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1import numpy as np
2import torch
3import torchvision.transforms as T
4from PIL import Image
5from torchvision.transforms.functional import InterpolationMode
6from transformers import AutoModel, AutoTokenizer
7
8IMAGENET_MEAN = (0.485, 0.456, 0.406)
9IMAGENET_STD = (0.229, 0.224, 0.225)
10
11def build_transform(input_size):
12 MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
13 transform = T.Compose([
14 T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
15 T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
16 T.ToTensor(),
17 T.Normalize(mean=MEAN, std=STD)
18 ])
19 return transform
20
21def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
22 best_ratio_diff = float('inf')
23 best_ratio = (1, 1)
24 area = width * height
25 for ratio in target_ratios:
26 target_aspect_ratio = ratio[0] / ratio[1]
27 ratio_diff = abs(aspect_ratio - target_aspect_ratio)
28 if ratio_diff < best_ratio_diff:
29 best_ratio_diff = ratio_diff
30 best_ratio = ratio
31 elif ratio_diff == best_ratio_diff:
32 if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
33 best_ratio = ratio
34 return best_ratio
35
36def dynamic_preprocess(image, min_num=1, max_num=6, image_size=672, use_thumbnail=False):
37 orig_width, orig_height = image.size
38 aspect_ratio = orig_width / orig_height
39
40 # calculate the existing image aspect ratio
41 target_ratios = set(
42 (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
43 i * j <= max_num and i * j >= min_num)
44 target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
45
46 # find the closest aspect ratio to the target
47 target_aspect_ratio = find_closest_aspect_ratio(
48 aspect_ratio, target_ratios, orig_width, orig_height, image_size)
49
50 # calculate the target width and height
51 target_width = image_size * target_aspect_ratio[0]
52 target_height = image_size * target_aspect_ratio[1]
53 blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
54
55 # resize the image
56 resized_img = image.resize((target_width, target_height))
57 processed_images = []
58 for i in range(blocks):
59 box = (
60 (i % (target_width // image_size)) * image_size,
61 (i // (target_width // image_size)) * image_size,
62 ((i % (target_width // image_size)) + 1) * image_size,
63 ((i // (target_width // image_size)) + 1) * image_size
64 )
65 # split the image
66 split_img = resized_img.crop(box)
67 processed_images.append(split_img)
68 assert len(processed_images) == blocks
69 if use_thumbnail and len(processed_images) != 1:
70 thumbnail_img = image.resize((image_size, image_size))
71 processed_images.append(thumbnail_img)
72 return processed_images
73
74def load_image(image_file, input_size=672, max_num=6):
75 image = Image.open(image_file).convert('RGB')
76 transform = build_transform(input_size=input_size)
77 images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
78 pixel_values = [transform(image) for image in images]
79 pixel_values = torch.stack(pixel_values)
80 return pixel_values
81
82# Load model and tokenizer
83model_path = 'tiendoan/Vintern-1B-v3_5-ocr-bill'
84model = AutoModel.from_pretrained(
85 model_path,
86 torch_dtype=torch.bfloat16,
87 low_cpu_mem_usage=True,
88 trust_remote_code=True,
89 use_flash_attn=False,
90).eval().cuda()
91
92tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
93
94# Load and process image
95test_image = 'bill.jpg'
96pixel_values = load_image(test_image, max_num=4).to(torch.bfloat16).cuda()
97
98# Generation config
99generation_config = dict(max_new_tokens=1024, do_sample=False, num_beams=3, repetition_penalty=2.5)
100
101# Ask question
102question = '<image>\nTrích xuất thông tin hoá đơn trong ảnh.'
103response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
104print(f'User: {question}
105Assistant: {response}')
106
107# For follow-up questions
108# question = "Câu hỏi khác ......"
109# response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
110# print(f'User: {question}
111Assistant: {response}')