fix CropAndResize doc

mindspore/ops/operations/image_ops.py

@@ -34,21 +34,21 @@ class CropAndResize(PrimitiveWithInfer):
 
     Inputs:
         - **x** (Tensor) - The input image must be a 4-D tensor of shape [batch, image_height, image_width, depth].
-            Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16.
+          Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16.
         - **boxes** (Tensor) - A 2-D tensor of shape [num_boxes, 4].
-            The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image
-            and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to
-            the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is
-            mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled
-            crop is an up-down flipped version of the original image. The width dimension is treated similarly.
-            Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to
-            extrapolate the input image values. Types allowd: float32.
+          The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image
+          and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to
+          the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is
+          mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled
+          crop is an up-down flipped version of the original image. The width dimension is treated similarly.
+          Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to
+          extrapolate the input image values. Types allowd: float32.
         - **box_index** (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
-            The value of box_ind[i] specifies the image that the i-th box refers to. Types allowd: int32.
+          The value of box_ind[i] specifies the image that the i-th box refers to. Types allowd: int32.
         - **crop_size** (Tensor) - Only constant value is allowd. Types allowed: int32.
-            A 1-D tensor of 2 elements, size = [crop_height, crop_width].
-            All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved.
-            Both crop_height and crop_width need to be positive.
+          A 1-D tensor of 2 elements, size = [crop_height, crop_width].
+          All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved.
+          Both crop_height and crop_width need to be positive.
     Outputs:
         A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
 
@@ -68,8 +68,8 @@ class CropAndResize(PrimitiveWithInfer):
         >>> IMAGE_WIDTH = 256
         >>> CHANNELS = 3
         >>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
-        >>> boxes = np.random.uniform(shape=[NUM_BOXES, 4]).astype(np.float32)
-        >>> box_index = np.random.uniform(shape=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
+        >>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
+        >>> box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
         >>> crop_size = np.array([24, 24]).astype(np.int32)
         >>> crop_and_resize = CropAndResizeNet(crop_size=Tensor(crop_size))
         >>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index))

tests/st/ops/ascend/test_aicpu_ops/test_crop_and_reszie.py

@@ -41,8 +41,8 @@ def test_net_float32():
     image_width = 256
     channels = 3
     image = np.random.normal(size=[batch_size, image_height, image_width, channels]).astype(np.float32)
-    boxes = np.random.uniform(shape=[num_boxes, 4]).astype(np.float32)
-    box_index = np.random.uniform(shape=[num_boxes], low=0, high=batch_size).astype(np.int32)
+    boxes = np.random.uniform(size=[num_boxes, 4]).astype(np.float32)
+    box_index = np.random.uniform(size=[num_boxes], low=0, high=batch_size).astype(np.int32)
     crop_size = np.array([24, 24]).astype(np.int32)
     net = Net(crop_size=Tensor(crop_size))
     output = net(Tensor(image), Tensor(boxes), Tensor(box_index))