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Change img_ar_errors to mean because too many imgs

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Kohya S
2023-01-24 20:17:15 +09:00
parent 9f644d8dc3
commit 91a50ea637
1 changed files with 7 additions and 4 deletions
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11
library/train_util.py
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@@ -225,9 +225,12 @@ class BaseDataset(torch.utils.data.Dataset):
for i, (reso, img_keys) in enumerate(zip(bucket_resos, self.buckets)):
self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(img_keys)}
print(f"bucket {i}: resolution {reso}, count: {len(img_keys)}")
self.bucket_info["img_ar_errors"] = img_ar_errors
img_ar_errors = np.array(img_ar_errors)
print(f"mean ar error (without repeats): {np.mean(np.abs(img_ar_errors))}")
mean_img_ar_error = np.mean(np.abs(img_ar_errors))
self.bucket_info["mean_img_ar_error"] = mean_img_ar_error
print(f"mean ar error (without repeats): {mean_img_ar_error}")
# 参照用indexを作る
self.buckets_indices: list(BucketBatchIndex) = []
@@ -834,7 +837,7 @@ def addnet_hash_safetensors(b):
offset = n + 8
b.seek(offset)
for chunk in iter(lambda: b.read(blksize), b""):
hash_sha256.update(chunk)
hash_sha256.update(chunk)
return hash_sha256.hexdigest()
@@ -1107,7 +1110,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
parser.add_argument("--save_every_n_epochs", type=int, default=None,
help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する")
parser.add_argument("--save_n_epoch_ratio", type=int, default=None,
help="save checkpoint N epoch ratio / 学習中のモデルを指定のエポック割合で保存する")
help="save checkpoint N epoch ratio (for example 5 means save at least 5 files total) / 学習中のモデルを指定のエポック割合で保存する（たとえば5を指定すると最低5個のファイルが保存される）")
parser.add_argument("--save_last_n_epochs", type=int, default=None, help="save last N checkpoints / 最大Nエポック保存する")
parser.add_argument("--save_last_n_epochs_state", type=int, default=None,
help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する(--save_last_n_epochsの指定を上書きします)")