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!40586 modify the wrong format of the files 

Merge pull request !40586 from 宦晓玲/code_docs_0818
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i-robot 2022-08-19 01:22:33 +00:00 committed by Gitee
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18 changed files with 68 additions and 61 deletions
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3
docs/api/api_python/nn/mindspore.nn.DynamicLossScaleUpdateCell.rst
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@ -24,3 +24,6 @@ mindspore.nn.DynamicLossScaleUpdateCell
.. py:method:: get_loss_scale()
获取当前损失缩放系数。
返回：
float，损失缩放系数。

3
docs/api/api_python/nn/mindspore.nn.FixedLossScaleUpdateCell.rst
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@ -20,3 +20,6 @@ mindspore.nn.FixedLossScaleUpdateCell
.. py:method:: get_loss_scale()
获取当前损失缩放系数。
返回：
float，损失缩放系数。

8
mindspore/python/mindspore/common/parameter.py
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@ -677,14 +677,14 @@ class Parameter(Tensor_):
set_sliced (bool): True if the parameter is set sliced after initializing the data.
Default: False.
Returns:
Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
returns the same initialized `Parameter`.
Raises:
RuntimeError: If it is from Initializer, and parallel mode has changed after the Initializer created.
ValueError: If the length of the layout is less than 6.
TypeError: If `layout` is not tuple.
Returns:
Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
returns the same initialized `Parameter`.
"""
if self.is_default_input_init and self.is_in_parallel != _is_in_parallel_mode():
raise RuntimeError("Must set or change parallel mode before any Tensor created.")

12
mindspore/python/mindspore/dataset/engine/datasets.py
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@ -1101,6 +1101,9 @@ class Dataset:
Shuffling the dataset may not be deterministic, which means the data in each split
will be different in each epoch.
Returns:
tuple(Dataset), a tuple of datasets that have been split.
Raises:
RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
RuntimeError: If `sizes` is list of integers and sum of all elements in sizes does not
@ -1110,9 +1113,6 @@ class Dataset:
ValueError: If `sizes` is list of float and not all floats are between 0 and 1, or if the
floats don't sum to 1.
Returns:
tuple(Dataset), a tuple of datasets that have been split.
Examples:
>>> # TextFileDataset is not a mappable dataset, so this non-optimized split will be called.
>>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
@ -2288,6 +2288,9 @@ class MappableDataset(SourceDataset):
will be different in each epoch. Furthermore, if sharding occurs after split, each
shard may not be part of the same split.
Returns:
tuple(Dataset), a tuple of datasets that have been split.
Raises:
RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
RuntimeError: If `sizes` is list of integers and sum of all elements in sizes does not
@ -2297,9 +2300,6 @@ class MappableDataset(SourceDataset):
ValueError: If `sizes` is list of float and not all floats are between 0 and 1, or if the
floats don't sum to 1.
Returns:
tuple(Dataset), a tuple of datasets that have been split.
Examples:
>>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir, shuffle=False)

7
mindspore/python/mindspore/nn/cell.py
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@ -914,8 +914,9 @@ class Cell(Cell_):
Returns the dynamic_inputs of a cell object in one network.
Returns:
inputs (tuple): Inputs of the Cell object.
NOTE:
inputs (tuple), Inputs of the Cell object.
Note:
This is an experimental interface that is subject to change or deletion.
"""
@ -2006,7 +2007,7 @@ class Cell(Cell_):
def set_comm_fusion(self, fusion_type, recurse=True):
"""
Set `comm_fusion` for all the parameters in this cell. Please refer to the description of
:class:`mindspore.Parameter.comm_fusion`.
:class:`mindspore.Parameter.comm_fusion`.
Note:
The value of attribute will be overwritten when the function is called multiply.

6
mindspore/python/mindspore/nn/dynamic_lr.py
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@ -358,15 +358,15 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
power (float): The power of polynomial. It must be greater than 0.
update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
Returns:
list[float]. The size of list is `total_step`.
Raises:
TypeError: If `learning_rate` or `end_learning_rate` or `power` is not a float.
TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int.
TypeError: If `update_decay_epoch` is not a bool.
ValueError: If `learning_rate` or `power` is not greater than 0.
Returns:
list[float]. The size of list is `total_step`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``

12
mindspore/python/mindspore/nn/layer/activation.py
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@ -1319,13 +1319,13 @@ class HShrink(Cell):
Outputs:
Tensor, the same shape and data type as the input.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
Raises:
TypeError: If `lambd` is not a float.
TypeError: If dtype of `input_x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
@ -1368,13 +1368,13 @@ class Threshold(Cell):
Outputs:
Tensor, the same shape and data type as the input.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
Raises:
TypeError: If `threshold` is not a float or an int.
TypeError: If `value` is not a float or an int.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

24
mindspore/python/mindspore/nn/layer/normalization.py
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@ -344,15 +344,15 @@ class BatchNorm1d(_BatchNorm):
Outputs:
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Raises:
TypeError: If `num_features` is not an int.
TypeError: If `eps` is not a float.
ValueError: If `num_features` is less than 1.
ValueError: If `momentum` is not in range [0, 1].
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
@ -977,9 +977,6 @@ class InstanceNorm1d(_InstanceNorm):
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, L)`. Same type and
shape as the `x`.
Supported Platforms:
``GPU``
Raises:
TypeError: If the type of `num_features` is not int.
TypeError: If the type of `eps` is not float.
@ -993,6 +990,9 @@ class InstanceNorm1d(_InstanceNorm):
KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
exists.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import numpy as np
@ -1067,9 +1067,6 @@ class InstanceNorm2d(_InstanceNorm):
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, H, W)`. Same type and
shape as the `x`.
Supported Platforms:
``GPU``
Raises:
TypeError: If the type of `num_features` is not int.
TypeError: If the type of `eps` is not float.
@ -1083,6 +1080,9 @@ class InstanceNorm2d(_InstanceNorm):
KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
exists.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import numpy as np
@ -1157,9 +1157,6 @@ class InstanceNorm3d(_InstanceNorm):
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, D, H, W)`. Same type and
shape as the `x`.
Supported Platforms:
``GPU``
Raises:
TypeError: If the type of `num_features` is not int.
TypeError: If the type of `eps` is not float.
@ -1173,6 +1170,9 @@ class InstanceNorm3d(_InstanceNorm):
KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not
exists.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import numpy as np

6
mindspore/python/mindspore/nn/layer/timedistributed.py
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@ -87,12 +87,12 @@ class TimeDistributed(Cell):
Outputs:
Tensor of shape :math:`(N, T, *)`
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Raises:
TypeError: If layer is not a Cell or Primitive.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.random.random([32, 10, 3]), mindspore.float32)
>>> dense = nn.Dense(3, 6)

2
mindspore/python/mindspore/nn/optim/adamax.py
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@ -73,7 +73,7 @@ class AdaMax(Optimizer):
Note:
If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When
parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be
parameters are grouped, each group can set `weight_decay`. If not, the `weight_decay` in optimizer will be
applied.
Args:

8
mindspore/python/mindspore/nn/probability/bijector/bijector.py
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@ -243,7 +243,7 @@ class Bijector(Cell):
*args (list): the list of positional arguments forwarded to subclasses.
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Output:
Returns:
Tensor, the value of the transformed random variable.
"""
return self._forward(value, *args, **kwargs)
@ -257,7 +257,7 @@ class Bijector(Cell):
*args (list): the list of positional arguments forwarded to subclasses.
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Output:
Returns:
Tensor, the value of the input random variable.
"""
return self._inverse(value, *args, **kwargs)
@ -271,7 +271,7 @@ class Bijector(Cell):
*args (list): the list of positional arguments forwarded to subclasses.
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Output:
Returns:
Tensor, the value of logarithm of the derivative of the forward transformation.
"""
return self._forward_log_jacobian(value, *args, **kwargs)
@ -285,7 +285,7 @@ class Bijector(Cell):
*args (list): the list of positional arguments forwarded to subclasses.
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Output:
Returns:
Tensor, the value of logarithm of the derivative of the inverse transformation.
"""
return self._inverse_log_jacobian(value, *args, **kwargs)

6
mindspore/python/mindspore/nn/probability/bijector/gumbel_cdf.py
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@ -32,9 +32,6 @@ class GumbelCDF(Bijector):
scale (float, list, numpy.ndarray, Tensor): The scale. Default: 1.0.
name (str): The name of the Bijector. Default: 'GumbelCDF'.
Supported Platforms:
``Ascend`` ``GPU``
Note:
`scale` must be greater than zero.
For `inverse` and `inverse_log_jacobian`, input should be in range of (0, 1).
@ -46,6 +43,9 @@ class GumbelCDF(Bijector):
TypeError: When the dtype of `loc` or `scale` is not float,
or when the dtype of `loc` and `scale` is not same.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

6
mindspore/python/mindspore/nn/probability/bijector/power_transform.py
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@ -37,9 +37,6 @@ class PowerTransform(Bijector):
power (float, list, numpy.ndarray, Tensor): The scale factor. Default: 0.
name (str): The name of the bijector. Default: 'PowerTransform'.
Supported Platforms:
``Ascend`` ``GPU``
Note:
The dtype of `power` must be float.
@ -47,6 +44,9 @@ class PowerTransform(Bijector):
ValueError: When `power` is less than 0 or is not known statically.
TypeError: When the dtype of `power` is not float.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

6
mindspore/python/mindspore/nn/probability/bijector/scalar_affine.py
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@ -33,9 +33,6 @@ class ScalarAffine(Bijector):
shift (float, list, numpy.ndarray, Tensor): The shift factor. Default: 0.0.
name (str): The name of the bijector. Default: 'ScalarAffine'.
Supported Platforms:
``Ascend`` ``GPU``
Note:
The dtype of `shift` and `scale` must be float.
If `shift`, `scale` are passed in as numpy.ndarray or tensor, they have to have
@ -45,6 +42,9 @@ class ScalarAffine(Bijector):
TypeError: When the dtype of `shift` or `scale` is not float,
and when the dtype of `shift` and `scale` is not same.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

6
mindspore/python/mindspore/nn/probability/bijector/softplus.py
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@ -34,15 +34,15 @@ class Softplus(Bijector):
sharpness (float, list, numpy.ndarray, Tensor): The scale factor. Default: 1.0.
name (str): The name of the Bijector. Default: 'Softplus'.
Supported Platforms:
``Ascend`` ``GPU``
Note:
The dtype of `sharpness` must be float.
Raises:
TypeError: When the dtype of the sharpness is not float.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

6
mindspore/python/mindspore/nn/probability/distribution/bernoulli.py
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@ -34,9 +34,6 @@ class Bernoulli(Distribution):
dtype (mindspore.dtype): The type of the event samples. Default: mstype.int32.
name (str): The name of the distribution. Default: 'Bernoulli'.
Supported Platforms:
``Ascend`` ``GPU``
Note:
`probs` must be a proper probability (0 < p < 1).
`dist_spec_args` is `probs`.
@ -44,6 +41,9 @@ class Bernoulli(Distribution):
Raises:
ValueError: When p <= 0 or p >=1.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

6
mindspore/python/mindspore/nn/probability/distribution/beta.py
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@ -43,9 +43,6 @@ class Beta(Distribution):
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Beta'.
Supported Platforms:
``Ascend``
Note:
`concentration1` and `concentration0` must be greater than zero.
`dist_spec_args` are `concentration1` and `concentration0`.
@ -55,6 +52,9 @@ class Beta(Distribution):
ValueError: When concentration1 <= 0 or concentration0 >=1.
TypeError: When the input `dtype` is not a subclass of float.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn

2
mindspore/python/mindspore/train/train_thor/convert_utils.py
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@ -207,7 +207,7 @@ class ConvertModelUtils:
will be overwritten. Default: False.
Returns:
model (Object): High-Level API for Training.
model (Object), High-Level API for Training.
Supported Platforms:
``Ascend`` ``GPU``