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chair_llava.py
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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from llava.constants import (
IMAGE_TOKEN_INDEX, # IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN, # DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IM_START_TOKEN, # DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN, # DEFAULT_IM_END_TOKEN = "<im_end>"
IMAGE_PLACEHOLDER, # IMAGE_PLACEHOLDER = "<image-placeholder>"
)
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, KeywordsStoppingCriteria
from PIL import Image
import base64
import requests
from io import BytesIO
import re
import math
import torch.distributed as dist
from utils import dist_util
from utils.logger import create_logger
from glob import glob
from transformers import set_seed
def image_parser(args):
out = args.image_file.split(args.sep)
return out
def load_image(image_file):
# 预处理
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
image = Image.open(image_file).convert("RGB")
return image
def load_images(image_files):
# 图片列表
out = []
for image_file in image_files:
image = load_image(image_file)
out.append(image)
return out
def eval_model(args):
# set up gpu and logging
dist_util.setup_dist(args)
device = dist_util.device()
# Setup an experiment folder:
if dist.get_rank() == 0:
os.makedirs(
args.log_path, exist_ok=True
) # Make results folder (holds all experiment subfolders)
model_string_name = args.model_path.split("/")[-1]
experiment_index = len(glob(f"{args.log_path}/{model_string_name}/*"))
experiment_dir = f"{args.log_path}" # Create an experiment folder
os.makedirs(experiment_dir, exist_ok=True)
logger = create_logger(experiment_dir)
logger.info(f"Experiment directory created at {experiment_dir}")
else:
logger = create_logger(None)
# 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,device="cuda:5")
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
with open("../opera_log/llava-1.5/ours.jsonl", "r", encoding="utf-8") as f:
for _, data_line in tqdm(enumerate(f.readlines()),total=500):
line = json.loads(data_line)
idx = line["image_id"]
image_file = ".../val2014/COCO_val2014_" + str(idx).zfill(12) + ".jpg"
qs = "Please describe this image in detail."#line["query"]
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
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(5)
image = Image.open(image_file)
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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():
with torch.no_grad():
output_dict = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(5),
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=args.max_new_tokens,
return_dict_in_generate=True,
output_hidden_states=True,
stopping_criteria=[stopping_criteria],
use_deco = True,
alpha = args.alpha,
threshold_top_p=args.threshold_top_p,
threshold_top_k=args.threshold_top_k,
early_exit_layers=[i for i in range(args.start_layer, args.end_layer)],
return_dict=True
)
output_ids = output_dict.sequences
input_token_len = input_ids.shape[1]
outputs = tokenizer.batch_decode(
output_ids[:, input_token_len:], skip_special_tokens=True
)[0]
outputs = outputs.strip()
logger.info(f"[{image_file}]")
logger.info(f"prompt: {cur_prompt}")
logger.info(f"text: {outputs}")
res_dict = {"id": idx,"response": outputs}
# ans_file.write(json.dumps(res_dict, ensure_ascii=False) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default=".../llava-v1.5-7b")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--answers-file", type=str, default="")
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=-1) # 可以考虑调大温度试试
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--top_k", type=int, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=512)
parser.add_argument("--log_path", type=str, default="...")
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--alpha", type=float, default=0.6)
parser.add_argument("--threshold_top_p", type=float, default=0.9)
parser.add_argument("--threshold_top_k", type=int, default=20)
parser.add_argument("--start_layer", type=int, default=20)
parser.add_argument("--end_layer", type=int, default=29)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
set_seed(args.seed)
eval_model(args)