Spaces:
Runtime error
Runtime error
File size: 6,515 Bytes
c3f3b0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
import argparse
import torch
import os
import json
import pandas as pd
from tqdm import tqdm
import shortuuid
from cumo.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from cumo.conversation import conv_templates, SeparatorStyle
from cumo.model.builder import load_pretrained_model
from cumo.utils import disable_torch_init
from cumo.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)
model.config.training = False
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,
pad_token_id=tokenizer.eos_token_id,
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)
|