"` or `"full"` + Returns: + video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches + and are of shape `(num_videos, video_length, embed_dim)`). + """ + batch_size, frames, channels, height, width = pixel_values.shape + pixel_values = pixel_values.view(batch_size * frames, channels, height, width) + video_features = self.vision_tower(pixel_values, output_hidden_states=True) + + # If we have one vision feature layer, return the corresponding hidden states, + # otherwise, select the hidden states of each feature layer and concatenate them + if isinstance(vision_feature_layer, int): + selected_video_feature = video_features.hidden_states[vision_feature_layer] + else: + hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] + selected_video_feature = torch.cat(hs_pool, dim=-1) + + if vision_feature_select_strategy == "default": + selected_video_feature = selected_video_feature[:, 1:] + video_features = self.multi_modal_projector(selected_video_feature) + + video_features = self.apply_pooling(video_features) + video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1) + + return video_features + + def apply_pooling(self, image_features): + height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size + batch_frames, seq_len, dim = image_features.shape + image_features = image_features.view(batch_frames, height, width, -1) + image_features = image_features.permute(0, 3, 1, 2).contiguous() + + height, width = image_features.shape[2:] + scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)] + image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear") + + image_features = image_features.permute(0, 2, 3, 1) + image_features = image_features.view(batch_frames, -1, dim) + return image_features + + +@auto_docstring( + custom_intro=""" + The LLAVA-NeXT model which consists of a vision backbone and a language model. + """ +) +class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin): + _checkpoint_conversion_mapping = { + "^language_model.model": "model.language_model", + "^vision_tower": "model.vision_tower", + "^multi_modal_projector": "model.multi_modal_projector", + "^image_newline": "model.image_newline", + "^language_model.lm_head": "lm_head", + } + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: LlavaOnevisionConfig): + super().__init__(config) + self.model = LlavaOnevisionModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def get_output_embeddings(self) -> nn.Module: + return self.lm_head + + def set_decoder(self, decoder): + self.model.set_decoder(decoder) + + def get_decoder(self): + return self.model.get_decoder() + + def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None): + return self.model.pack_image_features( + image_features=image_features, + image_sizes=image_sizes, + vision_feature_select_strategy=vision_feature_select_strategy, + image_newline=image_newline, + ) + + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_sizes: torch.Tensor, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + ): + return self.model.get_image_features( + pixel_values=pixel_values, + image_sizes=image_sizes, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + ) + + # Make modules available through conditional class for BC + @property + def language_model(self): + return self.model.language_model + + @property + def vision_tower(self): + return self.model.vision_tower + + @property + def multi_modal_projector(self): + return self.model.multi_modal_projector + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_sizes_videos: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> Union[tuple, LlavaOnevisionCausalLMOutputWithPast]: + r""" + image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): + The sizes of the videos in the batch, being (height, width) for each frame in the video. + vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import requests + >>> import torch + >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration + + >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", dtype="float16", device_map="cuda:0") + >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") + + >>> conversation = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "text", "text": "What is shown in this image?"}, + ... {"type": "image"}, + ... ], + ... }, + ... ] + >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) + + >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> raw_image = Image.open(requests.get(image_file, stream=True).raw) + >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16) + + >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False) + >>> processor.batch_decode(output, skip_special_tokens=True)[0] + "user\n\nWhat is shown in this image?\nassistant\ncat" + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_sizes=image_sizes, + image_sizes_videos=image_sizes_videos, + vision_aspect_ratio=vision_aspect_ratio, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + batch_num_images=batch_num_images, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + cache_position=cache_position, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return LlavaOnevisionCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + video_hidden_states=outputs.video_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + image_sizes=None, + pixel_values_videos=None, + image_sizes_videos=None, + attention_mask=None, + cache_position=None, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + cache_position=cache_position, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + if cache_position[0] == 0: + # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore + # Otherwise we need pixel values to be passed to model + model_inputs["pixel_values"] = pixel_values + model_inputs["image_sizes"] = image_sizes + model_inputs["pixel_values_videos"] = pixel_values_videos + model_inputs["image_sizes_videos"] = image_sizes_videos + + return model_inputs + + @staticmethod + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + cache_position: torch.Tensor, + batch_size: int, + **kwargs, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape + `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, + to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device + ) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( + causal_mask.device + ) + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + def get_video_features( + self, + pixel_values: torch.FloatTensor, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + ): + return self.model.get_video_features( + pixel_values=pixel_values, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + ) + + +__all__ = ["LlavaOnevisionModel", "LlavaOnevisionForConditionalGeneration", "LlavaOnevisionPreTrainedModel"] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modular_llava_onevision.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modular_llava_onevision.py new file mode 100644 index 0000000000000000000000000000000000000000..b4f64dee8e041096dc7b648f23341e3eb5c11a1c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/modular_llava_onevision.py @@ -0,0 +1,744 @@ +# coding=utf-8 +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# 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 +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Optional, Union + +import torch +from torch import nn +from torchvision.transforms.v2 import functional as F + +from transformers.models.llava_next.image_processing_llava_next_fast import LlavaNextImageProcessorFast +from transformers.models.llava_next_video.modeling_llava_next_video import ( + LlavaNextVideoCausalLMOutputWithPast, + LlavaNextVideoForConditionalGeneration, + LlavaNextVideoModel, + LlavaNextVideoModelOutputWithPast, + LlavaNextVideoPreTrainedModel, + TransformersKwargs, + get_anyres_image_grid_shape, + image_size_to_num_patches, + unpad_image, +) + +from ...cache_utils import Cache +from ...image_processing_utils import BatchFeature +from ...image_processing_utils_fast import DefaultFastImageProcessorKwargs, group_images_by_shape, reorder_images +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...processing_utils import Unpack +from ...utils import ( + TensorType, + auto_docstring, + can_return_tuple, + logging, +) + + +logger = logging.get_logger(__name__) + + +class LlavaOnevisionFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): + """ + image_grid_pinpoints (`list[list[int]]`, *optional*): + A list of possible resolutions to use for processing high resolution images. The best resolution is selected + based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` + method. + """ + + image_grid_pinpoints: Optional[list[list[int]]] + + +class LlavaOnevisionImageProcessorFast(LlavaNextImageProcessorFast): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 384, "width": 384} + crop_size = None + default_to_square = False + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_pad = True + image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304]] # fmt: skip + model_input_names = ["pixel_values", "image_sizes", "batch_num_images"] + + # Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square + def pad_to_square( + self, + images: "torch.Tensor", + background_color: Union[int, tuple[int, int, int]] = 0, + ) -> "torch.Tensor": + """ + Pads an image to a square based on the longest edge. + + Args: + images (`np.ndarray`): + The images to pad. + background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): + The color to use for the padding. Can be an integer for single channel or a + tuple of integers representing for multi-channel images. If passed as integer + in multi-channel mode, it will default to `0` in subsequent channels. + Returns: + `torch.Tensor`: The padded images. + """ + height, width = get_image_size(images, ChannelDimension.FIRST) + + if height == width: + return images + + num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0] + if isinstance(background_color, int): + background_color = [background_color] + [0] * (num_channels - 1) + elif len(background_color) != num_channels: + raise ValueError( + f"background_color must have no more than {num_channels} elements to match the number of channels" + ) + + max_dim = max(height, width) + paste_x_left = (max_dim - width) // 2 + paste_y_left = (max_dim - height) // 2 + paste_x_right = max_dim - width - paste_x_left + paste_y_right = max_dim - height - paste_y_left + padded_images = F.pad( + images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color + ) + + return padded_images + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]) -> BatchFeature: + if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)): + # if the first element is a list, we assume that all elements are lists + batch_num_images = [len(x) for x in images] + elif isinstance(images, (tuple, list)): + # treat this as a single-image case for backward compatibility + batch_num_images = [1] * len(images) + else: + batch_num_images = [1] + kwargs["batch_num_images"] = batch_num_images + return super().preprocess(images, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + image_grid_pinpoints: list[list[int]], + interpolation: Optional["F.InterpolationMode"], + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: Optional[Union[float, list[float]]], + image_std: Optional[Union[float, list[float]]], + do_pad: bool, + batch_num_images: list[int], + disable_grouping: Optional[bool], + return_tensors: Optional[Union[str, TensorType]], + **kwargs, + ) -> BatchFeature: + processed_images = [] + image_sizes = [] + + # only single image patching is supported + need_patching = [n == 1 for n in batch_num_images for _ in range(n)] + + # Determine the size tuple + if size and size.height and size.width: + size_tuple = (size.height, size.width) + else: + size_tuple = (size.shortest_edge, size.shortest_edge) + + # Determine the patch size + if crop_size and crop_size.height: + patch_size = crop_size.height + elif size and size.height: + patch_size = size.height + else: + patch_size = size.shortest_edge + + for i, image in enumerate(images): + if need_patching[i]: + image_patches = self._get_image_patches( + image, + image_grid_pinpoints, + size=size_tuple, + patch_size=patch_size, + interpolation=interpolation, + ) + else: + padded_image = self.pad_to_square( + images=image, background_color=tuple(int(x * 255) for x in self.image_mean) + ) + image_patches = [padded_image] + + # Group images by size for batched processing + processed_image_patches_grouped = {} + grouped_image_patches, grouped_image_patches_index = group_images_by_shape( + image_patches, disable_grouping=disable_grouping + ) + for shape, stacked_image_patches in grouped_image_patches.items(): + if do_resize: + stacked_image_patches = self.resize( + image=stacked_image_patches, + size=size, + interpolation=interpolation, + ) + if do_center_crop: + stacked_image_patches = self.center_crop(stacked_image_patches, crop_size) + # Fused rescale and normalize + stacked_image_patches = self.rescale_and_normalize( + stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_image_patches_grouped[shape] = stacked_image_patches + processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index) + processed_image_patches = ( + torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches + ) + processed_images.append(processed_image_patches) + image_sizes.append(get_image_size(image, ChannelDimension.FIRST)) + + if do_pad: + processed_images = self._pad_for_batching(processed_images) + processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images + return BatchFeature( + data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images}, + tensor_type=return_tensors, + ) + + +class LlavaOnevisionModelOutputWithPast(LlavaNextVideoModelOutputWithPast): + pass + + +class LlavaOnevisionCausalLMOutputWithPast(LlavaNextVideoCausalLMOutputWithPast): + pass + + +class LlavaOnevisionPreTrainedModel(LlavaNextVideoPreTrainedModel): + pass + + +class LlavaOnevisionModel(LlavaNextVideoModel): + def __init__(self, config): + super().__init__(config) + del self.vision_resampler + + def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"): + """ + Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. + + Args: + image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) + List of image feature tensor, each contains all the visual feature of all patches. + image_sizes (`torch.Tensor` of shape `(num_images, 2)`) + Actual image size of each images (H, W). + image_newline (`torch.Tensor` of shape `(embed_dim)`) + New line embedding vector. + vision_aspect_ratio (`str`, *optional*, "anyres_max_9"): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + Returns: + image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) + feature_lens (`list[int]`) + token length of each image in image_features + """ + new_image_features = [] + feature_lens = [] + for image_idx, image_feature in enumerate(image_features): + if image_feature.shape[0] > 1: + base_image_feature = image_feature[0] + image_feature = image_feature[1:] + height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size + if height * width != base_image_feature.shape[0]: + raise ValueError("The number of patches is not consistent with the image size.") + num_patch_height, num_patch_width = get_anyres_image_grid_shape( + image_sizes[image_idx], + self.config.image_grid_pinpoints, + self.config.vision_config.image_size, + ) + image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) + image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() + image_feature = image_feature.flatten(1, 2).flatten(2, 3) + image_feature = unpad_image(image_feature, image_sizes[image_idx]) + max_num_patches = int(vision_aspect_ratio.strip("anyres_max_")) + channels, curr_height, curr_width = image_feature.shape + ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2)) + if ratio > 1.1: + image_feature = image_feature[None] + image_feature = nn.functional.interpolate( + image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear" + )[0] + if image_newline is not None: + image_feature = torch.cat( + ( + image_feature, + image_newline[:, None, None] + .expand(*image_feature.shape[:-1], 1) + .to(image_feature.device, image_feature.dtype), + ), + dim=-1, + ) + image_feature = image_feature.flatten(1, 2).transpose(0, 1) + image_feature = torch.cat((base_image_feature, image_feature), dim=0) + else: + image_feature = image_feature[0] + if image_newline is not None: + image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) + new_image_features.append(image_feature) + feature_lens.append(image_feature.size(0)) + feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device) + return new_image_features, feature_lens + + def apply_pooling(self, image_features): + height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size + batch_frames, seq_len, dim = image_features.shape + image_features = image_features.view(batch_frames, height, width, -1) + image_features = image_features.permute(0, 3, 1, 2).contiguous() + + height, width = image_features.shape[2:] + scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)] + image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear") + + image_features = image_features.permute(0, 2, 3, 1) + image_features = image_features.view(batch_frames, -1, dim) + return image_features + + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_sizes: torch.Tensor, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + ): + """ + Obtains image last hidden states from the vision tower and apply multimodal projection. + + Args: + pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) + The tensors corresponding to the input images. + image_sizes (`torch.Tensor` of shape `(num_images, 2)`) + Actual image size of each images (H, W). + vision_feature_layer (`Union[int, list[int]]`): + The index of the layer to select the vision feature. If multiple indices are provided, + the vision feature of the corresponding indices will be concatenated to form the + vision features. + vision_feature_select_strategy (`str`): + The feature selection strategy used to select the vision feature from the vision backbone. + Can be one of `"default"` or `"full"` + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + Returns: + image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches + and are of shape `(num_patches, image_length, embed_dim)`). + """ + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + # ! infer image_num_patches from image_sizes + if batch_num_images is None: + # treat this as a single-image case for backward compatibility + need_patching = [True] * len(image_sizes) + else: + need_patching = [n == 1 for n in batch_num_images for _ in range(n)] + image_num_patches = [ + image_size_to_num_patches( + image_size=imsize, + grid_pinpoints=self.config.image_grid_pinpoints, + patch_size=self.config.vision_config.image_size, + ) + if should_patch + else 1 + for imsize, should_patch in zip(image_sizes, need_patching) + ] + if pixel_values.dim() == 5: + # stacked if input is (batch_size, num_patches, num_channels, height, width) + _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)] + pixel_values = torch.cat(_pixel_values_list, dim=0) + elif pixel_values.dim() != 4: + # otherwise has to be stacked from list of (num_patches, num_channels, height, width) + raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions") + + image_features = self.vision_tower(pixel_values, output_hidden_states=True) + # If we have one vision feature layer, return the corresponding hidden states, + # otherwise, select the hidden states of each feature layer and concatenate them + if isinstance(vision_feature_layer, int): + selected_image_feature = image_features.hidden_states[vision_feature_layer] + else: + hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] + selected_image_feature = torch.cat(hs_pool, dim=-1) + + if vision_feature_select_strategy == "default": + selected_image_feature = selected_image_feature[:, 1:] + image_features = self.multi_modal_projector(selected_image_feature) + image_features = torch.split(image_features, image_num_patches, dim=0) + + image_features, feature_lens = self.pack_image_features( + image_features, + image_sizes, + image_newline=self.image_newline, + vision_aspect_ratio=vision_aspect_ratio, + ) + return image_features + + def get_video_features( + self, + pixel_values: torch.FloatTensor, + vision_feature_layer: Union[int, list[int]], + vision_feature_select_strategy: str, + ): + """ + Obtains video last hidden states from the vision tower, apply multimodal projection and pooling. + + Args: + pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`) + The tensors corresponding to the input video. + vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`): + The index of the layer to select the vision feature. If multiple indices are provided, + the vision feature of the corresponding indices will be concatenated to form the + vision features. + vision_feature_select_strategy (`str`): + The feature selection strategy used to select the vision feature from the vision backbone. + Can be one of `"default"` or `"full"` + Returns: + video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches + and are of shape `(num_videos, video_length, embed_dim)`). + """ + batch_size, frames, channels, height, width = pixel_values.shape + pixel_values = pixel_values.view(batch_size * frames, channels, height, width) + video_features = self.vision_tower(pixel_values, output_hidden_states=True) + + # If we have one vision feature layer, return the corresponding hidden states, + # otherwise, select the hidden states of each feature layer and concatenate them + if isinstance(vision_feature_layer, int): + selected_video_feature = video_features.hidden_states[vision_feature_layer] + else: + hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] + selected_video_feature = torch.cat(hs_pool, dim=-1) + + if vision_feature_select_strategy == "default": + selected_video_feature = selected_video_feature[:, 1:] + video_features = self.multi_modal_projector(selected_video_feature) + + video_features = self.apply_pooling(video_features) + video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1) + + return video_features + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_sizes_videos: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Union[tuple, LlavaOnevisionModelOutputWithPast]: + r""" + image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): + The sizes of the videos in the batch, being (height, width) for each frame in the video. + vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + # Images are processed with Anyres + if pixel_values is not None: + image_features = self.get_image_features( + pixel_values, + image_sizes, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + batch_num_images=batch_num_images, + ) + image_features = torch.cat(image_features, dim=0) + image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) + special_image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + # Video are simply embedded and further pooled to decrease seq len + if pixel_values_videos is not None: + video_features = self.get_video_features( + pixel_values_videos, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + ) + image_newline = ( + self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device) + ) + video_features = torch.cat((video_features, image_newline), dim=1) + video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype) + _, special_video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + cache_position=cache_position, + **kwargs, + ) + + return LlavaOnevisionModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + video_hidden_states=video_features if pixel_values_videos is not None else None, + ) + + +class LlavaOnevisionForConditionalGeneration(LlavaNextVideoForConditionalGeneration): + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_sizes: Optional[torch.LongTensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_sizes_videos: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + vision_feature_layer: Optional[Union[int, list[int]]] = None, + vision_feature_select_strategy: Optional[str] = None, + vision_aspect_ratio: Optional[str] = None, + batch_num_images: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> Union[tuple, LlavaOnevisionCausalLMOutputWithPast]: + r""" + image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): + The sizes of the videos in the batch, being (height, width) for each frame in the video. + vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): + Aspect ratio used when processong image features. The default value is "anyres_max_9". + batch_num_images (`torch.LongTensor`, *optional*): + Number of images in each sample. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import requests + >>> import torch + >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration + + >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", dtype="float16", device_map="cuda:0") + >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") + + >>> conversation = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "text", "text": "What is shown in this image?"}, + ... {"type": "image"}, + ... ], + ... }, + ... ] + >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) + + >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> raw_image = Image.open(requests.get(image_file, stream=True).raw) + >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16) + + >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False) + >>> processor.batch_decode(output, skip_special_tokens=True)[0] + "user\n\nWhat is shown in this image?\nassistant\ncat" + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layer = ( + vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer + ) + vision_feature_select_strategy = ( + vision_feature_select_strategy + if vision_feature_select_strategy is not None + else self.config.vision_feature_select_strategy + ) + vision_aspect_ratio = ( + vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio + ) + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_sizes=image_sizes, + image_sizes_videos=image_sizes_videos, + vision_aspect_ratio=vision_aspect_ratio, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + batch_num_images=batch_num_images, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + cache_position=cache_position, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return LlavaOnevisionCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + video_hidden_states=outputs.video_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + image_sizes=None, + pixel_values_videos=None, + image_sizes_videos=None, + attention_mask=None, + cache_position=None, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + cache_position=cache_position, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + if cache_position[0] == 0: + # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore + # Otherwise we need pixel values to be passed to model + model_inputs["pixel_values"] = pixel_values + model_inputs["image_sizes"] = image_sizes + model_inputs["pixel_values_videos"] = pixel_values_videos + model_inputs["image_sizes_videos"] = image_sizes_videos + + return model_inputs + + +__all__ = [ + "LlavaOnevisionImageProcessorFast", + "LlavaOnevisionModel", + "LlavaOnevisionForConditionalGeneration", + "LlavaOnevisionPreTrainedModel", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/processing_llava_onevision.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/processing_llava_onevision.py new file mode 100644 index 0000000000000000000000000000000000000000..9fadc6af3067e3478c3f21626e4f9d3f99e64b00 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/transformers/models/llava_onevision/processing_llava_onevision.py @@ -0,0 +1,333 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# 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 +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for LLaVa-Onevision. +""" + +import math +from collections.abc import Iterable +from typing import Optional, Union + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_utils import select_best_resolution +from ...image_utils import ImageInput, get_image_size, to_numpy_array +from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import logging +from ...video_utils import VideoInput + + +logger = logging.get_logger(__name__) + + +class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False): + # see processing_utils.ProcessingKwargs documentation for usage. + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": False, + }, + "image_kwargs": {}, + "videos_kwargs": {}, + } + + +class LlavaOnevisionProcessor(ProcessorMixin): + r""" + Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor. + + [`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`LlavaOnevisionImageProcessor`] and [`LlamaTokenizerFast`]. See the + [`~LlavaOnevisionVideoProcessor.__call__`], [`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for more information. + + Args: + image_processor ([`LlavaOnevisionImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`LlamaTokenizerFast`], *optional*): + The tokenizer is a required input. + video_processor ([`LlavaOnevisionVideoProcessor`], *optional*): + The video processor is a required input. + num_image_tokens (`int`, *optional*): + Number of image tokens for one imagethat will be returned by vision tower. + vision_feature_select_strategy (`str`, *optional*): + The feature selection strategy used to select the vision feature from the vision backbone. + Should be same as in model's config + chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages + in a chat into a tokenizable string. + image_token (`str`, *optional*, defaults to `""`): + Special token used to denote image location. + video_token (`str`, *optional*, defaults to `"