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!16562 modify tensor API 

From: @huangbingjian
Reviewed-by: @zh_qh,@ginfung
Signed-off-by: @zh_qh
This commit is contained in:
mindspore-ci-bot 2021-05-20 08:58:17 +08:00 committed by Gitee
parent 05bbd1d8fa 529be922e3
commit e4975bba6d
1 changed files with 50 additions and 16 deletions
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66
mindspore/common/tensor.py
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@ -37,24 +37,25 @@ class Tensor(Tensor_):
Some functions are implemented in C++ and some functions are implemented in Python.
Args:
input_data (Tensor, float, int, bool, tuple, list, numpy.ndarray): Input data of the tensor.
input_data (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]): Input data of the tensor.
dtype (:class:`mindspore.dtype`): Input data should be None, bool or numeric type defined in `mindspore.dtype`.
The argument is used to define the data type of the output tensor. If it is None, the data type of the
output tensor will be as same as the `input_data`. Default: None.
shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
output. If `input_data` is available, `shape` doesn't need to be set. Default: None.
init (Initializer): the information of init data.
'init' is used for delayed initialization in parallel mode. Usually, it is not recommended to
use 'init' interface to initialize parameters in other conditions. If 'init' interface is used
to initialize parameters, the `init_data` API need to be called to convert `Tensor` to the actual data.
'init' is used for delayed initialization in parallel mode. Usually, it is not recommended to use
'init' interface to initialize parameters in other conditions. If 'init' interface is used to initialize
parameters, the `Tensor.init_data` API need to be called to convert `Tensor` to the actual data.
Outputs:
Tensor, with the same shape as `input_data`.
Tensor. If `dtype` and `shape` are not set, return a tensor with the same dtype and shape as `input_data`.
If `dtype` or `shape` is set, the dtype or shape of the output Tensor is consistent with the setting.
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.common.tensor import Tensor
>>> from mindspore import Tensor
>>> from mindspore.common.initializer import One
>>> # initialize a tensor with input data
>>> t1 = Tensor(np.zeros([1, 2, 3]), ms.float32)
@ -316,7 +317,7 @@ class Tensor(Tensor_):
@property
def virtual_flag(self):
"""Mark tensor is virtual."""
"""Used to mark whether the tensor is virtual. If the tensor is virtual, return True."""
return self._virtual_flag
@virtual_flag.setter
@ -328,11 +329,29 @@ class Tensor(Tensor_):
@staticmethod
def from_numpy(array):
"""Convert numpy array to Tensor without copy data."""
"""
Convert numpy array to Tensor without copy data.
Args:
array (numpy.array): The input array.
Returns:
Tensor, has the same data type as input array.
"""
return Tensor(Tensor_.from_numpy(array))
def item(self, index=None):
"""Getitem from the Tensor with the index."""
"""
Getitem from the Tensor with the index.
Args:
index (Union[None, int, tuple(int)]): The index in Tensor. Default: None.
Returns:
Tensor getitem by index whose dtype is int or tuple with int. If index is None,
the `item` API can only convert an array of size 1 to a Python scalar.
"""
output = tensor_operator_registry.get('item')(self, index)
return output
@ -361,7 +380,8 @@ class Tensor(Tensor_):
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
Returns:
Tensor, has the same data type as x.
Tensor, if all array elements along the given axis evaluate to True, its value is True,
otherwise its value is False. If axis is None or empty tuple, reduce all dimensions.
"""
self.init_check()
@ -379,7 +399,8 @@ class Tensor(Tensor_):
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
Returns:
Tensor, has the same data type as x.
Tensor, if any array element along the given axis evaluates to True, its value is True,
otherwise its value is False. If axis is None or empty tuple, reduce all dimensions.
"""
self.init_check()
@ -425,7 +446,7 @@ class Tensor(Tensor_):
Return absolute value element-wisely.
Returns:
Tensor, has the same data type as x.
Tensor, with absolute value element-wisely.
"""
self.init_check()
return tensor_operator_registry.get('abs')()(self)
@ -1086,10 +1107,10 @@ class Tensor(Tensor_):
Args:
slice_index (int): Slice index of a parameter's slices.
It is used when initialize a slice of a parameter, it guarantees that devices
using the same slice can generate the same tensor.
shape (list(int)): Shape of the slice, it is used when initialize a slice of the parameter.
using the same slice can generate the same tensor. Default: None.
shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter. Default: None.
opt_shard_group(str): Optimizer shard group which is used in auto or semi auto parallel mode
to get one shard of a parameter's slice.
to get one shard of a parameter's slice. Default: None.
"""
if self.init is None:
raise TypeError("init_data must be set Tensor.init, init can't be None")
@ -1137,7 +1158,20 @@ class Tensor(Tensor_):
return self
def to_tensor(self, slice_index=None, shape=None, opt_shard_group=None):
"""Return init_data()."""
"""
Return init_data() and get the tensor format data of this Tensor.
Note:
The usage of `to_tensor` is deprecated. Please use `init_data`.
Args:
slice_index (int): Slice index of a parameter's slices.
It is used when initialize a slice of a parameter, it guarantees that devices
using the same slice can generate the same tensor. Default: None.
shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter. Default: None.
opt_shard_group(str): Optimizer shard group which is used in auto or semi auto parallel mode
to get one shard of a parameter's slice. Default: None.
"""
logger.warning("WARN_DEPRECATED: The usage of to_tensor is deprecated."
" Please use init_data")
return self.init_data(slice_index, shape, opt_shard_group)