File size: 8,982 Bytes
032e687 |
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 |
import copy
from PIL import Image
import numpy as np
import torch
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoProcessor, AutoTokenizer
from xtuner.utils import IGNORE_INDEX
def find_closest_aspect_ratio(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(image,
min_num=1,
max_num=6,
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 = {(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 = 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 total_image_token(orig_size,
min_num=1,
max_num=12,
image_size=448,
use_thumbnail=True):
orig_width, orig_height = orig_size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = {(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 max_num >= 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 = find_closest_aspect_ratio(aspect_ratio,
target_ratios, orig_width,
orig_height, image_size)
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
if use_thumbnail:
blocks += 1
return blocks
class InternVLProcessor:
IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
IMG_START_TOKEN = '<img>'
IMG_END_TOKEN = '</img>'
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
SYSTEM = ''
template = dict(
SYSTEM='<|system|>\n{system}<|end|>\n',
# INSTRUCTION='<|user|>\n{input}<|end|>\n<|assistant|>\n',
INSTRUCTION='<|user|>\n{input}<|end|><|assistant|>\n',
SUFFIX='<|end|>',
SUFFIX_AS_EOS=True,
SEP='\n',
STOP_WORDS=['<|end|>'])
def __init__(self,
max_length=8192,
special_tokens=['[SEG]'],
pretrained_model_name_or_path=None):
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
if special_tokens:
self.tokenizer.add_tokens(special_tokens, special_tokens=True)
self.max_length = max_length
self.min_dynamic_patch = 1
self.max_dynamic_patch = 12
self.downsample_ratio = 0.5
self.image_size = 448
self.use_thumbnail = True
patch_size = 14
self.patch_token = int(
(self.image_size // patch_size)**2 * (self.downsample_ratio**2))
self.transformer = T.Compose([
T.Lambda(lambda img: img.convert('RGB')
if img.mode != 'RGB' else img),
T.Resize((self.image_size, self.image_size),
interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD)
])
def get_inputid_labels(self, conversations, image_token_str) -> dict:
input = ''
out_conversation = []
while conversations and conversations[0]['from'] == 'gpt':
conversations = conversations[1:]
for msg in conversations:
if msg['from'] == 'human':
if image_token_str is None and '<image>' in msg['value']:
msg['value'] = msg['value'].replace('<image>', '')
if '<image>' in msg['value']:
msg['value'] = msg['value'].replace('<image>', image_token_str).strip()
input += msg['value'].strip()
elif msg['from'] == 'gpt':
out_conversation.append({
'input': input,
'output': msg['value'].strip()
})
input = ''
else:
raise NotImplementedError
input_ids, labels = [], []
for i, single_turn_conversation in enumerate(out_conversation):
input = single_turn_conversation.get('input', '')
if input is None:
input = ''
input_text = self.template['INSTRUCTION'].format(
input=input, round=i + 1)
if i == 0:
if self.SYSTEM:
system = self.template['SYSTEM'].format(system=self.SYSTEM)
input_text = system + input_text
input_encode = self.tokenizer.encode(
input_text, add_special_tokens=True)
else:
input_encode = self.tokenizer.encode(
input_text, add_special_tokens=False)
input_ids += input_encode
labels += [IGNORE_INDEX] * len(input_encode)
output_text = single_turn_conversation.get('output', '')
if self.template.get('SUFFIX', None):
output_text += self.template['SUFFIX']
output_encode = self.tokenizer.encode(
output_text, add_special_tokens=False)
input_ids += output_encode
labels += copy.deepcopy(output_encode)
if len(input_ids) > self.max_length:
input_ids = input_ids[:self.max_length]
labels = labels[:self.max_length]
return {'input_ids': input_ids, 'labels': labels}
def __call__(self, data_dict):
out_data_dict = {}
if data_dict.get('image', None) is not None:
image_file = data_dict['image']
try:
image = Image.open(image_file).convert('RGB')
except Exception as e:
return None
images = dynamic_preprocess(image, self.min_dynamic_patch, self.max_dynamic_patch, self.image_size, self.use_thumbnail)
pixel_values = [self.transformer(image) for image in images]
pixel_values = torch.stack(pixel_values)
out_data_dict['pixel_values'] = pixel_values
num_image_tokens = pixel_values.shape[0] * self.patch_token
image_token_str = f'{self.IMG_START_TOKEN}' \
f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \
f'{self.IMG_END_TOKEN}'
token_dict = self.get_inputid_labels(data_dict['conversations'], image_token_str)
out_data_dict.update(token_dict)
else:
token_dict = self.get_inputid_labels(data_dict['conversations'], None)
out_data_dict.update(token_dict)
out_data_dict['pixel_values'] = torch.zeros(1, 3, self.image_size, self.image_size)
return out_data_dict |