Simplify Results() class (#4579)

docker/Dockerfile

@@ -52,9 +52,12 @@ ENV MKL_THREADING_LAYER=GNU
 # Build and Push
 # t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
 
-# Pull and Run
+# Pull and Run with access to all GPUs
 # t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
 
+# Pull and Run with access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1)
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
+
 # Pull and Run with local directory access
 # t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
 

docs/quickstart.md

@@ -82,7 +82,8 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         sudo docker pull $t
 
         # Run the ultralytics image in a container with GPU support
-        sudo docker run -it --ipc=host --gpus all $t
+        sudo docker run -it --ipc=host --gpus all $t  # all GPUs
+        sudo docker run -it --ipc=host --gpus '"device=2,3"' $t  # specify GPUs
         ```
 
         The above command initializes a Docker container with the latest `ultralytics` image. The `-it` flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The `--ipc=host` flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The `--gpus all` flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.

tests/test_python.py

@@ -217,7 +217,7 @@ def test_all_model_yamls():
     for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
         if 'rtdetr' in m.name:
             if TORCH_1_9:  # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
-                RTDETR(m.name)(SOURCE, imgsz=640)
+                RTDETR(m.name)(SOURCE, imgsz=640)  # must be 640
         else:
             YOLO(m.name)
 
@@ -225,8 +225,8 @@ def test_all_model_yamls():
 def test_workflow():
     model = YOLO(MODEL)
     model.train(data='coco8.yaml', epochs=1, imgsz=32)
-    model.val()
-    model.predict(SOURCE)
+    model.val(imgsz=32)
+    model.predict(SOURCE, imgsz=32)
     model.export(format='onnx')  # export a model to ONNX format
 
 
@@ -243,7 +243,7 @@ def test_predict_callback_and_setup():
 
     dataset = load_inference_source(source=SOURCE)
     bs = dataset.bs  # noqa access predictor properties
-    results = model.predict(dataset, stream=True)  # source already setup
+    results = model.predict(dataset, stream=True, imgsz=160)  # source already setup
     for r, im0, bs in results:
         print('test_callback', im0.shape)
         print('test_callback', bs)
@@ -254,7 +254,7 @@ def test_predict_callback_and_setup():
 def test_results():
     for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
         model = YOLO(m)
-        results = model([SOURCE, SOURCE])
+        results = model([SOURCE, SOURCE], imgsz=160)
         for r in results:
             r = r.cpu().numpy()
             r = r.to(device='cpu', dtype=torch.float32)
@@ -263,10 +263,7 @@ def test_results():
             r.tojson(normalize=True)
             r.plot(pil=True)
             r.plot(conf=True, boxes=True)
-            print(r)
-            print(r.path)
-            for k in r.keys:
-                print(getattr(r, k))
+            print(r, len(r), r.path)
 
 
 @pytest.mark.skipif(not ONLINE, reason='environment is offline')

ultralytics/engine/results.py

@@ -101,19 +101,18 @@ class Results(SimpleClass):
         self.names = names
         self.path = path
         self.save_dir = None
-        self._keys = ('boxes', 'masks', 'probs', 'keypoints')
+        self._keys = 'boxes', 'masks', 'probs', 'keypoints'
 
     def __getitem__(self, idx):
         """Return a Results object for the specified index."""
-        r = self.new()
-        for k in self.keys:
-            setattr(r, k, getattr(self, k)[idx])
-        return r
+        return self._apply('__getitem__', idx)
 
     def __len__(self):
         """Return the number of detections in the Results object."""
-        for k in self.keys:
-            return len(getattr(self, k))
+        for k in self._keys:
+            v = getattr(self, k)
+            if v is not None:
+                return len(v)
 
     def update(self, boxes=None, masks=None, probs=None):
         """Update the boxes, masks, and probs attributes of the Results object."""
@@ -125,43 +124,34 @@ class Results(SimpleClass):
         if probs is not None:
             self.probs = probs
 
+    def _apply(self, fn, *args, **kwargs):
+        r = self.new()
+        for k in self._keys:
+            v = getattr(self, k)
+            if v is not None:
+                setattr(r, k, getattr(v, fn)(*args, **kwargs))
+        return r
+
     def cpu(self):
         """Return a copy of the Results object with all tensors on CPU memory."""
-        r = self.new()
-        for k in self.keys:
-            setattr(r, k, getattr(self, k).cpu())
-        return r
+        return self._apply('cpu')
 
     def numpy(self):
         """Return a copy of the Results object with all tensors as numpy arrays."""
-        r = self.new()
-        for k in self.keys:
-            setattr(r, k, getattr(self, k).numpy())
-        return r
+        return self._apply('numpy')
 
     def cuda(self):
         """Return a copy of the Results object with all tensors on GPU memory."""
-        r = self.new()
-        for k in self.keys:
-            setattr(r, k, getattr(self, k).cuda())
-        return r
+        return self._apply('cuda')
 
     def to(self, *args, **kwargs):
         """Return a copy of the Results object with tensors on the specified device and dtype."""
-        r = self.new()
-        for k in self.keys:
-            setattr(r, k, getattr(self, k).to(*args, **kwargs))
-        return r
+        return self._apply('to', *args, **kwargs)
 
     def new(self):
         """Return a new Results object with the same image, path, and names."""
         return Results(orig_img=self.orig_img, path=self.path, names=self.names)
 
-    @property
-    def keys(self):
-        """Return a list of non-empty attribute names."""
-        return [k for k in self._keys if getattr(self, k) is not None]
-
     def plot(
             self,
             conf=True,