268 lines
12 KiB
Python
268 lines
12 KiB
Python
import argparse
|
||
import os
|
||
import json
|
||
|
||
from tqdm import tqdm
|
||
import numpy as np
|
||
from PIL import Image
|
||
import cv2
|
||
import torch
|
||
from torchvision import transforms
|
||
|
||
import library.model_util as model_util
|
||
import library.train_util as train_util
|
||
|
||
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||
|
||
IMAGE_TRANSFORMS = transforms.Compose(
|
||
[
|
||
transforms.ToTensor(),
|
||
transforms.Normalize([0.5], [0.5]),
|
||
]
|
||
)
|
||
|
||
|
||
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 get_latents(vae, images, weight_dtype):
|
||
img_tensors = [IMAGE_TRANSFORMS(image) for image in images]
|
||
img_tensors = torch.stack(img_tensors)
|
||
img_tensors = img_tensors.to(DEVICE, weight_dtype)
|
||
with torch.no_grad():
|
||
latents = vae.encode(img_tensors).latent_dist.sample().float().to("cpu").numpy()
|
||
return latents
|
||
|
||
|
||
def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip):
|
||
if is_full_path:
|
||
base_name = os.path.splitext(os.path.basename(image_key))[0]
|
||
else:
|
||
base_name = image_key
|
||
if flip:
|
||
base_name += '_flip'
|
||
return os.path.join(data_dir, base_name)
|
||
|
||
|
||
def main(args):
|
||
# assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります"
|
||
if args.bucket_reso_steps % 8 > 0:
|
||
print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります")
|
||
|
||
image_paths = train_util.glob_images(args.train_data_dir)
|
||
print(f"found {len(image_paths)} images.")
|
||
|
||
if os.path.exists(args.in_json):
|
||
print(f"loading existing metadata: {args.in_json}")
|
||
with open(args.in_json, "rt", encoding='utf-8') as f:
|
||
metadata = json.load(f)
|
||
else:
|
||
print(f"no metadata / メタデータファイルがありません: {args.in_json}")
|
||
return
|
||
|
||
weight_dtype = torch.float32
|
||
if args.mixed_precision == "fp16":
|
||
weight_dtype = torch.float16
|
||
elif args.mixed_precision == "bf16":
|
||
weight_dtype = torch.bfloat16
|
||
|
||
vae = model_util.load_vae(args.model_name_or_path, weight_dtype)
|
||
vae.eval()
|
||
vae.to(DEVICE, dtype=weight_dtype)
|
||
|
||
# bucketのサイズを計算する
|
||
max_reso = tuple([int(t) for t in args.max_resolution.split(',')])
|
||
assert len(max_reso) == 2, f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}"
|
||
|
||
bucket_manager = train_util.BucketManager(args.bucket_no_upscale, max_reso,
|
||
args.min_bucket_reso, args.max_bucket_reso, args.bucket_reso_steps)
|
||
if not args.bucket_no_upscale:
|
||
bucket_manager.make_buckets()
|
||
else:
|
||
print("min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視されます")
|
||
|
||
# 画像をひとつずつ適切なbucketに割り当てながらlatentを計算する
|
||
img_ar_errors = []
|
||
|
||
def process_batch(is_last):
|
||
for bucket in bucket_manager.buckets:
|
||
if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
|
||
latents = get_latents(vae, [img for _, img in bucket], weight_dtype)
|
||
assert latents.shape[2] == bucket[0][1].shape[0] // 8 and latents.shape[3] == bucket[0][1].shape[1] // 8, \
|
||
f"latent shape {latents.shape}, {bucket[0][1].shape}"
|
||
|
||
for (image_key, _), latent in zip(bucket, latents):
|
||
npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False)
|
||
np.savez(npz_file_name, latent)
|
||
|
||
# flip
|
||
if args.flip_aug:
|
||
latents = get_latents(vae, [img[:, ::-1].copy() for _, img in bucket], weight_dtype) # copyがないとTensor変換できない
|
||
|
||
for (image_key, _), latent in zip(bucket, latents):
|
||
npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True)
|
||
np.savez(npz_file_name, latent)
|
||
else:
|
||
# remove existing flipped npz
|
||
for image_key, _ in bucket:
|
||
npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz"
|
||
if os.path.isfile(npz_file_name):
|
||
print(f"remove existing flipped npz / 既存のflipされたnpzファイルを削除します: {npz_file_name}")
|
||
os.remove(npz_file_name)
|
||
|
||
bucket.clear()
|
||
|
||
# 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=1, 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]
|
||
|
||
bucket_counts = {}
|
||
for data_entry in tqdm(data, smoothing=0.0):
|
||
if data_entry[0] is None:
|
||
continue
|
||
|
||
img_tensor, image_path = data_entry[0]
|
||
if img_tensor is not None:
|
||
image = transforms.functional.to_pil_image(img_tensor)
|
||
else:
|
||
try:
|
||
image = Image.open(image_path)
|
||
if image.mode != 'RGB':
|
||
image = image.convert("RGB")
|
||
except Exception as e:
|
||
print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
|
||
continue
|
||
|
||
image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
|
||
if image_key not in metadata:
|
||
metadata[image_key] = {}
|
||
|
||
# 本当はこのあとの部分もDataSetに持っていけば高速化できるがいろいろ大変
|
||
|
||
reso, resized_size, ar_error = bucket_manager.select_bucket(image.width, image.height)
|
||
img_ar_errors.append(abs(ar_error))
|
||
bucket_counts[reso] = bucket_counts.get(reso, 0) + 1
|
||
|
||
# メタデータに記録する解像度はlatent単位とするので、8単位で切り捨て
|
||
metadata[image_key]['train_resolution'] = (reso[0] - reso[0] % 8, reso[1] - reso[1] % 8)
|
||
|
||
if not args.bucket_no_upscale:
|
||
# upscaleを行わないときには、resize後のサイズは、bucketのサイズと、縦横どちらかが同じであることを確認する
|
||
assert resized_size[0] == reso[0] or resized_size[1] == reso[
|
||
1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}"
|
||
assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
|
||
1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
|
||
|
||
assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
|
||
1], f"internal error resized size is small: {resized_size}, {reso}"
|
||
|
||
# 既に存在するファイルがあればshapeを確認して同じならskipする
|
||
if args.skip_existing:
|
||
npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"]
|
||
if args.flip_aug:
|
||
npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz")
|
||
|
||
found = True
|
||
for npz_file in npz_files:
|
||
if not os.path.exists(npz_file):
|
||
found = False
|
||
break
|
||
|
||
dat = np.load(npz_file)['arr_0']
|
||
if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8: # latentsのshapeを確認
|
||
found = False
|
||
break
|
||
if found:
|
||
continue
|
||
|
||
# 画像をリサイズしてトリミングする
|
||
# PILにinter_areaがないのでcv2で……
|
||
image = np.array(image)
|
||
if resized_size[0] != image.shape[1] or resized_size[1] != image.shape[0]: # リサイズ処理が必要？
|
||
image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
|
||
|
||
if resized_size[0] > reso[0]:
|
||
trim_size = resized_size[0] - reso[0]
|
||
image = image[:, trim_size//2:trim_size//2 + reso[0]]
|
||
|
||
if resized_size[1] > reso[1]:
|
||
trim_size = resized_size[1] - reso[1]
|
||
image = image[trim_size//2:trim_size//2 + reso[1]]
|
||
|
||
assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
|
||
|
||
# # debug
|
||
# cv2.imwrite(f"r:\\test\\img_{len(img_ar_errors)}.jpg", image[:, :, ::-1])
|
||
|
||
# バッチへ追加
|
||
bucket_manager.add_image(reso, (image_key, image))
|
||
|
||
# バッチを推論するか判定して推論する
|
||
process_batch(False)
|
||
|
||
# 残りを処理する
|
||
process_batch(True)
|
||
|
||
bucket_manager.sort()
|
||
for i, reso in enumerate(bucket_manager.resos):
|
||
count = bucket_counts.get(reso, 0)
|
||
if count > 0:
|
||
print(f"bucket {i} {reso}: {count}")
|
||
img_ar_errors = np.array(img_ar_errors)
|
||
print(f"mean ar error: {np.mean(img_ar_errors)}")
|
||
|
||
# metadataを書き出して終わり
|
||
print(f"writing metadata: {args.out_json}")
|
||
with open(args.out_json, "wt", encoding='utf-8') as f:
|
||
json.dump(metadata, f, indent=2)
|
||
print("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("in_json", type=str, help="metadata file to input / 読み込むメタデータファイル")
|
||
parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
|
||
parser.add_argument("model_name_or_path", type=str, help="model name or path to encode latents / latentを取得するためのモデル")
|
||
parser.add_argument("--v2", action='store_true',
|
||
help='not used (for backward compatibility) / 使用されません（互換性のため残してあります）')
|
||
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_resolution", type=str, default="512,512",
|
||
help="max resolution in fine tuning (width,height) / fine tuning時の最大画像サイズ 「幅,高さ」（使用メモリ量に関係します）")
|
||
parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
|
||
parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度")
|
||
parser.add_argument("--bucket_reso_steps", type=int, default=64,
|
||
help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します")
|
||
parser.add_argument("--bucket_no_upscale", action="store_true",
|
||
help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します")
|
||
parser.add_argument("--mixed_precision", type=str, default="no",
|
||
choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度")
|
||
parser.add_argument("--full_path", action="store_true",
|
||
help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）")
|
||
parser.add_argument("--flip_aug", action="store_true",
|
||
help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する")
|
||
parser.add_argument("--skip_existing", action="store_true",
|
||
help="skip images if npz already exists (both normal and flipped exists if flip_aug is enabled) / npzが既に存在する画像をスキップする（flip_aug有効時は通常、反転の両方が存在する画像をスキップ）")
|
||
|
||
return parser
|
||
|
||
|
||
if __name__ == '__main__':
|
||
parser = setup_parser()
|
||
|
||
args = parser.parse_args()
|
||
main(args)
|