Support extensive shape for functions related to bounding box localiz…

…ation (#10516)

* support extensive shape for functions related to bounding box localization

Signed-off-by: Yonghye Kwon <[email protected]>

* merge exp branch updates

Signed-off-by: Yonghye Kwon <[email protected]>
Co-authored-by: Glenn Jocher <[email protected]>

utils/general.py

@@ -750,30 +750,30 @@ def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
 def xyxy2xywh(x):
     # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    y[..., 0] = (x[..., 0] + x[..., 2]) / 2  # x center
+    y[..., 1] = (x[..., 1] + x[..., 3]) / 2  # y center
+    y[..., 2] = x[..., 2] - x[..., 0]  # width
+    y[..., 3] = x[..., 3] - x[..., 1]  # height
     return y
 
 
 def xywh2xyxy(x):
     # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
+    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
+    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
+    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
     return y
 
 
 def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
     # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
-    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
-    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
-    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
+    y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw  # top left x
+    y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh  # top left y
+    y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw  # bottom right x
+    y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh  # bottom right y
     return y
 
 
@@ -782,18 +782,18 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
     if clip:
         clip_boxes(x, (h - eps, w - eps))  # warning: inplace clip
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
-    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
-    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
-    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
+    y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w  # x center
+    y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h  # y center
+    y[..., 2] = (x[..., 2] - x[..., 0]) / w  # width
+    y[..., 3] = (x[..., 3] - x[..., 1]) / h  # height
     return y
 
 
 def xyn2xy(x, w=640, h=640, padw=0, padh=0):
     # Convert normalized segments into pixel segments, shape (n,2)
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * x[:, 0] + padw  # top left x
-    y[:, 1] = h * x[:, 1] + padh  # top left y
+    y[..., 0] = w * x[..., 0] + padw  # top left x
+    y[..., 1] = h * x[..., 1] + padh  # top left y
     return y
 
 
@@ -833,9 +833,9 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
         gain = ratio_pad[0][0]
         pad = ratio_pad[1]
 
-    boxes[:, [0, 2]] -= pad[0]  # x padding
-    boxes[:, [1, 3]] -= pad[1]  # y padding
-    boxes[:, :4] /= gain
+    boxes[..., [0, 2]] -= pad[0]  # x padding
+    boxes[..., [1, 3]] -= pad[1]  # y padding
+    boxes[..., :4] /= gain
     clip_boxes(boxes, img0_shape)
     return boxes
 
@@ -862,13 +862,13 @@ def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=F
 def clip_boxes(boxes, shape):
     # Clip boxes (xyxy) to image shape (height, width)
     if isinstance(boxes, torch.Tensor):  # faster individually
-        boxes[:, 0].clamp_(0, shape[1])  # x1
-        boxes[:, 1].clamp_(0, shape[0])  # y1
-        boxes[:, 2].clamp_(0, shape[1])  # x2
-        boxes[:, 3].clamp_(0, shape[0])  # y2
+        boxes[..., 0].clamp_(0, shape[1])  # x1
+        boxes[..., 1].clamp_(0, shape[0])  # y1
+        boxes[..., 2].clamp_(0, shape[1])  # x2
+        boxes[..., 3].clamp_(0, shape[0])  # y2
     else:  # np.array (faster grouped)
-        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2
-        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2
+        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
+        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
 
 
 def clip_segments(segments, shape):