import argparse import torch import os import json from tqdm import tqdm import shortuuid 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, process_images, get_model_name_from_path from PIL import Image import math def split_list(lst, n): """Split a list into n (roughly) equal-sized chunks""" chunk_size = math.ceil(len(lst) / n) # integer division return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] def get_chunk(lst, n, k): chunks = split_list(lst, n) return chunks[k] def eval_model(args): # Model disable_torch_init() model_path = os.path.expanduser(args.model_path) model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] questions = get_chunk(questions, args.num_chunks, args.chunk_idx) answers_file = os.path.expanduser(args.answers_file) os.makedirs(os.path.dirname(answers_file), exist_ok=True) ans_file = open(answers_file, "w") for line in tqdm(questions): idx = line["id"] image_file = line["image"] qs = line["text"] if 'box' in line: box=line["box"] else: box="" cur_prompt = qs 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 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() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() image = Image.open(os.path.join(args.image_folder, image_file)).convert('RGB') # print("DEBUG",model.config) image_tensor, image_new_size = process_images([image], image_processor, model.config) # image_tensor,image_new_size = process_images([image], image_processor, model.config)[0] with torch.inference_mode(): output_ids = model.generate( input_ids, # images=image_tensor.unsqueeze(0).half().cuda(), images=image_tensor.half().cuda(), image_sizes=[image_new_size], do_sample=True if args.temperature > 0 else False, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, # no_repeat_ngram_size=3, max_new_tokens=16384, use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() metadata = {k: v for k, v in line.items() if k not in ["id", "image", "text"]} ans_id = shortuuid.uuid() ans_file.write(json.dumps({"question_id": idx, 'image': image_file, "prompt": cur_prompt, "text": outputs, "answer_id": ans_id, "model_id": model_name, "box": box, "metadata": metadata}) + "\n") ans_file.flush() ans_file.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="/fs/ess/PAS1576/boyu_gou/train_vlm/ui_llava_fine_tune/checkpoints/ui-llava-ocr-text/merged-llava-v1.5-vicuna-7b-16k-pad-fusion-ocr-100k-text-1-200k-mobile-aug-1-200k") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-folder", type=str, default="/fs/ess/PAS1576/boyu_gou/Benchmark/screenspot_imgs_resized/") parser.add_argument("--question-file", type=str, default="/fs/ess/PAS1576/boyu_gou/Benchmark/screenspot_web_text.jsonl") parser.add_argument("--answers-file", type=str, default="/fs/ess/PAS1576/boyu_gou/Benchmark/answer_screenspot_web.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v1") parser.add_argument("--num-chunks", type=int, default=1) parser.add_argument("--chunk-idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.2) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) args = parser.parse_args() eval_model(args)