llava/eval/model_vqa_loader.py

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,
    process_images,
    get_model_name_from_path,
)
from torch.utils.data import Dataset, DataLoader

from PIL import Image
import math


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]


# Custom dataset class
class CustomDataset(Dataset):
    def __init__(
        self,
        questions,
        image_folder,
        tokenizer,
        image_processor,
        model_config,
        model_name,
    ):
        self.questions = questions
        self.image_folder = image_folder
        self.tokenizer = tokenizer
        self.image_processor = image_processor
        self.model_config = model_config
        self.model_name = model_name

    def __getitem__(self, index):
        line = self.questions[index]
        image_file = line["image"]
        if 'question' in line:
            qs = line['question']
        else:
            qs = line["text"]
        if self.model_config.mm_use_im_start_end:
            qs = (
                DEFAULT_IM_START_TOKEN
                + DEFAULT_IMAGE_TOKEN
                + DEFAULT_IM_END_TOKEN
                + "\n"
                + qs
            )
        else:
            if "multiimg-template" in self.model_name:
                qs = "<img_0>" + DEFAULT_IMAGE_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()

        image = Image.open(os.path.join(self.image_folder, image_file)).convert("RGB")
        if isinstance(self.image_processor, list):
            image_tensor_0 = process_images(
                [image], self.image_processor[0], self.model_config
            )[0]
            image_tensor_1 = process_images(
                [image], self.image_processor[1], self.model_config
            )[0]
            image_tensor = torch.cat((image_tensor_0, image_tensor_1), dim=0)
        else:
            image_tensor = process_images(
                [image], self.image_processor, self.model_config
            )[0]

        input_ids = tokenizer_image_token(
            prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
        )

        return input_ids, image_tensor

    def __len__(self):
        return len(self.questions)


# DataLoader
def create_data_loader(
    questions,
    image_folder,
    tokenizer,
    image_processor,
    model_config,
    model_name,
    batch_size=1,
    num_workers=4,
):
    assert batch_size == 1, "batch_size must be 1"
    dataset = CustomDataset(
        questions, image_folder, tokenizer, image_processor, model_config, model_name
    )
    data_loader = DataLoader(
        dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False
    )
    return data_loader


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
    )

    questions = [
        json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")
    ]
    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")

    if (
        "plain" in model_name
        and "finetune" not in model_name.lower()
        and "mmtag" not in args.conv_mode
    ):
        args.conv_mode = args.conv_mode + "_mmtag"
        print(
            f"It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}."
        )
    data_loader = create_data_loader(
        questions,
        args.image_folder,
        tokenizer,
        image_processor,
        model.config,
        model_name,
    )

    for (input_ids, image_tensor), line in tqdm(
        zip(data_loader, questions), total=len(questions)
    ):
        idx = line["question_id"]
        cur_prompt = line["text"]

        stop_str = (
            conv_templates[args.conv_mode].sep
            if conv_templates[args.conv_mode].sep_style != SeparatorStyle.TWO
            else conv_templates[args.conv_mode].sep2
        )
        input_ids = input_ids.to(device="cuda", non_blocking=True)

        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor.to(
                    dtype=torch.bfloat16, device="cuda", non_blocking=True
                ),
                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=128,
                use_cache=True,
            )

        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()

        ans_id = shortuuid.uuid()
        ans_file.write(
            json.dumps(
                {
                    "question_id": idx,
                    "prompt": cur_prompt,
                    "text": outputs,
                    "answer_id": ans_id,
                    "model_id": model_name,
                    "metadata": {},
                }
            )
            + "\n"
        )
        # ans_file.flush()
    ans_file.close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    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="tables/question.jsonl")
    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
    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("--regen", action="store_true", default=False)
    args = parser.parse_args()
    if os.path.exists(args.answers_file) and not args.regen:
        print("{} already exists, won't regen again.".format(args.answers_file))
    else:
        eval_model(args)