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A10G
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import argparse
import torch
import os
import json
import pandas as pd
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, load_image_from_base64, get_model_name_from_path
from PIL import Image
import math
all_options = ['A', 'B', 'C', 'D']
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 is_none(value):
if value is None:
return True
if type(value) is float and math.isnan(value):
return True
if type(value) is str and value.lower() == 'nan':
return True
if type(value) is str and value.lower() == 'none':
return True
return False
def get_options(row, options):
parsed_options = []
for option in options:
option_value = row[option]
if is_none(option_value):
break
parsed_options.append(option_value)
return parsed_options
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 = pd.read_table(os.path.expanduser(args.question_file))
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}.')
for index, row in tqdm(questions.iterrows(), total=len(questions)):
options = get_options(row, all_options)
cur_option_char = all_options[:len(options)]
if args.all_rounds:
num_rounds = len(options)
else:
num_rounds = 1
for round_idx in range(num_rounds):
idx = row['index']
question = row['question']
hint = row['hint']
image = load_image_from_base64(row['image'])
if not is_none(hint):
question = hint + '\n' + question
for option_char, option in zip(all_options[:len(options)], options):
question = question + '\n' + option_char + '. ' + option
qs = cur_prompt = question
if 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
if args.single_pred_prompt:
if args.lang == 'cn':
qs = qs + '\n' + "θ―·η΄ζ₯εηιι‘Ήεζ―γ"
else:
qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
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()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
image_tensor = process_images([image], image_processor, model.config)[0]
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
image_sizes=[image.size],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
# no_repeat_ngram_size=3,
max_new_tokens=1024,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"round_id": round_idx,
"prompt": cur_prompt,
"text": outputs,
"options": options,
"option_char": cur_option_char,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
# rotate options
options = options[1:] + options[:1]
cur_option_char = cur_option_char[1:] + cur_option_char[:1]
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("--all-rounds", action="store_true")
parser.add_argument("--single-pred-prompt", action="store_true")
parser.add_argument("--lang", type=str, default="en")
args = parser.parse_args()
eval_model(args)
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