| | import argparse |
| | import torch |
| | import os |
| | import json |
| | from tqdm import tqdm |
| | import shortuuid |
| |
|
| | from llava.constants import MM_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) |
| | 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): |
| | |
| | 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('<image>', '').strip() |
| | cur_prompt = qs |
| |
|
| | if 'image' in line: |
| | image_file = line["image"] |
| | image = Image.open(os.path.join(args.image_folder, image_file)) |
| | image_tensor = process_images([image], image_processor, model.config)[0] |
| | images = image_tensor.unsqueeze(0).half().cuda() |
| | image_sizes = [image.size] |
| | if getattr(model.config, 'mm_use_start_end', False): |
| | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs |
| | else: |
| | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs |
| | cur_prompt = '<image>' + '\n' + cur_prompt |
| | else: |
| | images = None |
| | image_sizes = 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, MM_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() |
| |
|
| | with torch.inference_mode(): |
| | output_ids = model.generate( |
| | input_ids, |
| | images=images, |
| | image_sizes=image_sizes, |
| | do_sample=True if args.temperature > 0 else False, |
| | temperature=args.temperature, |
| | max_new_tokens=1024, |
| | use_cache=True, |
| | ) |
| |
|
| | outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() |
| |
|
| | 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") |
| | args = parser.parse_args() |
| |
|
| | eval_model(args) |
| |
|
