244 lines
11 KiB
Python
244 lines
11 KiB
Python
import argparse
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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 numpy as np
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from PIL import Image
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import cv2
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import torch
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from torchvision import transforms
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import library.model_util as model_util
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import library.train_util as train_util
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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IMAGE_TRANSFORMS = transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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)
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def collate_fn_remove_corrupted(batch):
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"""Collate function that allows to remove corrupted examples in the
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dataloader. It expects that the dataloader returns 'None' when that occurs.
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The 'None's in the batch are removed.
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"""
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# Filter out all the Nones (corrupted examples)
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batch = list(filter(lambda x: x is not None, batch))
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return batch
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def get_latents(vae, images, weight_dtype):
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img_tensors = [IMAGE_TRANSFORMS(image) for image in images]
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img_tensors = torch.stack(img_tensors)
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img_tensors = img_tensors.to(DEVICE, weight_dtype)
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with torch.no_grad():
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latents = vae.encode(img_tensors).latent_dist.sample().float().to("cpu").numpy()
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return latents
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def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip):
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if is_full_path:
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base_name = os.path.splitext(os.path.basename(image_key))[0]
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else:
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base_name = image_key
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if flip:
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base_name += '_flip'
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return os.path.join(data_dir, base_name)
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def main(args):
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image_paths = train_util.glob_images(args.train_data_dir)
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print(f"found {len(image_paths)} images.")
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if os.path.exists(args.in_json):
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print(f"loading existing metadata: {args.in_json}")
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with open(args.in_json, "rt", encoding='utf-8') as f:
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metadata = json.load(f)
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else:
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print(f"no metadata / メタデータファイルがありません: {args.in_json}")
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return
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weight_dtype = torch.float32
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if args.mixed_precision == "fp16":
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weight_dtype = torch.float16
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elif args.mixed_precision == "bf16":
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weight_dtype = torch.bfloat16
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vae = model_util.load_vae(args.model_name_or_path, weight_dtype)
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vae.eval()
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vae.to(DEVICE, dtype=weight_dtype)
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# bucketのサイズを計算する
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max_reso = tuple([int(t) for t in args.max_resolution.split(',')])
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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}"
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bucket_resos, bucket_aspect_ratios = model_util.make_bucket_resolutions(
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max_reso, args.min_bucket_reso, args.max_bucket_reso)
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# 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
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bucket_aspect_ratios = np.array(bucket_aspect_ratios)
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buckets_imgs = [[] for _ in range(len(bucket_resos))]
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bucket_counts = [0 for _ in range(len(bucket_resos))]
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img_ar_errors = []
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def process_batch(is_last):
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for j in range(len(buckets_imgs)):
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bucket = buckets_imgs[j]
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if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
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latents = get_latents(vae, [img for _, _, img in bucket], weight_dtype)
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for (image_key, _, _), latent in zip(bucket, latents):
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npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False)
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np.savez(npz_file_name, latent)
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# flip
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if args.flip_aug:
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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
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for (image_key, _, _), latent in zip(bucket, latents):
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npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True)
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np.savez(npz_file_name, latent)
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bucket.clear()
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# 読み込みの高速化のためにDataLoaderを使うオプション
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if args.max_data_loader_n_workers is not None:
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dataset = train_util.ImageLoadingDataset(image_paths)
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data = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
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num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
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else:
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data = [[(None, ip)] for ip in image_paths]
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for data_entry in tqdm(data, smoothing=0.0):
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if data_entry[0] is None:
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continue
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img_tensor, image_path = data_entry[0]
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if img_tensor is not None:
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image = transforms.functional.to_pil_image(img_tensor)
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else:
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try:
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image = Image.open(image_path)
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if image.mode != 'RGB':
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image = image.convert("RGB")
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except Exception as e:
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print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
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continue
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image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
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if image_key not in metadata:
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metadata[image_key] = {}
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# 本当はこの部分もDataSetに持っていけば高速化できるがいろいろ大変
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aspect_ratio = image.width / image.height
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ar_errors = bucket_aspect_ratios - aspect_ratio
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bucket_id = np.abs(ar_errors).argmin()
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reso = bucket_resos[bucket_id]
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ar_error = ar_errors[bucket_id]
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img_ar_errors.append(abs(ar_error))
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# どのサイズにリサイズするか→トリミングする方向で
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if ar_error <= 0: # 横が長い→縦を合わせる
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scale = reso[1] / image.height
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else:
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scale = reso[0] / image.width
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resized_size = (int(image.width * scale + .5), int(image.height * scale + .5))
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# print(image.width, image.height, bucket_id, bucket_resos[bucket_id], ar_errors[bucket_id], resized_size,
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# bucket_resos[bucket_id][0] - resized_size[0], bucket_resos[bucket_id][1] - resized_size[1])
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assert resized_size[0] == reso[0] or resized_size[1] == reso[
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1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}"
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assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
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1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
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# 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
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if args.skip_existing:
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npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"]
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if args.flip_aug:
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npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz")
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found = True
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for npz_file in npz_files:
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if not os.path.exists(npz_file):
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found = False
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break
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dat = np.load(npz_file)['arr_0']
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if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8: # latentsのshapeを確認
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found = False
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break
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if found:
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continue
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# 画像をリサイズしてトリミングする
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# PILにinter_areaがないのでcv2で……
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image = np.array(image)
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image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
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if resized_size[0] > reso[0]:
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trim_size = resized_size[0] - reso[0]
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image = image[:, trim_size//2:trim_size//2 + reso[0]]
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elif resized_size[1] > reso[1]:
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trim_size = resized_size[1] - reso[1]
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image = image[trim_size//2:trim_size//2 + reso[1]]
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assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
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# # debug
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# cv2.imwrite(f"r:\\test\\img_{i:05d}.jpg", image[:, :, ::-1])
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# バッチへ追加
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buckets_imgs[bucket_id].append((image_key, reso, image))
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bucket_counts[bucket_id] += 1
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metadata[image_key]['train_resolution'] = reso
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# バッチを推論するか判定して推論する
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process_batch(False)
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# 残りを処理する
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process_batch(True)
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for i, (reso, count) in enumerate(zip(bucket_resos, bucket_counts)):
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print(f"bucket {i} {reso}: {count}")
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img_ar_errors = np.array(img_ar_errors)
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print(f"mean ar error: {np.mean(img_ar_errors)}")
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# metadataを書き出して終わり
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print(f"writing metadata: {args.out_json}")
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with open(args.out_json, "wt", encoding='utf-8') as f:
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json.dump(metadata, f, indent=2)
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print("done!")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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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")
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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")
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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")
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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")
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parser.add_argument("--v2", action='store_true',
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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）')
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parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
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parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
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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）")
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parser.add_argument("--max_resolution", type=str, default="512,512",
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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）")
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parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
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parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度")
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parser.add_argument("--mixed_precision", type=str, default="no",
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choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度")
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parser.add_argument("--full_path", action="store_true",
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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）")
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parser.add_argument("--flip_aug", action="store_true",
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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")
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parser.add_argument("--skip_existing", action="store_true",
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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）")
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args = parser.parse_args()
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main(args)
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