File size: 50,601 Bytes
3246934 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 |
import cv2
import numpy as np
from PIL import Image
from concurrent.futures import ThreadPoolExecutor
from config.configu import *
from models.model import *
from models.similarity import *
from sklearn.cluster import KMeans
from utils.utils import *
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch
import random
import torch.utils.checkpoint
import transformers
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
LlamaTokenizer)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput, logging
from .configuration_internvl_chat import InternVLChatConfig
from .conversation import get_conv_template
from .modeling_intern_vit import InternVisionModel
from .modeling_internlm2 import InternLM2ForCausalLM
logger = logging.get_logger(__name__)
def coord_transform(box,return_4=True):
if return_4:
return [box[0][0],box[0][1],box[1][0],box[1][1]]
else:
return [[box[0],box[1]],[box[2],box[3]]]
def insert_zeros(input_ids, attention_mask, num_zeros=5):
device = input_ids.device # 获取原始设备
input_ids = input_ids.cpu().clone() # 将张量移到 CPU 并克隆
attention_mask = attention_mask.cpu().clone() # 将张量移到 CPU 并克隆
for _ in range(num_zeros):
# 随机选择插入位置
insert_pos = random.randint(0, input_ids.size(1))
# 在 input_ids 中插入 0
input_ids = torch.cat((input_ids[:, :insert_pos], torch.tensor([[0]]), input_ids[:, insert_pos:]), dim=1)
# 在 attention_mask 中插入 1
attention_mask = torch.cat((attention_mask[:, :insert_pos], torch.tensor([[1]]), attention_mask[:, insert_pos:]), dim=1)
# 将张量移回原始设备
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
return input_ids, attention_mask
def add_Gaussian_noise(input_embeds, rate=1e-1):
device = input_embeds.device
input_embeds = input_embeds.cpu().clone()
mean = input_embeds.mean()
std = input_embeds.std()
noise = torch.randn(input_embeds.size()) * std + mean
noisy_input_embeds = input_embeds + rate * noise
noisy_input_embeds = noisy_input_embeds.to(device)
noisy_input_embeds = noisy_input_embeds.to(torch.bfloat16)
return noisy_input_embeds
def version_cmp(v1, v2, op='eq'):
import operator
from packaging import version
op_func = getattr(operator, op)
return op_func(version.parse(v1), version.parse(v2))
def most_frequent_rgb(image_array):
"""找一张图片中最frequent的rgb,用于填充mask"""
# Flatten the image array to a 2D array where each row is an RGB tuple
pixels = image_array.reshape(-1, image_array.shape[-1])
# Use np.unique with return_counts to find unique rows and their counts
unique_pixels, counts = np.unique(pixels, axis=0, return_counts=True)
# Find the index of the most frequent pixel
most_frequent_index = np.argmax(counts)
# Get the most frequent pixel and its count
most_frequent_pixel = unique_pixels[most_frequent_index]
frequency = counts[most_frequent_index]
return most_frequent_pixel, frequency
def most_frequent_rgb_fast(image_array):
"""快速查找图片中最频繁的RGB值，不返回频率"""
# 将RGB每个通道的值映射为一个唯一的整数，形如 R * 256^2 + G * 256 + B
flattened = image_array.reshape(-1, 3)
rgb_ints = flattened[:, 0] * 256**2 + flattened[:, 1] * 256 + flattened[:, 2]
# 使用np.bincount统计每个唯一RGB组合出现的次数
counts = np.bincount(rgb_ints)
# 找到出现次数最多的那个整数
most_frequent_index = np.argmax(counts)
# 将整数转换回RGB值
r = (most_frequent_index // 256**2) % 256
g = (most_frequent_index // 256) % 256
b = most_frequent_index % 256
return (r, g, b)
def mask_area(image_array,coords,color):
"""对一张图片在框定的一系列box进行mask"""
# Define the bounding box (x1, y1, x2, y2)
#color=average_rgb(modified_image)
for coord in coords:
x1, y1, x2, y2 = coord
image_array[y1:y2, x1:x2] =color # 255 for white in an RGB image
return image_array
class InternVLChatModel(PreTrainedModel):
config_class = InternVLChatConfig
main_input_name = 'pixel_values'
_supports_flash_attn_2 = True
_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None):
super().__init__(config)
assert version_cmp(transformers.__version__, '4.36.2', 'ge')
image_size = config.force_image_size or config.vision_config.image_size
patch_size = config.vision_config.patch_size
self.patch_size = patch_size
self.select_layer = config.select_layer
self.template = config.template
##TODO change the number of img tokens
self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
#self.num_image_token = 3
self.downsample_ratio = config.downsample_ratio
self.ps_version = config.ps_version
self.mu_sigma=torch.load(NORM_PARAMS_PATH)['weight']
self.mu=self.mu_sigma[:,0].reshape((-1,1))
self.sigma=self.mu_sigma[:,1].reshape((-1,1)) #[vocab_size, 1]
self.normed_emb,self.mu_sigma=self.load_normed_tok_embeddings(load_checkboard=True)
self.resampler=load_perceiver_resampler_2(PERCEIVER_CHECKPOINT,num_layers=4)
self.sorter=load_orderformer(ORDERFORMER_CHECKPOINT)
logger.info(f'num_image_token: {self.num_image_token}')
logger.info(f'ps_version: {self.ps_version}')
# print('vision_model', vision_model)
# print('language_model', language_model)
# print('config.llm_config.architectures[0]', config.llm_config.architectures[0])
if vision_model is not None:
self.vision_model = vision_model
else:
self.vision_model = InternVisionModel(config.vision_config)
if language_model is not None:
self.language_model = language_model
else:
if config.llm_config.architectures[0] == 'LlamaForCausalLM':
self.language_model = LlamaForCausalLM(config.llm_config)
elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
self.language_model = InternLM2ForCausalLM(config.llm_config)
else:
raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
vit_hidden_size = config.vision_config.hidden_size
llm_hidden_size = config.llm_config.hidden_size
self.mlp1 = nn.Sequential(
nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
nn.GELU(),
nn.Linear(llm_hidden_size, llm_hidden_size)
)
self.img_context_token_id = None
self.conv_template = get_conv_template(self.template)
self.system_message = self.conv_template.system_message
def load_normed_tok_embeddings(self,vocab_size=92553, llm_hidden_size=4096,load_checkboard=False):
tok_embeddings = nn.Embedding(vocab_size, llm_hidden_size, padding_idx=2).to_empty(device=torch.device('cuda')).to(torch.bfloat16)
tok_embeddings.load_state_dict(torch.load(NORM_TOK_EMBEDDING_PATH, weights_only=True, map_location="cpu"))
if load_checkboard:
checkboard_norm=torch.load(NORM_PARAMS_PATH) # (voc_size, 2) mu sigma pred * sigma + mu (逐行)
return tok_embeddings,checkboard_norm['weight']
return tok_embeddings
def forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_flags: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
image_flags = image_flags.squeeze(-1)
input_embeds = self.language_model.get_input_embeddings()(input_ids)
vit_embeds = self.extract_feature(pixel_values)
vit_embeds = vit_embeds[image_flags == 1]
vit_batch_size = pixel_values.shape[0]
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
if torch.distributed.get_rank() == 0:
print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.img_context_token_id)
try:
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
except Exception as e:
vit_embeds = vit_embeds.reshape(-1, C)
print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
f'vit_embeds.shape={vit_embeds.shape}')
n_token = selected.sum()
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
input_embeds = input_embeds.reshape(B, N, C)
outputs = self.language_model(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = outputs.logits
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def pixel_shuffle(self, x, scale_factor=0.5):
n, w, h, c = x.size()
# N, W, H, C --> N, W, H * scale, C // scale
x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
x = x.permute(0, 2, 1, 3).contiguous()
# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
x = x.view(n, int(h * scale_factor), int(w * scale_factor),
int(c / (scale_factor * scale_factor)))
if self.ps_version == 'v1':
warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
'which results in a transposed image.')
else:
x = x.permute(0, 2, 1, 3).contiguous()
return x
def extract_feature(self, pixel_values):
if self.select_layer == -1:
vit_embeds = self.vision_model(
pixel_values=pixel_values,
output_hidden_states=False,
return_dict=True).last_hidden_state
else:
vit_embeds = self.vision_model(
pixel_values=pixel_values,
output_hidden_states=True,
return_dict=True).hidden_states[self.select_layer]
vit_embeds = vit_embeds[:, 1:, :]
h = w = int(vit_embeds.shape[1] ** 0.5)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
vit_embeds = self.mlp1(vit_embeds)
return vit_embeds
@torch.no_grad()
def calli_align(self,img_path,detect_model, drop_zero = False, use_hard_vector_quant=False,save_path=None,verbose=False):
def dynamic_read(img_path,mode='c'):
# 如果是字符串类型（文件路径），用 cv2 读取
if isinstance(img_path, str):
img = cv2.imread(img_path)
if img is None:
try:
img = Image.open(img_path).convert("RGB")
img = np.array(img)
except:
raise ValueError(f"Image at path {img_path} could not be loaded.")
# 如果是 PIL.Image.Image 类型，将其转为 cv2 格式
elif isinstance(img_path, Image.Image):
img = np.array(img_path) # PIL 转 numpy 数组
# 因为 OpenCV 是 BGR，需要将 RGB 转为 BGR
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
else:
raise TypeError(f"Unsupported image type: {type(img_path)}")
if mode=='i':
img=Image.fromarray(img).convert("RGB")
return img
import time
def iterative_only_boxes(model,jpg_path):
image = dynamic_read(jpg_path)
image_array = np.array(image)
h, w, channels = image.shape
boxes=[]
color=most_frequent_rgb_fast(image_array)
while True:
res=model(image_array,verbose=False)[0]
to_be_masked=[]
for box in res.boxes:
xyxy = box.xyxy.squeeze().tolist()
x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
to_be_masked.append([x1,y1,x2,y2])
boxes.extend(to_be_masked)
if len(to_be_masked)>250:
image_array=mask_area(image_array,to_be_masked,color)
else:
break
boxes=[[[max(item[0],0),max(item[1],0)],[min(item[2],w),min(item[3],h)]]for item in boxes]
i=0
length=len(boxes)
while i<length:
j=0
main_box=boxes[i]
while j<length:
if i==j:
j+=1
continue
iou=calculate_iou(coord_transform(main_box),coord_transform(boxes[j]))
if iou>0.8:
rm = boxes[j]
boxes.remove(rm)
if j<i:
i-=1
length-=1
j-=1
j+=1
i+=1
return boxes
def char2col_with_kmeans(jpg_path,boxes, verbose=False):
## modified
def kmeans_boxes(bounding_boxes):
areas = [ (box[1][0] - box[0][0])*(box[1][1] - box[0][1]) for box in bounding_boxes]
# 转换为 numpy 数组
areas = np.array(areas).reshape(-1, 1)
# 使用 KMeans 进行聚类，将面积分为两组
kmeans = KMeans(n_clusters=2, random_state=0).fit(areas)
# 获取每个 bounding box 的标签
labels = kmeans.labels_
# 根据标签将 bounding boxes 分成两个组
group_0 = []
group_1 = []
for i, label in enumerate(labels):
if label == 0:
group_0.append(bounding_boxes[i])
else:
group_1.append(bounding_boxes[i])
group_0 = sorted(group_0, key = lambda x: (x[1][0]-x[0][0]), reverse=True)
group_1 = sorted(group_1, key = lambda x: (x[1][0]-x[0][0]), reverse=True)
if (group_1[0][1][0] - group_1[0][0][0]) > (group_0[0][1][0] - group_0[0][0][0]):# and len(group_1) > 0.8*len(group_0): # 1 为正文，0为落款
g1_hs = np.array([x[1][1]-x[0][1] for x in group_1]).mean()
thr1 = 1*( group_1[-1][1][0] - group_1[-1][0][0])
thr2 = 0.8*g1_hs
#luokuan_mean_area = np.array([(ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1]) for ele in group_0]).mean()
new_0 = []
for ele in group_0:
if (ele[1][0] - ele[0][0]) >= thr1 or (ele[1][1] - ele[0][1]) >= thr2 or (areas.min()/(ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1]) <= 1/5 and areas.mean() / ((ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1])) <= 1.3):
group_1.append(ele)
else:
new_0.append(ele)
grouped_luokuan = merge_boxes(new_0.copy())
final_ = []
for ele in new_0:
if ele in grouped_luokuan:
group_1.append(ele)
else:
final_.append(ele)
group_0 = final_
elif (group_0[0][1][0] - group_0[0][0][0]) > (group_1[0][1][0] - group_1[0][0][0]):# and len(group_0) > 0.8*len(group_1):
g0_hs = np.array([x[1][1]-x[0][1] for x in group_0]).mean()
thr1 = 1*( group_0[-1][1][0] - group_0[-1][0][0])
thr2 = 0.8*g0_hs
#luokuan_mean_area = np.array([(ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1]) for ele in group_1]).mean()
new_1 = []
for ele in group_1:
if (ele[1][0] - ele[0][0]) >= thr1 or (ele[1][1] - ele[0][1]) >= thr2 or (areas.min()/(ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1]) <= 1/5 and areas.mean() / ((ele[1][0] - ele[0][0])*(ele[1][1] - ele[0][1])) <=1.3):
group_0.append(ele)
else:
new_1.append(ele)
grouped_luokuan = merge_boxes(new_1.copy())
final_ = []
for ele in new_1:
if ele in grouped_luokuan:
group_0.append(ele)
else:
final_.append(ele)
group_1 = final_
return group_0,group_1
def toint(lst):
if len(lst)==2:
return [[int(lst[0][0]),int(lst[0][1])],[int(lst[1][0]),int(lst[1][1])]]
else:
return [int(lst[0]),int(lst[1]),int(lst[2]),int(lst[3])]
img = dynamic_read(jpg_path)
h, w, channels = img.shape
normalized_boxes=[[[item[0][0]/w,item[0][1]/h],[item[1][0]/w,item[1][1]/h]] for item in boxes]
S=np.array([(item[0][0]-item[1][0])*(item[0][1]-item[1][1]) for item in normalized_boxes])
# print(np.max(S)-np.min(S),h,w)
# print(boxes)
# print(normalized_boxes)
coef_var=np.std(S)/np.mean(S)
boxes2class=None
col2class=None
if coef_var>0.66 and S.min()/S.mean() <= 1/8:
boxes1,boxes2=kmeans_boxes(normalized_boxes)
boxes1=[[[item[0][0]*w,item[0][1]*h],[item[1][0]*w,item[1][1]*h]] for item in boxes1]
boxes2=[[[item[0][0]*w,item[0][1]*h],[item[1][0]*w,item[1][1]*h]] for item in boxes2]
columns1=merge_boxes(boxes1.copy())
columns2=merge_boxes(boxes2.copy())
columns=columns1+columns2
boxes2class={1:[toint(item) for item in boxes1],2:[toint(item) for item in boxes2]}
col2class={1:[toint(item) for item in columns1],2:[toint(item) for item in columns2]}
#[[481.3252033886607, 1185.3073037637248], [748.9909909909909, 1616.216216216216]]
else:
columns=merge_boxes(boxes.copy())
results={"imageHeight":h,"imageWidth":w,"shapes":[{"points":toint(col)} for col in columns],
"boxes2class":boxes2class,"col2class":col2class}
#print("saving results...")
# if verbose:
# frame = dynamic_read(jpg_path)
# name=jpg_path.split("/")[-1]
# os.makedirs("./detect_boxes_char2col/result_merge", exist_ok=True)
# for i,box in enumerate(results['shapes']):
# xyxy = box['points']
# x1, y1, x2, y2 = int(xyxy[0][0]), int(xyxy[0][1]), int(xyxy[1][0]), int(xyxy[1][1])
# colo = (255,0,0)
# cv2.rectangle(frame, (x1, y1), (x2, y2), thickness=2,color=colo,lineType=cv2.LINE_AA)
# # put labels
# if boxes2class is not None:
# if xyxy in col2class[1]:
# cv2.putText(frame, str(1), ((x1+x2)//2, (y1+y2)//2), cv2.FONT_HERSHEY_SIMPLEX, 1.5, colo, thickness=2, lineType=cv2.LINE_AA)
# elif xyxy in col2class[2]:
# cv2.putText(frame, str(2), ((x1+x2)//2, (y1+y2)//2), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 30, 235), thickness=2, lineType=cv2.LINE_AA)
# #cv2.putText(frame, str(i+1), ((x1+x2)//2, (y1+y2)//2), cv2.FONT_HERSHEY_SIMPLEX, 1.5, colo, thickness=2, lineType=cv2.LINE_AA)
# cv2.imwrite("./detect_boxes_char2col/result_merge"+name,frame)
return results
def sort_boxes(jpg,detector,model,thres=0.8):
boxes=iterative_only_boxes(detector,jpg)
data=char2col_with_kmeans(jpg,boxes,verbose=False)
res=model.predict(data,jpg)
final_results=[]
for idx,col in res.items():
lst=[]
for item in boxes:
ratio=calculate_iou(col,[item[0][0],item[0][1],item[1][0],item[1][1]],mini=True)
if ratio>=thres:
lst.append([item[0][0],item[0][1],item[1][0],item[1][1]])
lst=sorted(lst, key=lambda item: (item[1]+item[3])/2)
final_results.extend(lst)
#print(len(boxes),len(res),len(final_results))
return final_results
if img_path is None:
return None,None
st=time.time()
boxes=sort_boxes(img_path,detect_model,self.sorter)
ed=time.time()
if verbose:
print(f"YOLO+Orderformer {ed-st:.2f}s")
if save_path!=None:
frame = dynamic_read(img_path)
name=img_path.split("/")[-1]
for i,box in enumerate(boxes):
xyxy = box
x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
colo = (255,0,0)
cv2.rectangle(frame, (x1, y1), (x2, y2), thickness=2,color=colo,lineType=cv2.LINE_AA)
# put labels
cv2.putText(frame, str(i+1), ((x1+x2)//2, (y1+y2)//2), cv2.FONT_HERSHEY_SIMPLEX, 1.5, colo, thickness=2, lineType=cv2.LINE_AA)
print(save_path+"oredered_result_"+name)
cv2.imwrite(save_path+"oredered_result_"+name,frame)
st=time.time()
pixel_values=[]
img=np.array(dynamic_read(img_path,mode='i').convert("RGB"))
for xyxy in boxes:
x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
sub_img=Image.fromarray(img[y1:y2,x1:x2])
pixel_values.append(load_image_2(sub_img).to(torch.bfloat16).cuda())
ed1=time.time()
results=torch.cat(pixel_values)
image_embeddings=self.extract_feature(results)
ed2=time.time()
output=self.resampler(image_embeddings)
ed3=time.time()
#TODO 可以indices转换回去
outs=vq_cos_sim(self.normed_emb,output, use_hard_vector_quant) #(B, 3) #如果use_vq的话现在改成dynamic: 对于max cos_sim小于等于thresh的，使用向量量化进行替换
ed4=time.time()
if verbose:
print(f"Get pixel values {ed1-st:.2f}s")
print(f"extract feat {ed2-ed1:.2f}s")
print(f"Resampler forward {ed3-ed2:.2f}")
print(f"vq cos sim {ed4-ed3:.2f}s")
if use_hard_vector_quant:
indices, cos_sim_values = outs
#### DEFINE THRESH!!!
thresh = 0.5
else:
indices = outs
if use_hard_vector_quant:
print("Dynamic vector quantization...")
below_mask = (cos_sim_values <= thresh).to(torch.bfloat16).unsqueeze(-1)
output = output * (1-below_mask) + self.normed_emb.weight[indices] * below_mask
flattened_output = output.view(-1, output.shape[-1])
flattened_indices = indices.view(-1)
if drop_zero:
filtered_indices=flattened_indices[flattened_indices!=0]
filtered_output=flattened_output[flattened_indices!=0]
sigma_flat = self.sigma[filtered_indices] # 形状 (183 * 3, 1)
mu_flat = self.mu[filtered_indices]
sigma_flat = sigma_flat.expand(-1, filtered_output.shape[-1])
mu_flat = mu_flat.expand(-1, filtered_output.shape[-1])
back_to_origin_flat = filtered_output * sigma_flat + mu_flat
else:
sigma_flat = self.sigma[flattened_indices]
mu_flat = self.mu[flattened_indices]
sigma_flat = sigma_flat.expand(-1, flattened_output.shape[-1])
mu_flat = mu_flat.expand(-1, flattened_output.shape[-1])
back_to_origin_flat = flattened_output * sigma_flat + mu_flat
return back_to_origin_flat, indices
def find_coordinates(self,text):
import re
numbers = re.findall(r'\d+', text)
numbers = [int(num) for num in numbers] # 如果需要浮点数，可以用 float()
return numbers
def chat_ocr(self, tokenizer, detect_model,img_path, questions, generation_config, num_patches_list=None,
history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', ALIGNED_TOKEN="[UNUSED_TOKEN_140]",verbose=False, image_counts=None,batch=False,
use_p=True, drop_zero=False, hard_vq=False, repetition_penalty=1.5,region_wise=False):
pixel_values = None
if img_path is not None:
try:
if region_wise:
img=np.array(Image.open(img_path).convert("RGB"))
coord=self.find_coordinates(questions)
x1,x2,y1,y2=coord
sub_img=Image.fromarray(img[y1:y2,x1:x2])
questions="输出图片中所有文字:"
pixel_values=load_image(sub_img).to(torch.bfloat16).to(torch.device("cuda"))
else:
pixel_values=load_image(img_path).to(torch.bfloat16).to(torch.device("cuda"))
except:
raise FileNotFoundError
if use_p:
import time
st=time.time()
if region_wise:
try:
out_tokens, indices =self.calli_align(sub_img,detect_model, drop_zero = drop_zero, use_hard_vector_quant=hard_vq,verbose=verbose)
except:
return "检测失败"
else:
out_tokens, indices =self.calli_align(img_path,detect_model, drop_zero = drop_zero, use_hard_vector_quant=hard_vq,verbose=verbose) #,tokenizer=tokenizer)
if verbose:
print(f"Calli Align: {time.time()-st:.2f}s")
# 删掉多余0
# indices 备用，因为我们也想未来看仅使用calliAlign效果
if pixel_values is None:
question=questions
if pixel_values is not None and '<image>' not in questions:
question = '<image>\n' + questions
#question = questions
elif history is None and pixel_values is None:
question=questions
elif '<image>' in questions:
question=questions
if history is None and use_p and '[UNUSED_TOKEN_140]' not in question:
question =question+'[UNUSED_TOKEN_140]'*out_tokens.shape[0]
if num_patches_list is None:
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
template = get_conv_template(self.template)
template.system_message = self.system_message
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
history = [] if history is None else history
for (old_question, old_answer) in history:
template.append_message(template.roles[0], old_question)
template.append_message(template.roles[1], old_answer)
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
for num_patches in num_patches_list:
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
query = query.replace('<image>', image_tokens, 1)
model_inputs = tokenizer(query, return_tensors='pt')
input_ids = model_inputs['input_ids'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
generation_config['eos_token_id'] = eos_token_id
if use_p:
generation_output = self.generate_ocr(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
reference_embeds=out_tokens,
repetition_penalty=repetition_penalty,
**generation_config
)
else:
generation_output = self.generate_ocr(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
repetition_penalty=repetition_penalty,
**generation_config
)
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
response = response.split(template.sep)[0].strip()
history.append((question, response))
if return_history:
return response, history
else:
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
return response
def dynamic_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None,batch=False,use_p=True):
if use_p:
self.num_image_token=3
if batch:
assert isinstance(questions,list) and len(questions)>0 and isinstance(questions[0],str)
if history is not None or return_history:
print('Now multi-turn chat is not supported in batch_chat.')
raise NotImplementedError
if image_counts is not None:
num_patches_list = image_counts
print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
print(f'dynamic ViT batch size: {image_bs}')
queries = []
for idx, num_patches in enumerate(num_patches_list):
question = questions[idx]
if pixel_values is not None and '<image>' not in question:
question = '<image>\n' + question
template = get_conv_template(self.template)
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
query = query.replace('<image>', image_tokens, 1)
queries.append(query)
# print(query)
tokenizer.padding_side = 'left'
model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
input_ids = model_inputs['input_ids'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
generation_config['eos_token_id'] = eos_token_id
if use_p:
generation_output = self.generate(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
else:
generation_output = self.generate_origin(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
responses = [response.split(template.sep)[0].strip() for response in responses]
return responses
else:
assert isinstance(questions,str)
if num_patches_list is None:
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
template = get_conv_template(self.template)
template.system_message = self.system_message
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
history = [] if history is None else history
for (old_question, old_answer) in history:
template.append_message(template.roles[0], old_question)
template.append_message(template.roles[1], old_answer)
template.append_message(template.roles[0], questions)
template.append_message(template.roles[1], None)
query = template.get_prompt()
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
print(f'dynamic ViT batch size: {image_bs}')
# print('num_image_token', self.num_image_token)
# print('num_patches_list', num_patches_list)
query=f"""<|im_start|>system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。<|im_end|>\n<|im_start|>user{questions}"""
query = query+'<image>'
for num_patches in num_patches_list:
#image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
image_tokens = IMG_CONTEXT_TOKEN * self.num_image_token
#print('tokens_num', len(image_tokens))
query = query.replace('<image>', image_tokens, 1)
query+="<|im_end|>\n<|im_start|>assistant"
# print(query)
model_inputs = tokenizer(query, return_tensors='pt')
input_ids = model_inputs['input_ids'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
generation_config['eos_token_id'] = eos_token_id
if use_p:
generation_output = self.generate(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
else:
generation_output = self.generate_origin(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
response = response.split(template.sep)[0].strip()
history.append((questions, response))
if return_history:
return response, history
else:
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
if verbose:
print(query_to_print, response)
return response
def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
if history is not None or return_history:
print('Now multi-turn chat is not supported in batch_chat.')
raise NotImplementedError
if image_counts is not None:
num_patches_list = image_counts
print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
print(f'dynamic ViT batch size: {image_bs}')
queries = []
for idx, num_patches in enumerate(num_patches_list):
question = questions[idx]
if pixel_values is not None and '<image>' not in question:
question = '<image>\n' + question
template = get_conv_template(self.template)
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
query = query.replace('<image>', image_tokens, 1)
queries.append(query)
# print(query)
tokenizer.padding_side = 'left'
model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
input_ids = model_inputs['input_ids'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
generation_config['eos_token_id'] = eos_token_id
generation_output = self.generate_origin(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
responses = [response.split(template.sep)[0].strip() for response in responses]
return responses
#When call internvl,this func is called
def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
verbose=False):
#self.num_image_token=3
# original_question = question
if history is None and pixel_values is not None and '<image>' not in question:
question = '<image>\n' + question
if num_patches_list is None:
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
template = get_conv_template(self.template)
template.system_message = self.system_message
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
history = [] if history is None else history
for (old_question, old_answer) in history:
template.append_message(template.roles[0], old_question)
template.append_message(template.roles[1], old_answer)
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
for num_patches in num_patches_list:
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
query = query.replace('<image>', image_tokens, 1)
print(num_patches,self.num_image_token)
print(pixel_values.shape[0])
model_inputs = tokenizer(query, return_tensors='pt')
input_ids = model_inputs['input_ids'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
generation_config['eos_token_id'] = eos_token_id
generation_output = self.generate_origin(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
response = response.split(template.sep)[0].strip()
history.append((question, response))
if return_history:
return response, history
else:
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
if verbose:
print(query_to_print, response)
return response
@torch.no_grad()
def generate_origin(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
visual_features: Optional[torch.FloatTensor] = None,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**generate_kwargs,
) -> torch.LongTensor:
assert self.img_context_token_id is not None
if pixel_values is not None:
if visual_features is not None:
vit_embeds = visual_features
else:
vit_embeds = self.extract_feature(pixel_values)
input_embeds = self.language_model.get_input_embeddings()(input_ids)
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.img_context_token_id)
assert selected.sum() != 0
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
print("ID: ",self.img_context_token_id)
input_embeds = input_embeds.reshape(B, N, C)
else:
input_embeds = self.language_model.get_input_embeddings()(input_ids)
outputs = self.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=True,
**generate_kwargs,
)
return outputs
@torch.no_grad()
def generate_ocr(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
visual_features: Optional[torch.FloatTensor] = None,
generation_config: Optional[GenerationConfig] = None,
reference_embeds=None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
repetition_penalty=1.5,
**generate_kwargs,
) -> torch.LongTensor:
assert self.img_context_token_id is not None
if pixel_values is not None:
if visual_features is not None:
vit_embeds = visual_features
else:
vit_embeds = self.extract_feature(pixel_values)
input_embeds = self.language_model.get_input_embeddings()(input_ids)
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.img_context_token_id)
assert selected.sum() != 0
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
if reference_embeds is not None:
selected = (input_ids == 92537)
assert selected.sum() != 0
input_embeds[selected] =reference_embeds.reshape(-1, C).to(input_embeds.device)
input_embeds = input_embeds.reshape(B, N, C)
else:
input_embeds = self.language_model.get_input_embeddings()(input_ids)
outputs = self.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=True,
repetition_penalty=repetition_penalty,
**generate_kwargs,
)
return outputs
@torch.no_grad()
def generate(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
visual_features: Optional[torch.FloatTensor] = None,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**generate_kwargs,
) -> torch.LongTensor:
assert self.img_context_token_id is not None
if pixel_values is not None:
if visual_features is not None:
vit_embeds = visual_features
else:
vit_embeds = self.extract_feature(pixel_values)
input_embeds = self.language_model.get_input_embeddings()(input_ids)
vit_embeds = self.resampler(vit_embeds)
mu=self.mu_sigma[:,0].reshape((-1,1))
sigma=self.mu_sigma[:,1].reshape((-1,1))
indices=vq_cos_sim(self.normed_emb,vit_embeds).reshape((-1,))
vit_embeds=vit_embeds.reshape((-1,vit_embeds.shape[-1]))*sigma[indices][:]+mu[indices][:]
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.img_context_token_id)
assert selected.sum() != 0
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
input_embeds = input_embeds.reshape(B, N, C)
else:
input_embeds = self.language_model.get_input_embeddings()(input_ids)
outputs = self.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=True,
**generate_kwargs,
)
return outputs
|