Mercurial > repos > bgruening > json2yolosegment

changeset 2:158e6ce48345 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 739bcabf09bdb9c291121a6b1f889dabe1a98ea9
author bgruening
date Fri, 11 Jul 2025 06:49:20 +0000
parents dfda27273ead
children 97bc82ee2a61
files macros.xml yolov8.py
diffstat 2 files changed, 13 insertions(+), 4 deletions(-) [+]
line wrap: on
line diff
--- a/macros.xml	Mon Jul 07 06:47:08 2025 +0000
+++ b/macros.xml	Fri Jul 11 06:49:20 2025 +0000
@@ -1,6 +1,6 @@
 <macros>
     <token name="@TOOL_VERSION@">8.3.0</token>
-    <token name="@VERSION_SUFFIX@">1</token>
+    <token name="@VERSION_SUFFIX@">2</token>
     <xml name="creator">
         <creator>
             <person name="Yi Sun" email="sunyi000@gmail.com" />
--- a/yolov8.py	Mon Jul 07 06:47:08 2025 +0000
+++ b/yolov8.py	Fri Jul 11 06:49:20 2025 +0000
@@ -175,6 +175,15 @@
 #
 # Functions
 #
+
+def safe_rmtree(path):
+    try:
+        shutil.rmtree(path)
+    except OSError:
+        time.sleep(1)
+        shutil.rmtree(path, ignore_errors=True)
+
+
 # Train a new model on the dataset mentioned in yaml file
 def trainModel(model_path, model_name, yaml_filepath, **kwargs):
     if "imgsz" in kwargs:
@@ -264,7 +273,7 @@
 
     train_save_path = os.path.expanduser('~/runs/' + args.mode + '/train/')
     if os.path.isdir(train_save_path):
-        shutil.rmtree(train_save_path)
+        safe_rmtree(train_save_path)
     # Load a pretrained YOLO model (recommended for training)
     if args.model_format == 'pt':
         model = YOLO(os.path.join(model_path, model_name + "." + args.model_format))
@@ -285,7 +294,7 @@
     # Remove prediction save path if already exists
     val_save_path = os.path.expanduser('~/runs/' + args.mode + '/val/')
     if os.path.isdir(val_save_path):
-        shutil.rmtree(val_save_path)
+        safe_rmtree(val_save_path)
     # Validate the model
     metrics = model.val()  # no args needed, dataset & settings remembered
     metrics.box.map    # map50-95
@@ -327,7 +336,7 @@
         # Remove prediction save path if already exists
         pred_save_path = os.path.expanduser('~/runs/' + args.mode + '/predict/')
         if os.path.isdir(pred_save_path):
-            shutil.rmtree(pred_save_path)
+            safe_rmtree(pred_save_path)
     if "foldername" in kwargs:
         save_folder_name = kwargs['foldername']
     # infer on a local image or directory containing images/videos