Resolve `albumentations` UserWarning (#13098)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/mkdocs_github_authors.yaml

@@ -28,4 +28,4 @@ priytosh.revolution@live.com: priytosh-tripathi
 shuizhuyuanluo@126.com: null
 stormsson@users.noreply.github.com: stormsson
 xinwang614@gmail.com: GreatV
-andrei.kochin@intel: andrei-kochin
+andrei.kochin@intel.com: andrei-kochin

ultralytics/data/augment.py

@@ -874,11 +874,56 @@ class Albumentations:
         self.p = p
         self.transform = None
         prefix = colorstr("albumentations: ")
+
         try:
             import albumentations as A
 
             check_version(A.__version__, "1.0.3", hard=True)  # version requirement
 
+            # List of possible spatial transforms
+            spatial_transforms = {
+                "Affine",
+                "BBoxSafeRandomCrop",
+                "CenterCrop",
+                "CoarseDropout",
+                "Crop",
+                "CropAndPad",
+                "CropNonEmptyMaskIfExists",
+                "D4",
+                "ElasticTransform",
+                "Flip",
+                "GridDistortion",
+                "GridDropout",
+                "HorizontalFlip",
+                "Lambda",
+                "LongestMaxSize",
+                "MaskDropout",
+                "MixUp",
+                "Morphological",
+                "NoOp",
+                "OpticalDistortion",
+                "PadIfNeeded",
+                "Perspective",
+                "PiecewiseAffine",
+                "PixelDropout",
+                "RandomCrop",
+                "RandomCropFromBorders",
+                "RandomGridShuffle",
+                "RandomResizedCrop",
+                "RandomRotate90",
+                "RandomScale",
+                "RandomSizedBBoxSafeCrop",
+                "RandomSizedCrop",
+                "Resize",
+                "Rotate",
+                "SafeRotate",
+                "ShiftScaleRotate",
+                "SmallestMaxSize",
+                "Transpose",
+                "VerticalFlip",
+                "XYMasking",
+            }  # from https://albumentations.ai/docs/getting_started/transforms_and_targets/#spatial-level-transforms
+
             # Transforms
             T = [
                 A.Blur(p=0.01),
@@ -889,8 +934,14 @@ class Albumentations:
                 A.RandomGamma(p=0.0),
                 A.ImageCompression(quality_lower=75, p=0.0),
             ]
-            self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
 
+            # Compose transforms
+            self.contains_spatial = any(transform.__class__.__name__ in spatial_transforms for transform in T)
+            self.transform = (
+                A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
+                if self.contains_spatial
+                else A.Compose(T)
+            )
             LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
         except ImportError:  # package not installed, skip
             pass
@@ -899,20 +950,26 @@ class Albumentations:
 
     def __call__(self, labels):
         """Generates object detections and returns a dictionary with detection results."""
-        im = labels["img"]
-        cls = labels["cls"]
-        if len(cls):
-            labels["instances"].convert_bbox("xywh")
-            labels["instances"].normalize(*im.shape[:2][::-1])
-            bboxes = labels["instances"].bboxes
-            # TODO: add supports of segments and keypoints
-            if self.transform and random.random() < self.p:
+        if self.transform is None or random.random() > self.p:
+            return labels
+
+        if self.contains_spatial:
+            cls = labels["cls"]
+            if len(cls):
+                im = labels["img"]
+                labels["instances"].convert_bbox("xywh")
+                labels["instances"].normalize(*im.shape[:2][::-1])
+                bboxes = labels["instances"].bboxes
+                # TODO: add supports of segments and keypoints
                 new = self.transform(image=im, bboxes=bboxes, class_labels=cls)  # transformed
                 if len(new["class_labels"]) > 0:  # skip update if no bbox in new im
                     labels["img"] = new["image"]
                     labels["cls"] = np.array(new["class_labels"])
                     bboxes = np.array(new["bboxes"], dtype=np.float32)
-            labels["instances"].update(bboxes=bboxes)
+                labels["instances"].update(bboxes=bboxes)
+        else:
+            labels["img"] = self.transform(image=labels["img"])["image"]  # transformed
+
         return labels