ola_vlm/eval/model_mmstar_loader.py

import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid

from ola_vlm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from ola_vlm.conversation import conv_templates, SeparatorStyle
from ola_vlm.model.builder import load_pretrained_model
from ola_vlm.utils import disable_torch_init
from ola_vlm.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from torch.utils.data import Dataset, DataLoader
from datasets import load_dataset
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]


def prepare_MMStar(path):
    os.makedirs(f"{path}/images", exist_ok=True)
    dataset = load_dataset(path, "val")
    dataset = dataset["val"]
    data = []
    for i in range(len(dataset)):
        if not os.path.exists(f"{path}/images/{i}.jpeg"):
            dataset[i]["image"].save(f"{path}/images/{i}.jpeg")
        prompt = dataset[i]["question"] + "\n"
        prompt += "Answer with the option's letter from the given choices directly, such as answer letter 'A' only. \n"
        
        d = {
            "image": f"{path}/images/{i}.jpeg",
            "question": prompt,
            "answer": dataset[i]["answer"],
            "category": dataset[i]["category"],
            "l2_category": dataset[i]["l2_category"]
        }
        data.append(d)
    return data


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

    def __getitem__(self, index):
        d = self.questions[index]
        qs = d["question"]
        image_file = d["image"]
        ans = d["answer"]

        if self.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()

        image = Image.open(image_file).convert('RGB')
        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, image.size, ans, d["category"], d["l2_category"]

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


def collate_fn(batch):
    input_ids, image_tensors, image_sizes, answers, cats, cats_l2 = zip(*batch)
    input_ids = torch.stack(input_ids, dim=0)
    image_tensors = torch.stack(image_tensors, dim=0)
    return input_ids, image_tensors, image_sizes, answers, cats, cats_l2


# DataLoader
def create_data_loader(questions, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
    assert batch_size == 1, "batch_size must be 1"
    dataset = CustomDataset(questions, tokenizer, image_processor, model_config)
    data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, collate_fn=collate_fn)
    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 = prepare_MMStar(args.path)
    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, tokenizer, image_processor, model.config)

    for (input_ids, image_tensor, image_sizes, answer, cat, cat_l2), line in tqdm(zip(data_loader, questions), total=len(questions)):
        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.float16, device='cuda', non_blocking=True),
                image_sizes=image_sizes,
                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,
                use_cache=True)

        if not isinstance(output_ids, torch.Tensor):
            output_ids = output_ids.sequences
            
        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()

        ans_file.write(json.dumps({"prediction": outputs,
                                   "answer": answer[0],
                                   "question": line,
                                   "category": cat[0],
                                   "l2_category": cat_l2[0]}) + "\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("--path", type=str, default="MMStar")
    parser.add_argument("--answers-file", type=str, default="mmstar_answer.jsonl")
    parser.add_argument("--conv-mode", type=str, default="llava_phi_3")
    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("--max_new_tokens", type=int, default=128)
    args = parser.parse_args()

    eval_model(args)