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from transformers import AutoTokenizer, AutoModel |
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import torch |
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import json |
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import os |
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from tqdm import tqdm |
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import torchvision.transforms as T |
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from PIL import Image |
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from torchvision.transforms.functional import InterpolationMode |
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from time import sleep |
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IMAGENET_MEAN = (0.485, 0.456, 0.406) |
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IMAGENET_STD = (0.229, 0.224, 0.225) |
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def build_transform(input_size): |
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
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transform = T.Compose([ |
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
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T.ToTensor(), |
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T.Normalize(mean=MEAN, std=STD) |
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]) |
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return transform |
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
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best_ratio_diff = float('inf') |
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best_ratio = (1, 1) |
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area = width * height |
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for ratio in target_ratios: |
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target_aspect_ratio = ratio[0] / ratio[1] |
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ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
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if ratio_diff < best_ratio_diff: |
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best_ratio_diff = ratio_diff |
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best_ratio = ratio |
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elif ratio_diff == best_ratio_diff: |
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
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best_ratio = ratio |
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return best_ratio |
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def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): |
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orig_width, orig_height = image.size |
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aspect_ratio = orig_width / orig_height |
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target_ratios = set( |
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(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 |
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i * j <= max_num and i * j >= min_num) |
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
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target_aspect_ratio = find_closest_aspect_ratio( |
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aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
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target_width = image_size * target_aspect_ratio[0] |
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target_height = image_size * target_aspect_ratio[1] |
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
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resized_img = image.resize((target_width, target_height)) |
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processed_images = [] |
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for i in range(blocks): |
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box = ( |
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(i % (target_width // image_size)) * image_size, |
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(i // (target_width // image_size)) * image_size, |
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((i % (target_width // image_size)) + 1) * image_size, |
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((i // (target_width // image_size)) + 1) * image_size |
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) |
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split_img = resized_img.crop(box) |
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processed_images.append(split_img) |
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assert len(processed_images) == blocks |
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if use_thumbnail and len(processed_images) != 1: |
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thumbnail_img = image.resize((image_size, image_size)) |
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processed_images.append(thumbnail_img) |
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return processed_images |
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def load_image(image_file, input_size=448, max_num=6): |
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return transform_img(Image.open(image_file)) |
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def transform_img(image, input_size=448, max_num=6): |
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image = image.convert('RGB') |
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transform = build_transform(input_size=input_size) |
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
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pixel_values = [transform(image) for image in images] |
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pixel_values = torch.stack(pixel_values) |
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return pixel_values |
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model = AutoModel.from_pretrained( |
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"failspy/InternVL-Chat-V1-5-8bit", |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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device_map='auto').eval() |
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tokenizer = AutoTokenizer.from_pretrained("failspy/InternVL-Chat-V1-5-8bit", trust_remote_code=True) |
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pixel_values = load_image("./TestImage.jpg").to(torch.bfloat16).cuda() |
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generation_config = dict(num_beams=1, max_new_tokens=512, do_sample=False) |
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question = "What is shown in this image?" |
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response = model.chat(tokenizer,pixel_values,question,generation_config) |
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