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import argparse
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import torch
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import os
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import json
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from tqdm import tqdm
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import shortuuid
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.model.builder import load_pretrained_model
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from llava.utils import disable_torch_init
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from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
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from torch.utils.data import Dataset, DataLoader
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from PIL import Image
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import math
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def split_list(lst, n):
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"""Split a list into n (roughly) equal-sized chunks"""
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chunk_size = math.ceil(len(lst) / n)
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return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
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def get_chunk(lst, n, k):
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chunks = split_list(lst, n)
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return chunks[k]
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class CustomDataset(Dataset):
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def __init__(self, questions, image_folder, tokenizer, image_processor, model_config):
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self.questions = questions
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self.image_folder = image_folder
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self.tokenizer = tokenizer
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self.image_processor = image_processor
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self.model_config = model_config
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def __getitem__(self, index):
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line = self.questions[index]
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image_file = line["image"]
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qs = line["text"]
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if self.model_config.mm_use_im_start_end:
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qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
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else:
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qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
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conv = conv_templates[args.conv_mode].copy()
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conv.append_message(conv.roles[0], qs)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB')
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image_tensor = process_images([image], self.image_processor, self.model_config)[0]
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input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
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return input_ids, image_tensor
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def __len__(self):
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return len(self.questions)
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def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
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assert batch_size == 1, "batch_size must be 1"
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dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config)
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data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
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return data_loader
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def eval_model(args):
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disable_torch_init()
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model_path = os.path.expanduser(args.model_path)
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model_name = get_model_name_from_path(model_path)
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tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
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questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
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questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
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answers_file = os.path.expanduser(args.answers_file)
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os.makedirs(os.path.dirname(answers_file), exist_ok=True)
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ans_file = open(answers_file, "w")
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if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
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args.conv_mode = args.conv_mode + '_mmtag'
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print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
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data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config)
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for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)):
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idx = line["question_id"]
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cur_prompt = line["text"]
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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
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input_ids = input_ids.to(device='cuda', non_blocking=True)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True),
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do_sample=True if args.temperature > 0 else False,
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temperature=args.temperature,
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top_p=args.top_p,
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num_beams=args.num_beams,
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max_new_tokens=128,
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use_cache=True)
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input_token_len = input_ids.shape[1]
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n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
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if n_diff_input_output > 0:
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print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
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outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
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outputs = outputs.strip()
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if outputs.endswith(stop_str):
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outputs = outputs[:-len(stop_str)]
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outputs = outputs.strip()
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ans_id = shortuuid.uuid()
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ans_file.write(json.dumps({"question_id": idx,
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"prompt": cur_prompt,
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"text": outputs,
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"answer_id": ans_id,
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"model_id": model_name,
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"metadata": {}}) + "\n")
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ans_file.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
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parser.add_argument("--model-base", type=str, default=None)
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parser.add_argument("--image-folder", type=str, default="")
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parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
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parser.add_argument("--answers-file", type=str, default="answer.jsonl")
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parser.add_argument("--conv-mode", type=str, default="llava_v1")
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parser.add_argument("--num-chunks", type=int, default=1)
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parser.add_argument("--chunk-idx", type=int, default=0)
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parser.add_argument("--temperature", type=float, default=0.2)
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parser.add_argument("--top_p", type=float, default=None)
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parser.add_argument("--num_beams", type=int, default=1)
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args = parser.parse_args()
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eval_model(args)
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