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llava_next_110B_all.py
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llava_next_110B_all.py
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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 collections import defaultdict
from PIL import Image
import math
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
class imageDataset(Dataset):
def __init__(self, data_list):
self.data_list = data_list
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
data = self.data_list[idx]
return data
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 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
# Apply prompt templates
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 gen_samples(root_dir):
for task in os.listdir(root_dir):
for sample_dir in os.listdir(os.path.join(root_dir, task)):
if not os.path.isdir(os.path.join(root_dir, task, sample_dir)):
continue
yield root_dir, task, sample_dir
def load_jsonl(path):
with open(path, 'r') as json_file:
json_list = list(json_file)
res = {}
for json_str in json_list:
result = json.loads(json_str)
# print(f"result: {result}")
# print(isinstance(result, dict))
res.update(result)
# print(res)
return res
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)
root_dir=args.root_dir
# Data
# 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")
# samples=gen_samples(root_dir)
tasks = os.listdir(root_dir)
tasks.reverse()
for task in tasks:
image_path=[]
# if task=='POPE' or task =='scienceqa':continue
if task!='scienceqa':continue
# for root, task, sample_dir in tqdm(samples):
file_path=os.path.join(root_dir,task,'question.jsonl')
with open(file_path,'r', encoding='utf-8') as jsonl_file:
try:
for line in jsonl_file:
data=json.loads(line)
image_path.append(data['image'])
except Exception as e:
continue
# 实例化数据集
dataset = imageDataset(image_path)
output_dict={}
# 使用 DataLoader 加载数据集
dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
for image_files in tqdm(dataloader, desc="Progress image description"):
text = DEFAULT_IMAGE_TOKEN+'\nPlease provide a description of the following image, You should consider elements in the image. '
# json_path = os.path.join(root_dir, task, sample_dir, 'qa.jsonl')
# json_data = load_jsonl(json_path)
# answer = json_data['answer']
# image1 = os.path.join(root_dir, task, sample_dir, 'image1.jpg')
# image2 = os.path.join(root_dir, task, sample_dir, 'image2.jpg')
# image_files=[image1,image2]
cur_prompt = args.extra_prompt + text
args.conv_mode = "qwen_1_5"
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
image_tensors = []
batch_input_ids=[]
description_dict=defaultdict(str)
for idx,image_file in enumerate(image_files):
input_ids = preprocess_qwen([{'from': 'human','value': prompt},{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda().to(model.device)
img_num = list(input_ids.squeeze()).count(IMAGE_TOKEN_INDEX)
image = Image.open(image_file)
# print(image_file)
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values']
image_tensors.append(image_tensor.half().cuda().to(model.device))
# image_tensors=[image_tensor.half().cuda().to(model.device)]
# image_tensors = torch.cat(image_tensors, dim=0)
batch_input_ids.append(input_ids)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
input_ids=torch.stack(batch_input_ids).squeeze(1)
image_tensors=torch.stack(image_tensors)
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,
# no_repeat_ngram_size=3,
max_new_tokens=256,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
for output,image_path in zip(outputs,image_files):
output = output.strip()
if output.endswith(stop_str):
output = output[:-len(stop_str)]
output = output.strip()
if output.startswith('<Description>: '):
output=output.replace('<Description>: ','')
# print(output)
output_dict[image_path] = output
save_path=os.path.join(root_dir,task,'VD.json')
with open(save_path,'w') as json_file:
json_data=json.dumps(output_dict)
json_file.write(json_data)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="/ML-A100/team/mm/zk/models/llava-next-110b")
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("--root-dir", type=str, default="/ML-A100/team/mm/zk/lmms-eval/vlms-bench-data")
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)