# coding=utf-8 # Copyright 2023 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. """ import itertools from typing import List, Optional, Union import PIL.Image import numpy as np from transformers import AutoTokenizer from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ( ImageInput, make_list_of_images, valid_images, infer_channel_dimension_format, to_numpy_array, get_image_size, ChannelDimension, ) from transformers.image_processing_utils import get_size_dict from transformers.image_utils import PILImageResampling from transformers.processing_utils import ProcessorMixin from transformers.image_transforms import resize, pad, PaddingMode, to_channel_dimension_format, get_resize_output_image_size from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from transformers.utils import TensorType class PllavaProcessor(ProcessorMixin): r""" Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor. [`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information. Args: image_processor ([`CLIPImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "CLIPImageProcessor" tokenizer_class = "AutoTokenizer" def __init__(self, image_processor=None, tokenizer=None, shortest_edge=336, longest_edge=762, center_pad=False): self.shortest_edge = shortest_edge self.longest_edge = longest_edge self.center_pad = center_pad super().__init__(image_processor, tokenizer) def resize_crop_longshort(self, videos: list[list[np.ndarray]], input_data_format): video_spatial_sizes = [get_image_size(images[0], input_data_format) for images in videos] long_short_rates = [max(size) / min(size) for size in video_spatial_sizes] min_long_short_rate = min(long_short_rates) min_long_short_video_idx = long_short_rates.index(min_long_short_rate) clip_resolution = self.image_processor.size['shortest_edge'] out_video_spatial_size = video_spatial_sizes[min_long_short_video_idx] out_videos_short_edge = max(min(size) for size in video_spatial_sizes) resize_longest_edge = max(max(size) for size in video_spatial_sizes) resize_longest_edge = min(640, resize_longest_edge) out_videos_short_edge = min(out_videos_short_edge, int(resize_longest_edge / min_long_short_rate)) out_videos_short_edge = max(out_videos_short_edge, clip_resolution) if out_video_spatial_size[0] > out_video_spatial_size[1]: # h > w: out_video_spatial_size = (int(out_videos_short_edge * min_long_short_rate), out_videos_short_edge ) else: out_video_spatial_size = ( out_videos_short_edge, int(out_videos_short_edge * min_long_short_rate) ) videos = [ [self.resize(frame, input_data_format=input_data_format, shortest_edge=out_videos_short_edge, longest_edge=9999) for frame in frames] for frames in videos ] out_videos = [] for frames in videos: out_frames = [] video_spatial_size = get_image_size(frames[0], input_data_format) assert min(video_spatial_size) == out_videos_short_edge overhead = (max(video_spatial_size) - max(out_video_spatial_size)) // 2 slice_start, slice_end = overhead // 2, overhead // 2 + max(out_video_spatial_size) hslice, wslice = (slice(slice_start, slice_end), slice(None, None)) if video_spatial_size[0] > video_spatial_size[1] \ else (slice(None, None), slice(slice_start, slice_end)) # h > w for frame in frames: if input_data_format == ChannelDimension.FIRST: out_frames.append(frame[..., hslice, wslice]) elif input_data_format == ChannelDimension.LAST: out_frames.append(frame[..., hslice, wslice, :]) out_videos.append(out_frames) return out_videos @staticmethod def _compute_num_blocks_and_overlaps(input_shape, resolution): input_shape = np.array(input_shape) resolution = np.array(resolution) assert input_shape.max() >= resolution num_blocks = np.ceil(input_shape / resolution).astype(np.int32).tolist() overlaps = [0 if size % resolution==0 else int(np.floor((resolution - size % resolution) / (num_block - 1))) for num_block, size in zip(num_blocks, input_shape)] return num_blocks, overlaps def resize( self, image: np.ndarray, resample: PILImageResampling = PILImageResampling.BICUBIC, # type: ignore data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, shortest_edge: int = None, longest_edge: int = None, **kwargs, ) -> np.ndarray: """ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ shortest_edge = getattr(self, 'shortest_edge', None) if shortest_edge is None else shortest_edge longest_edge = getattr(self, 'longest_edge', None) if longest_edge is None else longest_edge default_to_square = False output_size = get_resize_output_image_size( image, size=shortest_edge, default_to_square=default_to_square, max_size=longest_edge, input_data_format=input_data_format, ) clip_resolution = self.image_processor.size['shortest_edge'] if min(output_size) < clip_resolution: output_size = get_resize_output_image_size( image, size=shortest_edge, default_to_square=default_to_square, input_data_format=input_data_format, ) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, images: ImageInput = None, center_pad = None, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length=None, return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). 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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). truncation (`bool`, *optional*): Activates truncation to cut input sequences longer than `max_length` to `max_length`. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. """ data=dict() if images is not None: if isinstance(images, list) and isinstance(images[0], PIL.Image.Image): videos = [images] # one video else: videos = images pixel_values_list = [] videos = [[to_numpy_array(image) for image in make_list_of_images(images)] for images in videos] # images = [self.resize(image, ) if min(get_image_size(image, input_data_format)) < clip_resolution else image for image in images] input_data_format = infer_channel_dimension_format(videos[0][0]) videos = self.resize_crop_longshort(videos, input_data_format) for images in videos: 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." ) center_pad = center_pad if center_pad is not None else self.center_pad if center_pad: images = [self.pad_to_square(image, 0, input_data_format, input_data_format) for image in images] pixel_values = self.image_processor(images, return_tensors='np')["pixel_values"] pixel_values_list.append(pixel_values) pixel_values = np.concatenate(pixel_values_list) data.update(pixel_values=pixel_values) else: data.update(pixel_values = None) if text is not None: text_inputs = self.tokenizer( text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length ) data.update(**text_inputs) return BatchFeature(data, tensor_type=return_tensors) # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama def decode(self, *args, **kwargs): """ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))