File size: 8,876 Bytes
c39b2dc |
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 |
import math
from typing import ClassVar, List, Optional, Tuple, Union
import torch
from PIL import Image
from transformers import BatchFeature
from .processing_utils import BaseVisualRetrieverProcessor
import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
from .conversation import get_conv_template
from transformers import BatchFeature, ProcessorMixin
class ColInternVL2Processor(BaseVisualRetrieverProcessor, ProcessorMixin):
"""
Processor for ColInternVL2.
"""
attributes = [ "tokenizer"]
image_processor_class = "InternVL2ImageProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(self, tokenizer, **kwargs):
self.template = "Hermes-2"
self.num_image_token = 256
# self.max_num = 6
self.max_num = 4
if isinstance(tokenizer, str):
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer, trust_remote_code=True, use_fast=False)
else:
self.tokenizer = tokenizer
self.tokenizer.padding_side = 'left'
self.IMAGENET_MEAN = (0.485, 0.456, 0.406)
self.IMAGENET_STD = (0.229, 0.224, 0.225)
self.IMG_CONTEXT_TOKEN='<IMG_CONTEXT>'
self.IMG_START_TOKEN='<img>'
self.IMG_END_TOKEN='</img>'
self.img_context_token_id = self.tokenizer.convert_tokens_to_ids(self.IMG_CONTEXT_TOKEN)
# self.system_message = '你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。'
self.system_message = 'Bạn là một mô hình trí tuệ nhân tạo đa phương thức Tiếng Việt có tên gọi là Vintern, được phát triển bởi người Việt. Bạn là một trợ lý trí tuệ nhân tạo hữu ích và không gây hại.'
super().__init__(tokenizer)
# def from_pretrained(pretrained_model_name_or_path, template="Hermes-2", **kwargs):
# return ColInternVL2Processor(pretrained_model_name_or_path, template=template, **kwargs)
def build_transform(self, input_size):
MEAN, STD = self.IMAGENET_MEAN, self.IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(self, image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = self.find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(self, image, input_size=448, max_num=12):
transform = self.build_transform(input_size=input_size)
images = self.dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def process_images(
self,
images: List[Image.Image],
) -> BatchFeature:
"""
Process images for InternVl2.
"""
pixel_values = [ self.load_image(image, max_num=self.max_num) for image in images]
num_patches_list = [ pixel_.size(0) for pixel_ in pixel_values]
image_flags = [ torch.tensor([1] * pixel_.shape[0], dtype=torch.long) for pixel_ in pixel_values ]
queries = []
for idx, num_patches in enumerate(num_patches_list):
question = "<image>\nDescribe the image."
template = get_conv_template(self.template)
template.system_message = self.system_message
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
image_tokens = self.IMG_START_TOKEN + self.IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + self.IMG_END_TOKEN
query = query.replace('<image>', image_tokens, 1)
queries.append(query)
model_inputs = self.tokenizer(queries, return_tensors='pt', padding=True)
input_ids = model_inputs['input_ids'] #.to(self.device)
attention_mask = model_inputs['attention_mask'] #.to(self.device)
pixel_values = torch.cat(pixel_values)
batch_doc = BatchFeature({
"pixel_values" : pixel_values,
"input_ids" : input_ids,
"attention_mask" : attention_mask,
# "image_flags" : image_flags
})
return batch_doc
def process_queries(
self,
queries: List[str],
max_length: int = 100,
suffix: Optional[str] = None,
) -> BatchFeature:
"""
Process queries for InternVl2.
"""
texts_query: List[str] = []
for query in queries:
query = f"Query: {query}"
template = get_conv_template(self.template)
template.system_message = self.system_message
template.append_message(template.roles[0], query)
template.append_message(template.roles[1], None)
query = template.get_prompt()
texts_query.append(query)
model_inputs = self.tokenizer(texts_query, return_tensors='pt', max_length=max_length, padding="longest")
input_ids = model_inputs['input_ids'] #.to(self.device)
attention_mask = model_inputs['attention_mask'] #.to(self.device)
batch_query = BatchFeature({
"pixel_values" : None,
"input_ids" : input_ids,
"attention_mask" : attention_mask,
})
return batch_query
def score(
self,
qs: List[torch.Tensor],
ps: List[torch.Tensor],
device: Optional[Union[str, torch.device]] = None,
**kwargs,
) -> torch.Tensor:
"""
Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
"""
return self.score_multi_vector(qs, ps, device=device, **kwargs)
def get_n_patches(
self,
image_size: Tuple[int, int],
patch_size: int,
) -> Tuple[int, int]:
raise NotImplementedError("This method is not implemented for ColInternVL2.")
|