VictorMylle/Kohya-ss-sd-scripts
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add logging args for training scripts

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Kohya S
2024-02-08 21:16:42 +09:00
parent 9b8ea12d34
commit 055f02e1e1
9 changed files with 147 additions and 48 deletions
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16
train_controlnet.py
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@@ -35,11 +35,14 @@ from library.custom_train_functions import (
pyramid_noise_like,
apply_noise_offset,
)
from library.utils import setup_logging
from library.utils import setup_logging, add_logging_arguments
setup_logging()
import logging
logger = logging.getLogger(__name__)
# TODO 他のスクリプトと共通化する
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
logs = {
@@ -256,7 +259,9 @@ def train(args):
args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
accelerator.print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
accelerator.print(
f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}"
)
# データセット側にも学習ステップを送信
train_dataset_group.set_max_train_steps(args.max_train_steps)
@@ -312,7 +317,9 @@ def train(args):
accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
accelerator.print(f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
accelerator.print(
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
)
# logger.info(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
@@ -335,7 +342,7 @@ def train(args):
if accelerator.is_main_process:
init_kwargs = {}
if args.wandb_run_name:
init_kwargs['wandb'] = {'name': args.wandb_run_name}
init_kwargs["wandb"] = {"name": args.wandb_run_name}
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers(
@@ -576,6 +583,7 @@ def train(args):
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
add_logging_arguments(parser)
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, False, True, True)
train_util.add_training_arguments(parser, False)