import os import subprocess # Install flash attention subprocess.run( "pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True, ) subprocess.run("nvidia-smi") from transformers import AutoTokenizer, AutoModel import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, 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(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 = 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 = 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(image_file, input_size=448, max_num=6): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = 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 path = "OpenGVLab/Mini-InternVL-Chat-2B-V1-5" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) # set the max number of tiles in `max_num` pixel_values = load_image('./examples/Screenshot 2024-05-15 212157.png', max_num=6).to(torch.bfloat16).cuda() generation_config = dict( num_beams=1, max_new_tokens=512, do_sample=False, ) # single-round single-image conversation question = "请详细描述图片" # Please describe the picture in detail response = model.chat(tokenizer, pixel_values, question, generation_config) print(question, response) # multi-round single-image conversation question = "请详细描述图片" # Please describe the picture in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "请根据图片写一首诗" # Please write a poem according to the picture response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # multi-round multi-image conversation pixel_values1 = load_image('./examples/Screenshot 2024-05-15 212157.png', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/Screenshot 2024-05-15 222516.png', max_num=6).to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) question = "详细描述这两张图片" # Describe the two pictures in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "这两张图片的相同点和区别分别是什么" # What are the similarities and differences between these two pictures response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # batch inference (single image per sample) pixel_values1 = load_image('./examples/Screenshot 2024-05-15 212157.png', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/Screenshot 2024-05-15 222516.png', max_num=6).to(torch.bfloat16).cuda() image_counts = [pixel_values1.size(0), pixel_values2.size(0)] pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) questions = ["Describe the image in detail."] * len(image_counts) responses = model.batch_chat(tokenizer, pixel_values, image_counts=image_counts, questions=questions, generation_config=generation_config) for question, response in zip(questions, responses): print(question) print(response)