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import argparse
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import torch
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import os
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import json
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from tqdm import tqdm
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import shortuuid
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.model.builder import load_pretrained_model
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from llava.utils import disable_torch_init
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from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
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from llava.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IMAGE_TOKEN_INDEX
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from typing import Dict, Optional, Sequence, List
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import transformers
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import re
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from PIL import Image
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import math
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def split_list(lst, n):
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"""Split a list into n (roughly) equal-sized chunks"""
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chunk_size = math.ceil(len(lst) / n)
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return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
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def get_chunk(lst, n, k):
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chunks = split_list(lst, n)
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return chunks[k]
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def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
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roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}
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im_start, im_end = tokenizer.additional_special_tokens_ids
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nl_tokens = tokenizer("\n").input_ids
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_system = tokenizer("system").input_ids + nl_tokens
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_user = tokenizer("user").input_ids + nl_tokens
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_assistant = tokenizer("assistant").input_ids + nl_tokens
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input_ids, targets = [], []
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source = sources
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if roles[source[0]["from"]] != roles["human"]:
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source = source[1:]
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input_id, target = [], []
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system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
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input_id += system
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target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
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assert len(input_id) == len(target)
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for j, sentence in enumerate(source):
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role = roles[sentence["from"]]
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if has_image and sentence["value"] is not None and "<image>" in sentence["value"]:
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num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
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texts = sentence["value"].split('<image>')
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_input_id = tokenizer(role).input_ids + nl_tokens
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for i,text in enumerate(texts):
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_input_id += tokenizer(text).input_ids
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if i<len(texts)-1:
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_input_id += [IMAGE_TOKEN_INDEX] + nl_tokens
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_input_id += [im_end] + nl_tokens
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assert sum([i==IMAGE_TOKEN_INDEX for i in _input_id])==num_image
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else:
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if sentence["value"] is None:
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_input_id = tokenizer(role).input_ids + nl_tokens
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else:
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_input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
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input_id += _input_id
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if role == "<|im_start|>user":
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_target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
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elif role == "<|im_start|>assistant":
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_target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
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else:
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raise NotImplementedError
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target += _target
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input_ids.append(input_id)
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targets.append(target)
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input_ids = torch.tensor(input_ids, dtype=torch.long)
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targets = torch.tensor(targets, dtype=torch.long)
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return input_ids
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def eval_model(args):
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disable_torch_init()
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model_path = os.path.expanduser(args.model_path)
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model_name = get_model_name_from_path(model_path)
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tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
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with open(os.path.expanduser(args.question_file)) as f:
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questions = json.load(f)
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questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
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answers_file = os.path.expanduser(args.answers_file)
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os.makedirs(os.path.dirname(answers_file), exist_ok=True)
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ans_file = open(answers_file, "w")
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for line in tqdm(questions):
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idx = line["sample_id"]
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question_type = line["metadata"]["question_type"]
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dataset_name = line["metadata"]["dataset"]
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gt = line["conversations"][1]["value"]
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image_files = line["image"]
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qs = line["conversations"][0]["value"]
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cur_prompt = args.extra_prompt + qs
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args.conv_mode = "qwen_1_5"
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conv = conv_templates[args.conv_mode].copy()
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conv.append_message(conv.roles[0], qs)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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input_ids = preprocess_qwen([line["conversations"][0],{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda()
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img_num = list(input_ids.squeeze()).count(IMAGE_TOKEN_INDEX)
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image_tensors = []
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for image_file in image_files:
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image = Image.open(os.path.join(args.image_folder, image_file))
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values']
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image_tensors.append(image_tensor.half().cuda())
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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keywords = [stop_str]
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensors,
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do_sample=True if args.temperature > 0 else False,
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temperature=args.temperature,
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top_p=args.top_p,
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num_beams=args.num_beams,
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max_new_tokens=1024,
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use_cache=True)
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outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
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outputs = outputs.strip()
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if outputs.endswith(stop_str):
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outputs = outputs[:-len(stop_str)]
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outputs = outputs.strip()
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ans_id = shortuuid.uuid()
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ans_file.write(json.dumps({
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"dataset": dataset_name,
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"sample_id": idx,
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"prompt": cur_prompt,
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"pred_response": outputs,
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"gt_response": gt,
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"shortuuid": ans_id,
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"model_id": model_name,
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"question_type": question_type,
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}) + "\n")
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ans_file.flush()
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if len(line["conversations"]) > 2:
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for i in range(2, len(line["conversations"]), 2):
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input_ids = torch.cat((input_ids, output_ids), dim=1)
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gt = line["conversations"][i + 1]["value"]
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qs = line["conversations"][i]["value"]
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cur_prompt = args.extra_prompt + qs
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args.conv_mode = "qwen_1_5"
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conv = conv_templates[args.conv_mode].copy()
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conv.append_message(conv.roles[0], qs)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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input_ids_new = preprocess_qwen([line["conversations"][i],{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda()
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input_ids = torch.cat((input_ids, input_ids_new), dim=1)
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img_num = list(input_ids_new.squeeze()).count(IMAGE_TOKEN_INDEX)
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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keywords = [stop_str]
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensors,
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do_sample=True if args.temperature > 0 else False,
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temperature=args.temperature,
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top_p=args.top_p,
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num_beams=args.num_beams,
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max_new_tokens=1024,
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use_cache=True)
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outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
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outputs = outputs.strip()
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if outputs.endswith(stop_str):
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outputs = outputs[:-len(stop_str)]
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outputs = outputs.strip()
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ans_id = shortuuid.uuid()
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ans_file.write(json.dumps({
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"dataset": dataset_name,
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"sample_id": idx,
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"prompt": cur_prompt,
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"pred_response": outputs,
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"gt_response": gt,
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"shortuuid": ans_id,
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"model_id": model_name,
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"question_type": question_type,
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}) + "\n")
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ans_file.flush()
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ans_file.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
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parser.add_argument("--model-base", type=str, default=None)
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parser.add_argument("--image-folder", type=str, default="")
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parser.add_argument("--extra-prompt", type=str, default="")
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parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
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parser.add_argument("--answers-file", type=str, default="answer.jsonl")
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parser.add_argument("--conv-mode", type=str, default="llava_v1")
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parser.add_argument("--num-chunks", type=int, default=1)
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parser.add_argument("--chunk-idx", type=int, default=0)
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parser.add_argument("--temperature", type=float, default=0.2)
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parser.add_argument("--top_p", type=float, default=None)
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parser.add_argument("--num_beams", type=int, default=1)
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parser.add_argument("--test_size", type=int, default=10000000)
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args = parser.parse_args()
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eval_model(args) |
