File size: 22,045 Bytes
19e7ad7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 |
# 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
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)) |