# coding=utf-8
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
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
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 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
# convert to RGB first
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))