import math import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode from transformers import AutoConfig, AutoTokenizer, AutoModel import os from utils.utils import COUNTING_BASE_PROMPT, RELATION_BASE_PROMPT, parse_model_answer, eval_loop, eval_pipeline current_dir = os.path.dirname(os.path.abspath(__file__)) parent_dir = os.path.split(current_dir)[0] 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=12, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height 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]) target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) 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] 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_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=12): 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 def split_model(path): device_map = {} world_size = torch.cuda.device_count() config = AutoConfig.from_pretrained(path, trust_remote_code=True) num_layers = config.llm_config.num_hidden_layers num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) num_layers_per_gpu = [num_layers_per_gpu] * world_size num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) layer_cnt = 0 for i, num_layer in enumerate(num_layers_per_gpu): for j in range(num_layer): device_map[f'language_model.model.layers.{layer_cnt}'] = i layer_cnt += 1 device_map['vision_model'] = 0 device_map['mlp1'] = 0 device_map['language_model.model.tok_embeddings'] = 0 device_map['language_model.model.embed_tokens'] = 0 device_map['language_model.output'] = 0 device_map['language_model.model.norm'] = 0 device_map['language_model.model.rotary_emb'] = 0 device_map['language_model.lm_head'] = 0 device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 return device_map def process(model, item, task_type="counting", **kwargs): """ :param model: :param item: :param task_type: :param kwargs: :return: """ generation_config = kwargs.get('generation_config') tokenizer = kwargs.get('tokenizer') question = item['question'] image_path = os.path.join(parent_dir, item['image_path']) if task_type == "counting": formatted_question = COUNTING_BASE_PROMPT + question else: # relation task formatted_question = RELATION_BASE_PROMPT + question pixel_values = load_image(image_path, max_num=12).to(torch.bfloat16).cuda() model_answer = model.chat(tokenizer, pixel_values, formatted_question, generation_config ) parsed_answer = parse_model_answer(model_answer, task_type) result = {'model_answer': model_answer, 'parsed_answer': parsed_answer} return result def main(): """ InternVL3 """ model_name_or_path = 'OpenGVLab/InternVL3-2B' model_name = model_name_or_path.split('/')[-1] cache_dir = './cache/' device_map = split_model(model_name_or_path) model = AutoModel.from_pretrained( model_name_or_path, torch_dtype=torch.bfloat16, load_in_8bit=False, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True, cache_dir=cache_dir, device_map=device_map).eval() tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, use_fast=False) generation_config = dict(max_new_tokens=1024, do_sample=True, pad_token_id=tokenizer.convert_tokens_to_ids(tokenizer.pad_token)) params = { 'model': model, 'tokenizer': tokenizer, 'generation_config': generation_config, 'process_fn': process, } eval_pipeline(model_name, current_dir, params) if __name__ == "__main__": main()