| | 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 llava.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IMAGE_TOKEN_INDEX |
| | from typing import Dict, Optional, Sequence, List |
| | import transformers |
| | import re |
| |
|
| | 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 preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict: |
| | roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"} |
| |
|
| | im_start, im_end = tokenizer.additional_special_tokens_ids |
| | nl_tokens = tokenizer("\n").input_ids |
| | _system = tokenizer("system").input_ids + nl_tokens |
| | _user = tokenizer("user").input_ids + nl_tokens |
| | _assistant = tokenizer("assistant").input_ids + nl_tokens |
| |
|
| | |
| | input_ids, targets = [], [] |
| |
|
| | source = sources |
| | if roles[source[0]["from"]] != roles["human"]: |
| | source = source[1:] |
| |
|
| | input_id, target = [], [] |
| | system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens |
| | input_id += system |
| | target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens |
| | assert len(input_id) == len(target) |
| | for j, sentence in enumerate(source): |
| | role = roles[sentence["from"]] |
| | if has_image and sentence["value"] is not None and "<image>" in sentence["value"]: |
| | num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"])) |
| | texts = sentence["value"].split('<image>') |
| | _input_id = tokenizer(role).input_ids + nl_tokens |
| | for i,text in enumerate(texts): |
| | _input_id += tokenizer(text).input_ids |
| | if i<len(texts)-1: |
| | _input_id += [IMAGE_TOKEN_INDEX] + nl_tokens |
| | _input_id += [im_end] + nl_tokens |
| | assert sum([i==IMAGE_TOKEN_INDEX for i in _input_id])==num_image |
| | else: |
| | if sentence["value"] is None: |
| | _input_id = tokenizer(role).input_ids + nl_tokens |
| | else: |
| | _input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens |
| | input_id += _input_id |
| | if role == "<|im_start|>user": |
| | _target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens |
| | elif role == "<|im_start|>assistant": |
| | _target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens |
| | else: |
| | raise NotImplementedError |
| | target += _target |
| |
|
| | input_ids.append(input_id) |
| | targets.append(target) |
| | input_ids = torch.tensor(input_ids, dtype=torch.long) |
| | targets = torch.tensor(targets, dtype=torch.long) |
| | return input_ids |
| |
|
| | 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) |
| |
|
| | |
| | with open(os.path.expanduser(args.question_file)) as f: |
| | questions = json.load(f) |
| | 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["sample_id"] |
| | question_type = line["metadata"]["question_type"] |
| | dataset_name = line["metadata"]["dataset"] |
| | gt = line["conversations"][1]["value"] |
| |
|
| | image_files = line["image"] |
| | qs = line["conversations"][0]["value"] |
| | cur_prompt = args.extra_prompt + qs |
| |
|
| | args.conv_mode = "qwen_1_5" |
| |
|
| | 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 = preprocess_qwen([line["conversations"][0],{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda() |
| | img_num = list(input_ids.squeeze()).count(IMAGE_TOKEN_INDEX) |
| |
|
| | image_tensors = [] |
| | for image_file in image_files: |
| | image = Image.open(os.path.join(args.image_folder, image_file)) |
| | image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'] |
| | image_tensors.append(image_tensor.half().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_tensors, |
| | do_sample=True if args.temperature > 0 else False, |
| | temperature=args.temperature, |
| | top_p=args.top_p, |
| | num_beams=args.num_beams, |
| | |
| | max_new_tokens=1024, |
| | use_cache=True) |
| |
|
| | |
| | outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] |
| | outputs = outputs.strip() |
| | if outputs.endswith(stop_str): |
| | outputs = outputs[:-len(stop_str)] |
| | outputs = outputs.strip() |
| |
|
| | ans_id = shortuuid.uuid() |
| | ans_file.write(json.dumps({ |
| | "dataset": dataset_name, |
| | "sample_id": idx, |
| | "prompt": cur_prompt, |
| | "pred_response": outputs, |
| | "gt_response": gt, |
| | "shortuuid": ans_id, |
| | "model_id": model_name, |
| | "question_type": question_type, |
| | }) + "\n") |
| | ans_file.flush() |
| |
|
| | if len(line["conversations"]) > 2: |
| |
|
| | for i in range(2, len(line["conversations"]), 2): |
| | input_ids = torch.cat((input_ids, output_ids), dim=1) |
| |
|
| | gt = line["conversations"][i + 1]["value"] |
| | qs = line["conversations"][i]["value"] |
| | cur_prompt = args.extra_prompt + qs |
| |
|
| | args.conv_mode = "qwen_1_5" |
| |
|
| | 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_new = preprocess_qwen([line["conversations"][i],{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda() |
| | input_ids = torch.cat((input_ids, input_ids_new), dim=1) |
| | img_num = list(input_ids_new.squeeze()).count(IMAGE_TOKEN_INDEX) |
| |
|
| | 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_tensors, |
| | do_sample=True if args.temperature > 0 else False, |
| | temperature=args.temperature, |
| | top_p=args.top_p, |
| | num_beams=args.num_beams, |
| | |
| | max_new_tokens=1024, |
| | use_cache=True) |
| | |
| | outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] |
| | outputs = outputs.strip() |
| | if outputs.endswith(stop_str): |
| | outputs = outputs[:-len(stop_str)] |
| | outputs = outputs.strip() |
| |
|
| | ans_id = shortuuid.uuid() |
| | ans_file.write(json.dumps({ |
| | "dataset": dataset_name, |
| | "sample_id": idx, |
| | "prompt": cur_prompt, |
| | "pred_response": outputs, |
| | "gt_response": gt, |
| | "shortuuid": ans_id, |
| | "model_id": model_name, |
| | "question_type": question_type, |
| | }) + "\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("--extra-prompt", type=str, default="") |
| | parser.add_argument("--question-file", type=str, default="tables/question.jsonl") |
| | parser.add_argument("--answers-file", type=str, default="answer.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) |
| | parser.add_argument("--test_size", type=int, default=10000000) |
| | args = parser.parse_args() |
| |
|
| | eval_model(args) |
