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!19228 Fix the errors due to the wrong format 

Merge pull request !19228 from dinglinhe/code_docs_dlh_ms_I3R3BX_FixError
This commit is contained in:
i-robot 2021-07-02 02:31:37 +00:00 committed by Gitee
parent 9055537f62 716e5667d4
commit 78f1dc4c30
5 changed files with 47 additions and 43 deletions
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2
mindspore/nn/layer/basic.py
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@ -667,7 +667,7 @@ class Pad(Cell):
- mode = "CONSTANT".
- paddings = [[1,1], [2,2]].
- x = [[1,2,3], [4,5,6], [7,8,9]].
- The above can be seen: 1st dimension of x is 3, 2nd dimension of x is 3.
- The above can be seen: 1st dimension of `x` is 3, 2nd dimension of `x` is 3.
- Substitute into the formula to get:
- 1st dimension of output is paddings[0][0] + 3 + paddings[0][1] = 1 + 3 + 1 = 4.
- 2nd dimension of output is paddings[1][0] + 3 + paddings[1][1] = 2 + 3 + 2 = 7.

2
mindspore/nn/layer/math.py
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@ -919,7 +919,7 @@ class Moments(Cell):
Inputs:
- **x** (Tensor) - The tensor to be calculated. Only float16 and float32 are supported.
:math:`(N,*)` where :math:`*` means,any number of additional dimensions.
:math:`(N,*)` where :math:`*` means,any number of additional dimensions.
Outputs:
- **mean** (Tensor) - The mean of `x`, with the same date type as input `x`.

75
mindspore/ops/operations/array_ops.py
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@ -2287,6 +2287,7 @@ class Concat(PrimitiveWithInfer):
:math:`i`-th tensor. Then, the shape of the output tensor is
.. math::
(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)
Args:
@ -3383,7 +3384,7 @@ class Eye(PrimitiveWithInfer):
- **n** (int) - The number of rows of returned tensor. only constant value.
- **m** (int) - The number of columns of returned tensor. only constant value.
- **t** (mindspore.dtype) - MindSpore's dtype, The data type of the returned tensor.
The data type can be Number.
The data type can be Number.
Outputs:
Tensor, a tensor with ones on the diagonal and the rest of elements are zero. The shape of `output` depends on
@ -3452,12 +3453,12 @@ class ScatterNd(PrimitiveWithInfer):
- **indices** (Tensor) - The index of scattering in the new tensor with int32 or int64 data type.
The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
- **updates** (Tensor) - The source Tensor to be scattered.
It has shape `indices_shape[:-1] + shape[indices_shape[-1]:]`.
It has shape `indices_shape[:-1] + shape[indices_shape[-1]:]`.
- **shape** (tuple[int]) - Define the shape of the output tensor, has the same data type as indices.
The shape of `shape` is :math:`(x_1, x_2, ..., x_R)`, and length of 'shape' is greater than or equal 2.
In other words, the shape of `shape` is at least :math:`(x_1, x_2)`.
And the value of any element in `shape` must be greater than or equal 1.
In other words, :math:`x_1` >= 1, :math:`x_2` >= 1.
The shape of `shape` is :math:`(x_1, x_2, ..., x_R)`, and length of 'shape' is greater than or equal 2.
In other words, the shape of `shape` is at least :math:`(x_1, x_2)`.
And the value of any element in `shape` must be greater than or equal 1.
In other words, :math:`x_1` >= 1, :math:`x_2` >= 1.
Outputs:
Tensor, the new tensor, has the same type as `update` and the same shape as `shape`.
@ -3599,9 +3600,9 @@ class GatherNd(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - The target tensor to gather values.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index tensor, with int32 or int64 data type.
The dimension of `indices` should be <= the dimension of `input_x`.
The dimension of `indices` should be <= the dimension of `input_x`.
Outputs:
Tensor, has the same type as `input_x` and the shape is indices_shape[:-1] + x_shape[indices_shape[-1]:].
@ -3655,12 +3656,12 @@ class TensorScatterUpdate(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - The target tensor. The dimension of input_x must be no less than indices.shape[-1].
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The data type is Number.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The data type is Number.
- **indices** (Tensor) - The index of input tensor whose data type is int32 or int64.
The rank must be at least 2.
The rank must be at least 2.
- **update** (Tensor) - The tensor to update the input tensor, has the same type as input,
and update.shape = indices.shape[:-1] + input_x.shape[indices.shape[-1]:].
and update.shape = indices.shape[:-1] + input_x.shape[indices.shape[-1]:].
Outputs:
Tensor, has the same shape and type as `input_x`.
@ -3791,11 +3792,11 @@ class ScatterUpdate(_ScatterOpDynamic):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index of input tensor. With int32 data type.
If there are duplicates in indices, the order for updating is undefined.
If there are duplicates in indices, the order for updating is undefined.
- **updates** (Tensor) - The tensor to update the input tensor, has the same type as input,
and updates.shape = indices.shape + input_x.shape[1:].
and updates.shape = indices.shape + input_x.shape[1:].
Outputs:
Tensor, has the same shape and type as `input_x`.
@ -3853,10 +3854,10 @@ class ScatterNdUpdate(_ScatterNdOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index of input tensor, with int32 data type.
- **updates** (Tensor) - The tensor to be updated to the input tensor, has the same type as input.
The shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
The shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
Outputs:
Tensor, has the same shape and type as `input_x`.
@ -3918,10 +3919,10 @@ class ScatterMax(_ScatterOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do max operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor that performs the maximum operation with `input_x`,
the data type is the same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is the same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -3971,10 +3972,10 @@ class ScatterMin(_ScatterOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4026,10 +4027,10 @@ class ScatterAdd(_ScatterOpDynamic):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4135,10 +4136,10 @@ class ScatterSub(_ScatterOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4237,10 +4238,10 @@ class ScatterMul(_ScatterOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4339,10 +4340,10 @@ class ScatterDiv(_ScatterOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4447,11 +4448,11 @@ class ScatterNdAdd(_ScatterNdOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32.
The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, the updated `input_x`, has the same shape and type as `input_x`.
@ -4524,11 +4525,11 @@ class ScatterNdSub(_ScatterNdOp):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index of input tensor, with int32 data type.
The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
- **updates** (Tensor) - The tensor to be updated to the input tensor, has the same type as input.
The shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
The shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
Outputs:
Tensor, has the same shape and type as `input_x`.
@ -4648,7 +4649,7 @@ class SpaceToDepth(PrimitiveWithInfer):
Outputs:
Tensor, the same data type as `x`. It must be a 4-D tensor.Tensor of shape
:math:`(N, ( C_{in} * \text{block_size} * 2), H_{in} / \text{block_size}, W_{in} / \text{block_size})`.
:math:`(N, ( C_{in} * \text{block_size} * 2), H_{in} / \text{block_size}, W_{in} / \text{block_size})`.
Raises:
TypeError: If `block_size` is not an int.
@ -4713,7 +4714,7 @@ class DepthToSpace(PrimitiveWithInfer):
Inputs:
- **x** (Tensor) - The target tensor. It must be a 4-D tensor with shape :math:`(N, C_{in}, H_{in}, W_{in})`.
The data type is Number.
The data type is Number.
Outputs:
Tensor of shape :math:`(N, C_{in} / \text{block_size} ^ 2, H_{in} * \text{block_size},

4
mindspore/ops/operations/math_ops.py
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@ -1680,7 +1680,7 @@ class Pow(_MathBinaryOp):
.. math::
out_{i} = x_{i} ^ y_{i}
out_{i} = x_{i} ^{ y_{i}}
Inputs:
- **x** (Union[Tensor, Number, bool]) - The first input is a number or
@ -2553,7 +2553,7 @@ class FloorMod(_MathBinaryOp):
out_{i} =\text{floor}(x_{i} // y_{i})
where the :math:`floor` indicates the operator that converts the input data into the floor data type.
where the :math:`floor` indicates the operator that converts the input data into the floor data type.
Inputs:
- **x** (Union[Tensor, Number, bool]) - The first input is a number or

7
mindspore/ops/operations/nn_ops.py
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@ -6981,7 +6981,7 @@ class Dropout2D(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - A 4-D tensor with shape :math:`(N, C, H, W)`. The data type should be int8, int16,
int32, int64, float16 or float32
int32, int64, float16 or float32
Outputs:
- **output** (Tensor) - with the same shape and data type as the `input_x` tensor.
- **mask** (Tensor[bool]) - with the same shape as the `input_x` tensor.
@ -7038,7 +7038,7 @@ class Dropout3D(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - A 5-D tensor with shape :math:`(N, C, D, H, W)`. The data type should be int8, int16,
int32, int64, float16 or float32
int32, int64, float16 or float32
Outputs:
- **output** (Tensor) - with the same shape and data type as the `input_x` tensor.
@ -7243,6 +7243,9 @@ class BasicLSTMCell(PrimitiveWithInfer):
"""
It's similar to operator :class:`DynamicRNN`. BasicLSTMCell will be deprecated in the future.
Please use DynamicRNN instead.
Supported Platforms:
Deprecated
"""
@prim_attr_register