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, get_model_name_from_path, KeywordsStoppingCriteria, ) 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.load(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 i, line in enumerate(tqdm(questions)): idx = line["id"] question = line["conversations"][0] qs = question["value"].replace("", "").strip() cur_prompt = qs if "image" in line: image_file = line["image"] image = Image.open(os.path.join(args.image_folder, image_file)) if isinstance(image_processor, list): image_tensor_0 = image_processor[0].preprocess( image, return_tensors="pt" )["pixel_values"][0] image_tensor_1 = image_processor[1].preprocess( image, return_tensors="pt" )["pixel_values"][0] image_tensor = torch.cat((image_tensor_0, image_tensor_1), dim=0) else: image_tensor = image_processor.preprocess(image, return_tensors="pt")[ "pixel_values" ][0] images = image_tensor.unsqueeze(0).bfloat16().cuda() if getattr(model.config, "mm_use_im_start_end", False): qs = ( DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs ) else: if "multiimg-template" in model_name: qs = "" + DEFAULT_IMAGE_TOKEN + "\n" + qs else: qs = DEFAULT_IMAGE_TOKEN + "\n" + qs if "multiimg-template" in model_name: cur_prompt = "" + "" + "\n" + cur_prompt else: cur_prompt = "" + "\n" + cur_prompt else: images = None if args.single_pred_prompt: qs = ( qs + "\n" + "Answer with the option's letter from the given choices directly." ) cur_prompt = ( cur_prompt + "\n" + "Answer with the option's letter from the given choices directly." ) 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() ) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = ( [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if conv.version == "v0" else None ) with torch.inference_mode(): output_ids = model.generate( input_ids, images=images, do_sample=True if args.temperature > 0 else False, temperature=args.temperature, 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() # prompt for answer if args.answer_prompter: outputs_reasoning = outputs input_ids = ( tokenizer_image_token( prompt + outputs_reasoning + " ###\nANSWER:", tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt", ) .unsqueeze(0) .cuda() ) with torch.inference_mode(): output_ids = model.generate( input_ids, images=images, do_sample=True if args.temperature > 0 else False, temperature=args.temperature, max_new_tokens=64, 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() outputs = outputs_reasoning + "\n The answer is " + outputs ans_id = shortuuid.uuid() ans_file.write( json.dumps( { "question_id": idx, "prompt": cur_prompt, "text": outputs, "answer_id": ans_id, "model_id": model_name, "metadata": {}, } ) + "\n" ) ans_file.flush() ans_file.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="facebook/opt-350m") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-folder", type=str, default="") parser.add_argument("--question-file", type=str, default="tables/question.json") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v0") 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("--answer-prompter", action="store_true") parser.add_argument("--single-pred-prompt", action="store_true") parser.add_argument("--regen", action="store_true", default=False) args = parser.parse_args() if os.path.exists(args.answers_file) and not args.regen: print("{} already exists, won't regen again.".format(args.answers_file)) else: eval_model(args)