ultralytics 8.0.172 faster LetterBox() and Classify Tune fix (#4766)

Co-authored-by: BardJun <70683507+jy1002@users.noreply.github.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.171'
+__version__ = '8.0.172'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/predictor.py

@@ -143,8 +143,8 @@ class BasePredictor:
             (list): A list of transformed images.
         """
         same_shapes = all(x.shape == im[0].shape for x in im)
-        auto = same_shapes and self.model.pt
-        return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]
+        letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride)
+        return [letterbox(image=x) for x in im]
 
     def write_results(self, idx, results, batch):
         """Write inference results to a file or directory."""

ultralytics/models/rtdetr/predict.py

@@ -58,4 +58,5 @@ class RTDETRPredictor(BasePredictor):
         Returns:
             (list): A list of transformed imgs.
         """
-        return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]
+        letterbox = LetterBox(self.imgsz, auto=False, scaleFill=True)
+        return [letterbox(image=x) for x in im]

ultralytics/models/sam/predict.py

@@ -48,11 +48,11 @@ class Predictor(BasePredictor):
             im = np.ascontiguousarray(im)  # contiguous
             im = torch.from_numpy(im)
 
-        img = im.to(self.device)
-        img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
+        im = im.to(self.device)
+        im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
         if not_tensor:
-            img = (img - self.mean) / self.std
-        return img
+            im = (im - self.mean) / self.std
+        return im
 
     def pre_transform(self, im):
         """
@@ -64,8 +64,9 @@ class Predictor(BasePredictor):
         Returns:
             (list): A list of transformed images.
         """
-        assert len(im) == 1, 'SAM model has not supported batch inference yet!'
-        return [LetterBox(self.args.imgsz, auto=False, center=False)(image=x) for x in im]
+        assert len(im) == 1, 'SAM model does not currently support batched inference'
+        letterbox = LetterBox(self.args.imgsz, auto=False, center=False)
+        return [letterbox(image=x) for x in im]
 
     def inference(self, im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False, *args, **kwargs):
         """

ultralytics/models/yolo/classify/val.py

@@ -70,6 +70,7 @@ class ClassificationValidator(BaseValidator):
                                            on_plot=self.on_plot)
         self.metrics.speed = self.speed
         self.metrics.confusion_matrix = self.confusion_matrix
+        self.metrics.save_dir = self.save_dir
 
     def get_stats(self):
         """Returns a dictionary of metrics obtained by processing targets and predictions."""

ultralytics/utils/callbacks/base.py

@@ -214,7 +214,7 @@ def add_integration_callbacks(instance):
         callbacks_list.extend([clear_cb, comet_cb, dvc_cb, mlflow_cb, neptune_cb, tune_cb, tb_cb, wb_cb])
 
     # Load export callbacks (patch to avoid CoreML protobuf error)
-    if 'Exporter' in instance.__class__.__name__:
+    if 'Exporter' in instance.__class__.__name__ and instance.args.format in ('coreml', 'mlmodel'):
         from .tensorboard import callbacks as tb_cb
         callbacks_list.append(tb_cb)