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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 tinyllava.utils import * |
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from tinyllava.data import * |
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from tinyllava.model import * |
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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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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, tokenizer, image_processor, context_len = load_pretrained_model(model_path) |
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model.to(device='cuda') |
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text_processor = TextPreprocess(tokenizer, args.conv_mode) |
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data_args = model.config |
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image_processor = ImagePreprocess(image_processor, data_args) |
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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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for line in tqdm(questions): |
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idx = line["question_id"] |
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image_file = line["image"] |
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qs = line["text"] |
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cur_prompt = qs |
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qs = DEFAULT_IMAGE_TOKEN + '\n' + qs |
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msg = Message() |
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msg.add_message(qs) |
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result = text_processor(msg.messages, mode='eval') |
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input_ids = result['input_ids'] |
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prompt = result['prompt'] |
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input_ids = input_ids.unsqueeze(0).cuda() |
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image = Image.open(os.path.join(args.image_folder, image_file)).convert('RGB') |
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image_tensor = image_processor(image) |
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image_tensors = image_tensor.unsqueeze(0).half().cuda() |
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image_sizes = [image.size] |
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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_tensors, |
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image_sizes=image_sizes, |
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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=1024, |
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use_cache=True) |
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outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].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": prompt, |
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"text": outputs, |
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"answer_id": ans_id, |
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"model_id": args.model_base, |
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"metadata": {}}) + "\n") |
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ans_file.flush() |
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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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