optimize code docs and global a int replace

mindspore/_checkparam.py

@@ -866,7 +866,7 @@ def check_input_format(input_param):
 
 
 def _expand_tuple(n_dimensions):
-    """To expand a int number to tuple."""
+    """To expand an int number to tuple."""
 
     def convert(m):
         if not isinstance(m, tuple):

mindspore/dataset/utils/browse_dataset.py

@@ -81,13 +81,13 @@ def imshow_det_bbox(image, bboxes, labels, segm=None, class_names=None, score_th
         "text_color must be a three tuple, formatted (B, G, R)."
     assert isinstance(mask_color, tuple) and len(mask_color) == 3, \
         "mask_color must be a three tuple, formatted (B, G, R)."
-    assert isinstance(thickness, int), "thickness must be a int."
+    assert isinstance(thickness, int), "thickness must be an int."
     assert thickness >= 0, "thickness must be larger than or equal to zero."
-    assert isinstance(font_size, (int, float)), "font_size must be a int or float."
+    assert isinstance(font_size, (int, float)), "font_size must be an int or float."
     assert font_size >= 0, "font_size must be larger than or equal to zero."
     assert isinstance(show, bool), "show must be a bool."
     assert isinstance(win_name, str), "win_name must be a str."
-    assert isinstance(wait_time, int), "wait_time must be a int."
+    assert isinstance(wait_time, int), "wait_time must be an int."
     assert wait_time >= 0, "wait_time must be larger than or equal to zero."
     if out_file is not None:
         assert isinstance(out_file, str), "out_file must be a str."

mindspore/nn/layer/image.py

@@ -338,7 +338,7 @@ class MSSSIM(Cell):
         ValueError: If length of shape of `img1` or `img2` is not equal to 4.
 
     Supported Platforms:
-        ``Ascend`` ``GPU`` ``CPU``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> import numpy as np

mindspore/ops/composite/math_ops.py

@@ -875,7 +875,7 @@ def cummin(x, axis):
 
     Raises:
         TypeError: If `input_x` is not a Tensor.
-        TypeError: If 'axis' is not a int.
+        TypeError: If 'axis' is not an int.
         ValueError:If 'axis' is out the range of [-len(`input_x`.shape) to len(`input_x`.shape) - 1]
 
     Supported Platforms:

mindspore/ops/composite/multitype_ops/_compile_utils.py

@@ -750,7 +750,7 @@ def tensor_setitem_by_ellipsis(self, index, value):
 
 
 def _tensor_setitem_by_int_tensor_with_tensor(data, index, value):
-    """Set a tensor item by a int tensor with a tensor."""
+    """Set a tensor item by an int tensor with a tensor."""
     updates = _generate_updates_from_tensor(data, index, value, const_utils.SET_ITEM_BY_ONE_TENSOR)
     index = F.select(index < 0, index + F.shape(data)[0], index)
     index = F.expand_dims(index, -1)

mindspore/ops/op_info_register.py

@@ -167,13 +167,13 @@ class RegOp:
 
     def _is_int(self, value):
         """
-        Check if the value is a int.
+        Check if the value is an int.
 
         Args:
             value: Parameter to be checked.
 
         Raises:
-            TypeError: If the type of value is not a int.
+            TypeError: If the type of value is not an int.
         """
         if not isinstance(value, int):
             raise TypeError("%s value must be int" % str(value))

mindspore/ops/operations/_inner_ops.py

@@ -1456,7 +1456,7 @@ class Cummin(Primitive):
 
     Raises:
         TypeError: If `input_x` is not a Tensor.
-        TypeError: If 'axis' is not a int.
+        TypeError: If 'axis' is not an int.
         ValueError:If 'axis' is out the range from -len(`input_x`.shape) to len(`input_x`.shape) - 1
 
     Supported Platforms:

mindspore/ops/operations/array_ops.py

@@ -5665,7 +5665,7 @@ class InplaceUpdate(PrimitiveWithInfer):
 
     Args:
         indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of x
-            to update with v. It is a int or tuple, whose value is in [0, the first dimension size of x).
+            to update with v. It is an int or tuple, whose value is in [0, the first dimension size of x).
 
     Inputs:
         - **x** (Tensor) - A tensor which to be inplace updated. It can be one of the following data types:
@@ -6773,7 +6773,7 @@ class ExtractVolumePatches(Primitive):
 
     Raises:
         TypeError: If dtype of input_x is neither float16 nor float32.
-        TypeError: If kernel_size or strides is not a list, a tuple or a int.
+        TypeError: If kernel_size or strides is not a list, a tuple or an int.
         TypeError: If input_x is not a tensor.
         TypeError: If padding is not str.
         ValueError: If the length of kernel_size is neither 3 nor 5 and kernel_size is not an integer.

mindspore/ops/operations/math_ops.py

@@ -150,7 +150,7 @@ class Ger(Primitive):
 
     Outputs:
         Tensor, output matrix with the same dtype as inputs.With `x1` shape :math:`(m,)` and
-        `x2` shape of :math:`(n,)`,`output` has shape :math:`(m * n)`.
+        `x2` shape of :math:`(n,)`,the `output` has shape :math:`(m * n)`.
 
     Raises:
         TypeError: If `x1` or `x2` is not a Tensor.
@@ -1149,6 +1149,7 @@ class LpNorm(Primitive):
         axis(int,list,tuple): Specifies which dimension or dimensions of input to calculate the norm across.
         p(int): The order of norm.
         keep_dims(bool): Whether the output tensors have dim retained or not.
+        epsilon(float): A value added to the denominator for numerical stability. Default: 1e-12.
 
     Inputs:
         - **input** (Tensor) - Input tensor.
@@ -1667,7 +1668,7 @@ class InplaceSub(PrimitiveWithInfer):
 
     Args:
         indices (Union[int, tuple]): Indices into the left-most dimension of x, and determines which rows of x
-            to subtract with v. It is a int or tuple, whose value is in [0, the first dimension size of x).
+            to subtract with v. It is an int or tuple, whose value is in [0, the first dimension size of x).
 
     Inputs:
         - **x** (Tensor) - The first input is a tensor whose data type is float16, float32 or int32.

tests/st/networks/models/deeplabv3/src/backbone/resnet_deeplab.py

@@ -227,12 +227,12 @@ class _DepthwiseConv2dNative(nn.Cell):
         self.group = group
         if not (isinstance(in_channels, int) and in_channels > 0):
             raise ValueError('Attr \'in_channels\' of \'DepthwiseConv2D\' Op passed '
-                             + str(in_channels) + ', should be a int and greater than 0.')
+                             + str(in_channels) + ', should be an int and greater than 0.')
         if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
             (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
                 kernel_size[0] < 1 or kernel_size[1] < 1:
             raise ValueError('Attr \'kernel_size\' of \'DepthwiseConv2D\' Op passed '
-                             + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
+                             + str(self.kernel_size) + ', should be an int or tuple and equal to or greater than 1.')
         self.weight = Parameter(initializer(weight_init, [1, in_channels // group, *kernel_size]),
                                 name='weight')