convert_rouge_llava.py

import torch
from tqdm import tqdm
import os
import json
device = "cuda"

import argparse
import torch
import types
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 process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer

def load_image(image_file):
    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 main(args):
    with open(args.data_path, "r") as f:
        data = json.load(f)

    outputs_data_json = []
    dataset_name = args.data_path.split('/')[-1].split('.')[0]
    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)

    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"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
    else:
        args.conv_mode = conv_mode

    data = data[:args.eval_samples]

    for item in tqdm(data):
        conv = conv_templates[args.conv_mode].copy()
        if "mpt" in model_name.lower():
            roles = ('user', 'assistant')
        else:
            roles = conv.roles
        image_path = os.path.join(args.image_path, item["image"])
        question = item['question']
        if "mm-vet" in args.data_path:
            question = question + '\n' + DEFAULT_IMAGE_TOKEN
        answer = item['answer']

        output_data_json = {}
        output_data_json['image'] = item["image"]
        output_data_json['question'] = question
        

        image = load_image(image_path)
        image_tensor = process_images([image], image_processor, args)
        image_tensor = image_tensor.to(model.device, dtype=torch.bfloat16)

        conv.append_message(conv.roles[0], question)
        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()
        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

        answer_ids = tokenizer.encode(answer, return_tensors='pt').cuda()[:, 1:]
        past_key_values = None

        num_of_token = 0
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            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,
            stopping_criteria=[stopping_criteria])
    
        outputs_generate = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
        output_data_json['answer'] = outputs_generate
        outputs_data_json.append(output_data_json)

    with open('./playground/data/' + dataset_name + '/rouge-' + model_name + '-' + dataset_name+'.json', 'w') as f:
        json.dump(outputs_data_json, f, indent=4)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="./models/llava-v1.5-7b")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--data-path", type=str, default="./playground/data/detail_1k.json")
    parser.add_argument("--image-path", type=str, default="./playground/data")
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--num-chunks", type=int, default=1)
    parser.add_argument("--chunk-idx", type=int, default=0)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--num_beams", type=int, default=1)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
    parser.add_argument("--start-size", type=int, default=1)
    parser.add_argument("--recent-size", type=int, default=2047)
    parser.add_argument("--eval-samples", type=int, default=218)
    parser.add_argument("--exp-name", type=str, default='')
    args = parser.parse_args()
    main(args)