import argparse import torch from tqdm import tqdm import json from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from llava.conversation import conv_templates, SeparatorStyle from llava.model.builder import load_pretrained_model from llava.utils import disable_torch_init from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria from PIL import Image import requests from PIL import Image from io import BytesIO def load_image(image_file): if image_file.startswith('http') or image_file.startswith('https'): response = requests.get(image_file) image = Image.open(BytesIO(response.content)).convert('RGB') else: image = Image.open(image_file).convert('RGB') return image def eval_model(args): # Model disable_torch_init() model_name = get_model_name_from_path(args.model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, True) with open(args.questions_file) as f: llvqa_data = json.load(f) for i, llddata in enumerate(tqdm(llvqa_data)): filename = llddata["img_path"] if args.lang == "en": message = llddata["question"] + "\nChoose between one of the options as follows:\n" elif args.lang == "zh": message = llddata["question"] + "\在下列选项中选择一个:\n" else: raise NotImplementedError("Q-Bench does not support languages other than English (en) and Chinese (zh) yet. Contact us (https://github.com/VQAssessment/Q-Bench/) to convert Q-Bench into more languages.") for choice, ans in zip(["A.", "B.", "C.", "D."], llddata["candidates"]): message += f"{choice} {ans}\n" qs = message if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs if 'llama-2' in model_name.lower(): conv_mode = "llava_llama_2" elif "v1" in model_name.lower(): conv_mode = "llava_v1" elif "mpt" in model_name.lower(): conv_mode = "mpt" else: conv_mode = "llava_v0" if args.conv_mode is not None and conv_mode != args.conv_mode: print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) else: args.conv_mode = conv_mode conv = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = load_image(args.image_folder + filename) image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor, num_beams=1, do_sample=False, temperature=0, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() llddata["response"] = outputs with open(args.answers_file, "a") as wf: json.dump(llddata, wf) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="llava-v1.5") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-folder", type=str, default="./playground/data/qbench/images_llvisionqa") parser.add_argument("--questions-file", type=str, default="./playground/data/qbench/llvisionqa_dev.json") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v1") parser.add_argument("--lang", type=str, default="en") args = parser.parse_args() eval_model(args)