pope_llava.py

import argparse
import torch
import os
import json
from tqdm import tqdm
import requests
from io import BytesIO
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, 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, process_images

from PIL import Image
import math
import re
# import kornia
from transformers import set_seed
# from vcd_utils.vcd_add_noise import add_diffusion_noise
# from vcd_utils.vcd_sample import evolve_vcd_sampling
# evolve_vcd_sampling()
def recorder(out):
    word_list = re.split(r'[^\w]+', out.lower())
    if "yes" in word_list:
        return "Yes"
    else:
        return "No"
    
# 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):
    # 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")

    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
    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["question_id"]
        image_file = line["image"]
        qs = line["text"]
        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

        
        if "llama-2" in model_name.lower():
            conv_mode = "llava_llama_2"
        elif "v1" in model_name.lower():
            conv_mode = "llava_v1"
        elif "mpt" in model_name.lower():
            conv_mode = "mpt"
        else:
            conv_mode = "llava_v0"
        conv = conv_templates[conv_mode].copy()
        conv.append_message(conv.roles[0], qs)# + " Please answer this question with one word.")
        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_files = [image_file]
        images = load_images(image_files)
        images_tensor = process_images(
                images,
                image_processor, # clip
                model.config
            ).to(model.device, dtype=torch.float16)
            

        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_dict = model.generate(
                input_ids,
                images=images_tensor,
                do_sample=True if args.temperature > 0 else False,
                temperature=args.temperature,
                top_p=args.top_p,
                top_k=args.top_k,
                max_new_tokens=5,
                use_deco=True,
                alpha = args.aplha,
                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)],
                output_hidden_states=True,
                return_dict_in_generate=True,
                stopping_criteria=[stopping_criteria])
        output_ids = output_dict.sequences
        input_token_len = input_ids.shape[1]
        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
        if n_diff_input_output > 0:
            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
        outputs = outputs.strip()
        if outputs.endswith(stop_str):
            outputs = outputs[:-len(stop_str)]
        outputs = outputs.strip()
        print(idx)
        print(outputs)

        ans_file.write(json.dumps({"question_id": idx,
                                   "prompt": cur_prompt,
                                   "text": recorder(outputs),
                                   "model_id": model_name,
                                   "image": image_file,
                                   "metadata": {}}) + "\n")
        ans_file.flush()
    ans_file.close()

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("--image-folder", type=str, default="")
    parser.add_argument("--question-file", type=str, default="")
    parser.add_argument("--conv-mode", type=str, default=None)
    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)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--top_k", type=int, default=None)
    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)