fixed Inv

mindspore/ops/_grad/grad_math_ops.py

@@ -1044,6 +1044,6 @@ def get_bprop_inv(self):
     inv_grad = G.InvGrad()
 
     def bprop(x, out, dout):
-        dx = inv_grad(x, dout)
+        dx = inv_grad(out, dout)
         return (dx,)
     return bprop

mindspore/ops/operations/array_ops.py

@@ -2644,7 +2644,7 @@ class BatchToSpaceND(PrimitiveWithInfer):
             The length of block_shape is M correspoding to the number of spatial dimensions.
         crops (list): The crop value for H and W dimension, containing 2 sub list, each containing 2 int value.
             All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to
-            input dimension i+2. It is required that input_shape[i+2]*block_size[i] >= crops[i][0]+crops[i][1].
+            input dimension i+2. It is required that input_shape[i+2]*block_size[i] > crops[i][0]+crops[i][1].
 
     Inputs:
         - **input_x** (Tensor) - The input tensor.

mindspore/ops/operations/other_ops.py

@@ -228,20 +228,20 @@ class IOU(PrimitiveWithInfer):
 
     Inputs:
         - **anchor_boxes** (Tensor) - Anchor boxes, tensor of shape (N, 4). "N" indicates the number of anchor boxes,
-          and the value "4" refers to "x0", "x1", "y0", and "y1".
+          and the value "4" refers to "x0", "x1", "y0", and "y1". Data type must be float16.
         - **gt_boxes** (Tensor) - Ground truth boxes, tensor of shape (M, 4). "M" indicates the number of ground
-          truth boxes, and the value "4" refers to "x0", "x1", "y0", and "y1".
+          truth boxes, and the value "4" refers to "x0", "x1", "y0", and "y1". Data type must be float16.
 
     Outputs:
-        Tensor, the 'iou' values, tensor of shape (M, N).
+        Tensor, the 'iou' values, tensor of shape (M, N), with data type float16.
 
     Raises:
         KeyError: When `mode` is not 'iou' or 'iof'.
 
     Examples:
         >>> iou = P.IOU()
-        >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float32)
-        >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float32)
+        >>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
+        >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> iou(anchor_boxes, gt_boxes)
     """