# coding=utf-8 # Copyright 2024 Microsoft and 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. """ Processor class for Phi3-V. """ import re from typing import List, Optional, Union import torch import transformers from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput from transformers.processing_utils import ProcessorMixin from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy from transformers.utils import TensorType """Image processor class for Phi3-V.""" from typing import List, Optional, Union import numpy as np from transformers.image_processing_utils import BaseImageProcessor, BatchFeature from transformers.image_transforms import ( convert_to_rgb, ) from transformers.image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ImageInput, make_list_of_images, valid_images, ) from transformers.utils import TensorType, is_vision_available, logging from transformers import AutoImageProcessor logger = logging.get_logger(__name__) if is_vision_available(): from PIL import Image import torch # why is this imported twice? import torchvision def padding_336(b): width, height = b.size tar = int(np.ceil(height / 336) * 336) top_padding = int((tar - height)/2) bottom_padding = tar - height - top_padding left_padding = 0 right_padding = 0 b = torchvision.transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255]) return b def calc_padded_size(width, height, padding_unit=336): target_height = int(np.ceil(height / padding_unit) * padding_unit) top_padding = int((target_height - height) / 2) bottom_padding = target_height - height - top_padding left_padding = 0 right_padding = 0 padded_width = width + left_padding + right_padding padded_height = height + top_padding + bottom_padding return padded_width, padded_height def HD_transform(img, hd_num=16): width, height = img.size trans = False if width < height: img = img.transpose(Image.TRANSPOSE) trans = True width, height = img.size ratio = (width/ height) scale = 1 while scale*np.ceil(scale/ratio) <= hd_num: scale += 1 scale -= 1 new_w = int(scale * 336) new_h = int(new_w / ratio) img = torchvision.transforms.functional.resize(img, [new_h, new_w],) img = padding_336(img) width, height = img.size if trans: img = img.transpose(Image.TRANSPOSE) return img def calc_hd_transform_size(width, height, hd_num=16): transposed = False if width < height: width, height = height, width transposed = True ratio = width / height scale = 1 while scale * np.ceil(scale / ratio) <= hd_num: scale += 1 scale -= 1 new_width = int(scale * 336) new_height = int(new_width / ratio) padded_width, padded_height = calc_padded_size(new_width, new_height) if transposed: padded_width, padded_height = padded_height, padded_width return padded_width, padded_height def pad_to_max_num_crops_tensor(images, max_crops=5): """ images: B x 3 x H x W, B<=max_crops """ B, _, H, W = images.shape if B < max_crops: pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device) images = torch.cat([images, pad], dim=0) return images class Phi3VImageProcessor(BaseImageProcessor): r""" Constructs a Phi3 image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques for processing high resolution images as explained in the [InternLM-XComposer2-4KHD](https://arxiv.org/pdf/2404.06512) Args: 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_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. """ model_input_names = ["pixel_values"] def __init__( self, num_crops: int = 1, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) self.num_crops = num_crops 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_convert_rgb = do_convert_rgb def calc_num_image_tokens( self, images: ImageInput ): """ Calculate the number of image tokens for each image. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. """ images = make_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." ) images = [image.convert('RGB') for image in images] # (H, W, C) elems = [HD_transform(im, hd_num = self.num_crops) for im in images] shapes = [[im.size[1], im.size[0]] for im in elems] num_img_tokens = [int((h//336*w//336+1)*144 + 1 + (h//336+1)*12) for h, w in shapes] return num_img_tokens def calc_num_image_tokens_from_image_size(self, width, height): """ Calculate the number of image tokens for a given image size. Args: width (`int`): Width of the image. height (`int`): Height of the image. """ new_width, new_height = calc_hd_transform_size(width, height, hd_num=self.num_crops) num_img_tokens = int((new_height // 336 * new_width // 336 + 1) * 144 + 1 + (new_height // 336 + 1) * 12) return num_img_tokens def convert_PIL(self, image): """ Convert an image to a PIL Image object if it is not already one. Args: image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`): The image to be converted. Can be a numpy array or a torch tensor or PIL object. Returns: A PIL Image object. """ if not isinstance(image, Image.Image): return torchvision.transforms.functional.to_pil_image(image) else: return image def preprocess( self, images: ImageInput, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: bool = None, return_tensors: Optional[Union[str, TensorType]] = None, ): """ Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. 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_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`. """ 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_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb images = make_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." ) # if do_convert_rgb: # images = [convert_to_rgb(image) for image in images] image_sizes = [] img_processor = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(image_mean, image_std) ]) # PIL images # HD_transform pad images to size of multiiply of 336, 336 # check and convert if the images are in PIL format images = [convert_PIL(image) for image in images] # convert to RGB first (I think the argument "do_convert_rgb is useless, since it is forced here") images = [image.convert('RGB') for image in images] elems = [HD_transform(im, hd_num = self.num_crops) for im in images] # tensor transform and normalize hd_images = [img_processor(im) for im in elems] # create global image global_image = [torch.nn.functional.interpolate(im.unsqueeze(0).float(), size=(336, 336), mode='bicubic',).to(im.dtype) for im in hd_images] # [(3, h, w)], where h, w is multiple of 336 shapes = [[im.size(1), im.size(2)] for im in hd_images] num_img_tokens = [int(((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12) for h, w in shapes] # reshape to channel dimension -> (num_images, num_crops, 3, 336, 336) # (1, 3, h//336, 336, w//336, 336) -> (1, h//336, w//336, 3, 336, 336) -> (h//336*w//336, 3, 336, 336) hd_images_reshape = [im.reshape(1, 3, h//336, 336, w//336, 336).permute(0,2,4,1,3,5).reshape(-1, 3, 336, 336).contiguous() for im, (h, w) in zip(hd_images, shapes)] # concat global image and local image hd_images_reshape = [torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)] # pad to max_num_crops image_transformed = [pad_to_max_num_crops_tensor(im, self.num_crops+1) for im in hd_images_reshape] image_transformed = torch.stack(image_transformed, dim=0) image_sizes = [torch.LongTensor(_shapes) for _shapes in shapes] padded_images = image_transformed image_sizes = shapes data = {"pixel_values": padded_images, "image_sizes": image_sizes, "num_img_tokens": num_img_tokens } return BatchFeature(data=data, tensor_type=return_tensors) AutoImageProcessor.register("Phi3VImageProcessor", Phi3VImageProcessor) transformers.Phi3VImageProcessor = Phi3VImageProcessor class Phi3VProcessor(ProcessorMixin): r""" Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor. [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information. Args: image_processor ([`Phi3VImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "Phi3VImageProcessor" tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") special_image_token = "<|image|>" def __init__(self, image_processor, tokenizer): self.image_processor = image_processor self.tokenizer = tokenizer self.num_img_tokens = image_processor.num_img_tokens self.img_tokens = [f"<|image_{i+1}|>" for i in range(1000000)] def __call__( self, text: Union[TextInput, List[TextInput]], images: ImageInput = 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 Phi3ImageProcessor's [`~Phi3ImageProcessor.__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. Both channels-first and channels-last formats are supported. 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`. """ if images is not None: image_inputs = self.image_processor(images, return_tensors=return_tensors) else: image_inputs = {} inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors) return inputs def calc_num_image_tokens(self, images: ImageInput): """ Calculate the number of image tokens for each image. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. """ return self.image_processor.calc_num_image_tokens(images) def calc_num_image_tokens_from_image_size(self, width, height): """ Calculate the number of image token for an image with given width and height. Args: width (`int`): Width of the image. height (`int`): Height of the image. """ return self.image_processor.calc_num_image_tokens_from_image_size(width, height) @property def special_image_token_id(self): return self.tokenizer.convert_tokens_to_ids(self.special_image_token) def get_special_image_token_id(self): return self.tokenizer.convert_tokens_to_ids(self.special_image_token) def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None): if not len(images): model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length) return BatchFeature(data={**model_inputs}) pattern = r"<\|image_\d+\|>" prompt_chunks = [self.tokenizer(chunk).input_ids for chunk in re.split(pattern, texts)] if 'num_img_tokens' in images: num_img_tokens = images['num_img_tokens'] else: assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided' num_crops = images['num_crops'] num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops] images, image_sizes = images['pixel_values'], images['image_sizes'] # image_tags needs to start from 1 to n image_tags = re.findall(pattern, texts) # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags] # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)] image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags] unique_image_ids = sorted(list(set(image_ids))) # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5] # check the condition assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}" # total images must be the same as the number of image tags assert len(unique_image_ids) == len(images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images" image_ids_pad = [[-iid]*num_img_tokens[iid-1] for iid in image_ids] def insert_separator(X, sep_list): if len(X) > len(sep_list): sep_list.append([]) return [ele for sublist in zip(X, sep_list) for ele in sublist] input_ids = [] offset = 0 for x in insert_separator(prompt_chunks, image_ids_pad): input_ids.extend(x[offset:]) input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0) attention_mask = (input_ids > -1000000).to(torch.long) return BatchFeature(data={"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": images, "image_sizes": image_sizes}) # 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))