!44266 modify format

Merge pull request !44266 from 俞涵/code_docs_1010
2022-10-20 06:40:13 +00:00 · 2022-10-20 06:40:13 +00:00 · 8f42725575
parent 9998d6dbde 3861531c58
commit 8f42725575
9 changed files with 32 additions and 26 deletions
--- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.reverse_sequence.rst
+++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.reverse_sequence.rst
@ -1,7 +1,7 @@
 mindspore.Tensor.reverse_sequence
 ==================================

-.. py:function:: mindspore.Tensor.reverse_sequce(seq_lengths, seq_dim=0, batch_dim=0)
+.. py:method:: mindspore.Tensor.reverse_sequce(seq_lengths, seq_dim=0, batch_dim=0)

    对输入序列进行部分反转。

--- a/docs/api/api_python/mindspore/mindspore.data_sink.rst
+++ b/docs/api/api_python/mindspore/mindspore.data_sink.rst
@ -13,7 +13,7 @@ mindspore.data_sink
        - **steps** (int) - 总的运行次数。 `steps` 必须为正整数。
        - **sink_size** (int) - 控制每次下沉的数据执行次数。 `sink_size` 必须为正整数。默认值：1。
        - **jit_config** (JitConfig) - 编译时所使用的JitConfig配置项，详细可参考 :class:`mindspore.JitConfig` 。默认值：None，表示以PyNative模式运行。
-        - **input_signature** (Tensor) - 用于表示输入参数的Tensor。Tensor的shape和dtype将作为函数的输入shape和dtype。默认值：None。
+        - **input_signature** (Union[Tensor, List or Tuple of Tensors]) - 用于表示输入参数的Tensor。Tensor的shape和dtype将作为函数的输入shape和dtype。默认值：None。

    返回：
        函数，该生成的函数会以数据下沉模式执行。
--- a/docs/api/api_python/nn/mindspore.nn.FractionalMaxPool3d.rst
+++ b/docs/api/api_python/nn/mindspore.nn.FractionalMaxPool3d.rst
@ -12,7 +12,7 @@ mindspore.nn.FractionalMaxPool3d
    输入输出的数据格式可以是"NCDHW"。其中，"N"是批次大小，"C"是通道数，"D"是特征深度，"H"是特征高度，"W"是特征宽度。

    参数：
-        - **kernel_size** (Union[float, tuple[int]]) - 指定池化核尺寸大小，如果为整数，则代表池化核的深、高和宽。如果为tuple，其值必须包含三个整数值分别表示池化核的深、高和宽。
+        - **kernel_size** (Union[int, tuple[int]]) - 指定池化核尺寸大小，如果为整数，则代表池化核的深、高和宽。如果为tuple，其值必须包含三个整数值分别表示池化核的深、高和宽。
        - **output_size** (Union[int, tuple[int]]) - 目标输出大小。如果是整数，则表示输出目标的深、高和宽。如果是tuple，其值必须包含三个整数值分别表示目标输出的深、高和宽。默认值是 `None` 。
        - **output_ratio** (Union[float, tuple[float]]) - 目标输出shape与输入shape的比率。通过输入shape和 `output_ratio` 确定输出shape。支持数据类型：float16、float32、double，数值介于0到1之间。默认值是 `None` 。
        - **return_indices** (bool) - 如果为 `True` ，返回分数最大池化的最大值的的索引值。默认值是 `False` 。
--- a/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst
+++ b/docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst
@ -11,11 +11,11 @@ mindspore.ops.broadcast_to

    - 如果不相等，分以下三种情况：

-        - 情况一：如果目标shape该维的值为-1，则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ；
+      - 情况一：如果目标shape该维的值为-1，则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ；

-        - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:`(1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ；
+      - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:`(1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ；

-        - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。
+      - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。

    至此输出shape后面m维就确定好了，现在看一下前面 :math:`*` 维，有以下两种情况：

--- a/docs/api/api_python/ops/mindspore.ops.func_scalar_cast.rst
+++ b/docs/api/api_python/ops/mindspore.ops.func_scalar_cast.rst
@ -1,7 +1,7 @@
 mindspore.ops.scalar_cast
 ==========================

-.. py:function:: mindspore.ops.mindspore.ops.scalar_cast(input_x, input_y)
+.. py:function:: mindspore.ops.scalar_cast(input_x, input_y)

    将输入Scalar转换为其他类型。

--- a/mindspore/python/mindspore/common/tensor.py
+++ b/mindspore/python/mindspore/common/tensor.py
@ -6451,11 +6451,14 @@ class Tensor(Tensor_):
            other (Union[Tensor, Number, bool]): The second input is a number or a bool when the first input is a tensor
                or a tensor whose data type is number or bool.
            rounding_mode (string, optional): Type of rounding applied to the result. Three types are defined as,
-                None: Default behavior. Equivalent to true division in Python or `true_divide` in NumPy.
-                `floor`: Rounds the results of the division down. Equivalent to floor division in Python
-                    or `floor_divide` in NumPy.
-                `trunc`: Rounds the results of the division towards zero. Equivalent to C-style integer division.
-                Default: None.
+
+                - None: Default behavior. Equivalent to true division in Python or `true_divide` in NumPy.
+
+                - floor: Rounds the results of the division down. Equivalent to floor division in Python
+                  or `floor_divide` in NumPy.
+
+                - trunc: Rounds the results of the division towards zero. Equivalent to C-style integer division.
+                  Default: None.

        Returns:
            Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher
--- a/mindspore/python/mindspore/nn/layer/pooling.py
+++ b/mindspore/python/mindspore/nn/layer/pooling.py
@ -122,7 +122,7 @@ class MaxPool3d(Cell):
        the max values are generated.

        - **output** (Tensor) - Maxpooling result, with shape :math:`(N_{out}, C_{out}, D_{out}, H_{out}, W_{out})` or
-        :math:`(C_{out}, D_{out}, H_{out}, W_{out})`. It has the same data type as `x`.
+          :math:`(C_{out}, D_{out}, H_{out}, W_{out})`. It has the same data type as `x`.
        - **argmax** (Tensor) - Index corresponding to the maximum value. Data type is int64.

    Raises:
@ -385,7 +385,7 @@ class AvgPool3d(Cell):

    Inputs:
        - **x** (Tensor) - Tensor of shape :math:`(N, C, D_{in}, H_{in}, W_{in})` or
-        :math:`(C, D_{in}, H_{in}, W_{in})`.
+          :math:`(C, D_{in}, H_{in}, W_{in})`.
          Currently support float16 and float32 data type.

    Outputs:
@ -1111,7 +1111,7 @@ class FractionalMaxPool2d(Cell):
            `output_size` can be a tuple, or a single H for H x H.
            specifying the size (H, W) of the output tensor.
            Default: None.
-        output_ratio (Union[float, tuple]): The target `output_ratio` is H x W.
+        output_ratio (Union[float, tuple[float]]): The target `output_ratio` is H x W.
            `output_ratio` can be a tuple, or a single H for H x H.
            Specifying the size of the output tensor by using a ratio of the input size.
            Data type : float16, float32, double, and value is between (0, 1).
@ -1234,14 +1234,14 @@ class FractionalMaxPool3d(Cell):
    D the feature depth, H is the feature height, and W is the feature width.

    Args:
-        kernel_size (Union[float, tuple]): The target `kernel_size` is D x H x W.
+        kernel_size (Union[int, tuple[int]]): The target `kernel_size` is D x H x W.
            `kernel_size` can be a tuple, or a single K for K x K x K.
            specifying the window size (D, H, W) of the input tensor.
-        output_size (Union[int, tuple]): The target `output_size` is D x H x W.
+        output_size (Union[int, tuple[int]]): The target `output_size` is D x H x W.
            `output_size` can be a tuple, or a single H for H x H x H.
            Specifying the size (D, H, W) of the output tensor.
            Default: None.
-        output_ratio (Union[float, tuple]): The target `output_ratio` is D x H x W.
+        output_ratio (Union[float, tuple[float]]): The target `output_ratio` is D x H x W.
            `output_ratio` can be a tuple, or a single H for H x H x H.
            Specifying the size of the output tensor by using a ratio of the input size.
            Data type : float16, float32, double, and value is between (0, 1).
--- a/mindspore/python/mindspore/ops/function/array_func.py
+++ b/mindspore/python/mindspore/ops/function/array_func.py
@ -3503,12 +3503,12 @@ def broadcast_to(x, shape):

    If the first :math:`*` dims of output shape does not have -1 in it, then fill the input
    shape with ones until their length are the same, and then refer to
-    Case 2 mentioned above to calculate the output shape. With target shape :math:` (3, 1, 4, 1, 5, 9)`,
+    Case 2 mentioned above to calculate the output shape. With target shape :math:`(3, 1, 4, 1, 5, 9)`,
    input shape :math:`(1, 5, 9)`, the filled input shape will be :math:`(1, 1, 1, 1, 5, 9)` and thus the
-    output shape is :math:` (3, 1, 4, 1, 5, 9)`.
+    output shape is :math:`(3, 1, 4, 1, 5, 9)`.

    If the first :math:`*` dims of output shape have -1 in it, it implies this -1 is conrresponding to
-    a non-existing dim so they're not broadcastable. With target shape :math:` (3, -1, 4, 1, 5, 9)`,
+    a non-existing dim so they're not broadcastable. With target shape :math:`(3, -1, 4, 1, 5, 9)`,
    input shape :math:`(1, 5, 9)`, instead of operating the dim-filling process first, it raises errors directly.

    Args:
--- a/mindspore/python/mindspore/ops/function/math_func.py
+++ b/mindspore/python/mindspore/ops/function/math_func.py
@ -622,11 +622,14 @@ def div(input, other, rounding_mode=None):
        y (Union[Tensor, Number, bool]): The second input is a number or
            a bool when the first input is a tensor or a tensor whose data type is number or bool.
        rounding_mode (str, optional): Type of rounding applied to the result. Three types are defined as,
-            None: Default behavior. Equivalent to true division in Python or `true_divide` in NumPy.
-            `floor`: Rounds the results of the division down. Equivalent to floor division in Python
-                or `floor_divide` in NumPy.
-            `trunc`: Rounds the results of the division towards zero. Equivalent to C-style integer division.
-            Default: None.
+
+            - None: Default behavior. Equivalent to true division in Python or `true_divide` in NumPy.
+
+            - floor: Rounds the results of the division down. Equivalent to floor division in Python
+              or `floor_divide` in NumPy.
+
+            - trunc: Rounds the results of the division towards zero. Equivalent to C-style integer division.
+              Default: None.

    Returns:
        Tensor, the shape is the same as the one after broadcasting,