add error message if model.onnx doesn't exist

finetune/tag_images_by_wd14_tagger.py

@@ -1,6 +1,5 @@
 import argparse
 import csv
-import glob
 import os
 from pathlib import Path
 
@@ -19,6 +18,7 @@ IMAGE_SIZE = 448
 # wd-v1-4-swinv2-tagger-v2 / wd-v1-4-vit-tagger / wd-v1-4-vit-tagger-v2/ wd-v1-4-convnext-tagger / wd-v1-4-convnext-tagger-v2
 DEFAULT_WD14_TAGGER_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
 FILES = ["keras_metadata.pb", "saved_model.pb", "selected_tags.csv"]
+FILES_ONNX = ["model.onnx"]
 SUB_DIR = "variables"
 SUB_DIR_FILES = ["variables.data-00000-of-00001", "variables.index"]
 CSV_FILE = FILES[-1]
@@ -80,9 +80,10 @@ def main(args):
     # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/22
     if not os.path.exists(args.model_dir) or args.force_download:
         print(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}")
+        files = FILES
         if args.onnx:
-            FILES.append("model.onnx")
+            files += FILES_ONNX
-        for file in FILES:
+        for file in files:
             hf_hub_download(args.repo_id, file, cache_dir=args.model_dir, force_download=True, force_filename=file)
         for file in SUB_DIR_FILES:
             hf_hub_download(
@@ -104,18 +105,29 @@ def main(args):
         onnx_path = f"{args.model_dir}/model.onnx"
         print("Running wd14 tagger with onnx")
         print(f"loading onnx model: {onnx_path}")
+
+        if not os.path.exists(onnx_path):
+            raise Exception(
+                f"onnx model not found: {onnx_path}, please redownload the model with --force_download"
+                + " / onnxモデルが見つかりませんでした。--force_downloadで再ダウンロードしてください"
+            )
+
         model = onnx.load(onnx_path)
         input_name = model.graph.input[0].name
         try:
             batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_value
         except:
             batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_param
+
         if args.batch_size != batch_size and type(batch_size) != str:
             # some rebatch model may use 'N' as dynamic axes
             print(
                 f"Batch size {args.batch_size} doesn't match onnx model batch size {batch_size}, use model batch size {batch_size}"
             )
             args.batch_size = batch_size
+
+        del model
+
         ort_sess = ort.InferenceSession(
             onnx_path,
             providers=["CUDAExecutionProvider"]
@@ -154,7 +166,10 @@ def main(args):
         imgs = np.array([im for _, im in path_imgs])
 
         if args.onnx:
+            if len(imgs) < args.batch_size:
+                imgs = np.concatenate([imgs, np.zeros((args.batch_size - len(imgs), IMAGE_SIZE, IMAGE_SIZE, 3))], axis=0)
             probs = ort_sess.run(None, {input_name: imgs})[0]  # onnx output numpy
+            probs = probs[: len(path_imgs)]
         else:
             probs = model(imgs, training=False)
             probs = probs.numpy()
@@ -333,7 +348,7 @@ def setup_parser() -> argparse.ArgumentParser:
         help="comma-separated list of undesired tags to remove from the output / 出力から除外したいタグのカンマ区切りのリスト",
     )
     parser.add_argument("--frequency_tags", action="store_true", help="Show frequency of tags for images / 画像ごとのタグの出現頻度を表示する")
-    parser.add_argument("--onnx", action="store_true", help="use onnx model for inference")
+    parser.add_argument("--onnx", action="store_true", help="use onnx model for inference / onnxモデルを推論に使用する")
     parser.add_argument("--append_tags", action="store_true", help="Append captions instead of overwriting / 上書きではなくキャプションを追記する")
 
     return parser