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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, get_model_name_from_path, KeywordsStoppingCriteria
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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_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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meta_pth = '/opt/data/private/metas/unsplash_ISO300-_PIL_1024_x2x4_APEX.txt'
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img_pths = []
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with open(meta_pth, 'r') as f:
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for line in f.readlines():
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img_pths.append(line.split('\t')[0])
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f.close()
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img_pths = get_chunk(img_pths, args.num_chunks, args.chunk_idx)
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img_pths = split_list(img_pths, 8)
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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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if 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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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
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image = Image.open(os.path.join(args.image_folder, image_file))
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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keywords = [stop_str]
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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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.unsqueeze(0).half().cuda(),
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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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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.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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