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import argparse
import os
import re
from pathlib import Path
from PIL import Image
from tqdm import tqdm
import torch
from library.device_utils import init_ipex, get_preferred_device
init_ipex()
from transformers import AutoProcessor, AutoModelForCausalLM
from transformers.generation.utils import GenerationMixin
import library.train_util as train_util
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
PATTERN_REPLACE = [
re.compile(r'(has|with|and) the (words?|letters?|name) (" ?[^"]*"|\w+)( ?(is )?(on|in) (the |her |their |him )?\w+)?'),
re.compile(r'(with a sign )?that says ?(" ?[^"]*"|\w+)( ?on it)?'),
re.compile(r"(with a sign )?that says ?(' ?(i'm)?[^']*'|\w+)( ?on it)?"),
re.compile(r"with the number \d+ on (it|\w+ \w+)"),
re.compile(r'with the words "'),
re.compile(r"word \w+ on it"),
re.compile(r"that says the word \w+ on it"),
re.compile("that says'the word \"( on it)?"),
]
# 誤検知しまくりの with the word xxxx を消す
def remove_words(captions, debug):
removed_caps = []
for caption in captions:
cap = caption
for pat in PATTERN_REPLACE:
cap = pat.sub("", cap)
if debug and cap != caption:
logger.info(caption)
logger.info(cap)
removed_caps.append(cap)
return removed_caps
def collate_fn_remove_corrupted(batch):
"""Collate function that allows to remove corrupted examples in the
dataloader. It expects that the dataloader returns 'None' when that occurs.
The 'None's in the batch are removed.
"""
# Filter out all the Nones (corrupted examples)
batch = list(filter(lambda x: x is not None, batch))
return batch
def main(args):
r"""
transformers 4.30.2で、バッチサイズ>1でも動くようになったので、以下コメントアウト
# GITにバッチサイズが1より大きくても動くようにパッチを当てる: transformers 4.26.0用
org_prepare_input_ids_for_generation = GenerationMixin._prepare_input_ids_for_generation
curr_batch_size = [args.batch_size] # ループの最後で件数がbatch_size未満になるので入れ替えられるように
# input_idsがバッチサイズと同じ件数である必要がある：バッチサイズはこの関数から参照できないので外から渡す
# ここより上で置き換えようとするとすごく大変
def _prepare_input_ids_for_generation_patch(self, bos_token_id, encoder_outputs):
input_ids = org_prepare_input_ids_for_generation(self, bos_token_id, encoder_outputs)
if input_ids.size()[0] != curr_batch_size[0]:
input_ids = input_ids.repeat(curr_batch_size[0], 1)
return input_ids
GenerationMixin._prepare_input_ids_for_generation = _prepare_input_ids_for_generation_patch
"""
logger.info(f"load images from {args.train_data_dir}")
train_data_dir_path = Path(args.train_data_dir)
image_paths = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
logger.info(f"found {len(image_paths)} images.")
# できればcacheに依存せず明示的にダウンロードしたい
logger.info(f"loading GIT: {args.model_id}")
git_processor = AutoProcessor.from_pretrained(args.model_id)
git_model = AutoModelForCausalLM.from_pretrained(args.model_id).to(DEVICE)
logger.info("GIT loaded")
# captioningする
def run_batch(path_imgs):
imgs = [im for _, im in path_imgs]
# curr_batch_size[0] = len(path_imgs)
inputs = git_processor(images=imgs, return_tensors="pt").to(DEVICE) # 画像はpil形式
generated_ids = git_model.generate(pixel_values=inputs.pixel_values, max_length=args.max_length)
captions = git_processor.batch_decode(generated_ids, skip_special_tokens=True)
if args.remove_words:
captions = remove_words(captions, args.debug)
for (image_path, _), caption in zip(path_imgs, captions):
with open(os.path.splitext(image_path)[0] + args.caption_extension, "wt", encoding="utf-8") as f:
f.write(caption + "\n")
if args.debug:
logger.info(f"{image_path} {caption}")
# 読み込みの高速化のためにDataLoaderを使うオプション
if args.max_data_loader_n_workers is not None:
dataset = train_util.ImageLoadingDataset(image_paths)
data = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.max_data_loader_n_workers,
collate_fn=collate_fn_remove_corrupted,
drop_last=False,
)
else:
data = [[(None, ip)] for ip in image_paths]
b_imgs = []
for data_entry in tqdm(data, smoothing=0.0):
for data in data_entry:
if data is None:
continue
image, image_path = data
if image is None:
try:
image = Image.open(image_path)
if image.mode != "RGB":
image = image.convert("RGB")
except Exception as e:
logger.error(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
continue
b_imgs.append((image_path, image))
if len(b_imgs) >= args.batch_size:
run_batch(b_imgs)
b_imgs.clear()
if len(b_imgs) > 0:
run_batch(b_imgs)
logger.info("done!")
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
parser.add_argument("--caption_extension", type=str, default=".caption", help="extension of caption file / 出力されるキャプションファイルの拡張子")
parser.add_argument(
"--model_id",
type=str,
default="microsoft/git-large-textcaps",
help="model id for GIT in Hugging Face / 使用するGITのHugging FaceのモデルID",
)
parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
parser.add_argument(
"--max_data_loader_n_workers",
type=int,
default=None,
help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）",
)
parser.add_argument("--max_length", type=int, default=50, help="max length of caption / captionの最大長")
parser.add_argument(
"--remove_words",
action="store_true",
help="remove like `with the words xxx` from caption / `with the words xxx`のような部分をキャプションから削除する",
)
parser.add_argument("--debug", action="store_true", help="debug mode")
parser.add_argument("--recursive", action="store_true", help="search for images in subfolders recursively / サブフォルダを再帰的に検索する")
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
main(args)
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