Replace print with logger if they are logs (#905)

* Add get_my_logger()

* Use logger instead of print

* Fix log level

* Removed line-breaks for readability

* Use setup_logging()

* Add rich to requirements.txt

* Make simple

* Use logger instead of print

---------

Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>

train_textual_inversion_XTI.py

@@ -36,6 +36,10 @@ from library.custom_train_functions import (
 )
 import library.original_unet as original_unet
 from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
 
 imagenet_templates_small = [
     "a photo of a {}",
@@ -99,7 +103,7 @@ def train(args):
     train_util.prepare_dataset_args(args, True)
 
     if args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None:
-        print(
+        logger.warning(
             "sample_every_n_steps and sample_every_n_epochs are not supported in this script currently / sample_every_n_stepsとsample_every_n_epochsは現在このスクリプトではサポートされていません"
         )
     assert (
@@ -114,7 +118,7 @@ def train(args):
     tokenizer = train_util.load_tokenizer(args)
 
     # acceleratorを準備する
-    print("prepare accelerator")
+    logger.info("prepare accelerator")
     accelerator = train_util.prepare_accelerator(args)
 
     # mixed precisionに対応した型を用意しておき適宜castする
@@ -127,7 +131,7 @@ def train(args):
     if args.init_word is not None:
         init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
         if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
-            print(
+            logger.warning(
                 f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
             )
     else:
@@ -141,7 +145,7 @@ def train(args):
     ), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
 
     token_ids = tokenizer.convert_tokens_to_ids(token_strings)
-    print(f"tokens are added: {token_ids}")
+    logger.info(f"tokens are added: {token_ids}")
     assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
     assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
 
@@ -169,7 +173,7 @@ def train(args):
 
     tokenizer.add_tokens(token_strings_XTI)
     token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
-    print(f"tokens are added (XTI): {token_ids_XTI}")
+    logger.info(f"tokens are added (XTI): {token_ids_XTI}")
     # Resize the token embeddings as we are adding new special tokens to the tokenizer
     text_encoder.resize_token_embeddings(len(tokenizer))
 
@@ -178,7 +182,7 @@ def train(args):
     if init_token_ids is not None:
         for i, token_id in enumerate(token_ids_XTI):
             token_embeds[token_id] = token_embeds[init_token_ids[(i // 16) % len(init_token_ids)]]
-            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+            # logger.info(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
 
     # load weights
     if args.weights is not None:
@@ -186,22 +190,22 @@ def train(args):
         assert len(token_ids) == len(
             embeddings
         ), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
-        # print(token_ids, embeddings.size())
+        # logger.info(token_ids, embeddings.size())
         for token_id, embedding in zip(token_ids_XTI, embeddings):
             token_embeds[token_id] = embedding
-            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
-        print(f"weighs loaded")
+            # logger.info(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+        logger.info(f"weighs loaded")
 
-    print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
+    logger.info(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
 
     # データセットを準備する
     blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, False))
     if args.dataset_config is not None:
-        print(f"Load dataset config from {args.dataset_config}")
+        logger.info(f"Load dataset config from {args.dataset_config}")
         user_config = config_util.load_user_config(args.dataset_config)
         ignored = ["train_data_dir", "reg_data_dir", "in_json"]
         if any(getattr(args, attr) is not None for attr in ignored):
-            print(
+            logger.info(
                 "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
                     ", ".join(ignored)
                 )
@@ -209,14 +213,14 @@ def train(args):
     else:
         use_dreambooth_method = args.in_json is None
         if use_dreambooth_method:
-            print("Use DreamBooth method.")
+            logger.info("Use DreamBooth method.")
             user_config = {
                 "datasets": [
                     {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
                 ]
             }
         else:
-            print("Train with captions.")
+            logger.info("Train with captions.")
             user_config = {
                 "datasets": [
                     {
@@ -240,7 +244,7 @@ def train(args):
 
     # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
     if use_template:
-        print(f"use template for training captions. is object: {args.use_object_template}")
+        logger.info(f"use template for training captions. is object: {args.use_object_template}")
         templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
         replace_to = " ".join(token_strings)
         captions = []
@@ -264,7 +268,7 @@ def train(args):
         train_util.debug_dataset(train_dataset_group, show_input_ids=True)
         return
     if len(train_dataset_group) == 0:
-        print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
+        logger.error("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
         return
 
     if cache_latents:
@@ -297,7 +301,7 @@ def train(args):
         text_encoder.gradient_checkpointing_enable()
 
     # 学習に必要なクラスを準備する
-    print("prepare optimizer, data loader etc.")
+    logger.info("prepare optimizer, data loader etc.")
     trainable_params = text_encoder.get_input_embeddings().parameters()
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
@@ -318,7 +322,7 @@ def train(args):
         args.max_train_steps = args.max_train_epochs * math.ceil(
             len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
         )
-        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}")
+        logger.info(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)
@@ -332,7 +336,7 @@ def train(args):
     )
 
     index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
-    # print(len(index_no_updates), torch.sum(index_no_updates))
+    # logger.info(len(index_no_updates), torch.sum(index_no_updates))
     orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
 
     # Freeze all parameters except for the token embeddings in text encoder
@@ -370,15 +374,15 @@ def train(args):
 
     # 学習する
     total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-    print("running training / 学習開始")
-    print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
-    print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
-    print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
-    print(f"  num epochs / epoch数: {num_train_epochs}")
-    print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
-    print(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}")
-    print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
-    print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+    logger.info("running training / 学習開始")
+    logger.info(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+    logger.info(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+    logger.info(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+    logger.info(f"  num epochs / epoch数: {num_train_epochs}")
+    logger.info(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
+    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}")
+    logger.info(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    logger.info(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
     progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
     global_step = 0
@@ -403,7 +407,8 @@ def train(args):
         os.makedirs(args.output_dir, exist_ok=True)
         ckpt_file = os.path.join(args.output_dir, ckpt_name)
 
-        print(f"\nsaving checkpoint: {ckpt_file}")
+        logger.info("")
+        logger.info(f"saving checkpoint: {ckpt_file}")
         save_weights(ckpt_file, embs, save_dtype)
         if args.huggingface_repo_id is not None:
             huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
@@ -411,12 +416,13 @@ def train(args):
     def remove_model(old_ckpt_name):
         old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
         if os.path.exists(old_ckpt_file):
-            print(f"removing old checkpoint: {old_ckpt_file}")
+            logger.info(f"removing old checkpoint: {old_ckpt_file}")
             os.remove(old_ckpt_file)
 
     # training loop
     for epoch in range(num_train_epochs):
-        print(f"\nepoch {epoch+1}/{num_train_epochs}")
+        logger.info("")
+        logger.info(f"epoch {epoch+1}/{num_train_epochs}")
         current_epoch.value = epoch + 1
 
         text_encoder.train()
@@ -586,7 +592,7 @@ def train(args):
         ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
         save_model(ckpt_name, updated_embs, global_step, num_train_epochs, force_sync_upload=True)
 
-        print("model saved.")
+        logger.info("model saved.")
 
 
 def save_weights(file, updated_embs, save_dtype):