forked from huawei/mindspore2022
Fix the formats and syntax in tensor and parameter
This commit is contained in:
zhiqiang
parent
26d750c84c
commit
1d64c6b714
|
|
@ -183,7 +183,7 @@ def get_py_obj_dtype(obj):
|
|||
Get the MindSpore data type which corresponds to python type or variable.
|
||||
|
||||
Args:
|
||||
obj: An object of python type, or a variable in python type.
|
||||
obj (type): An object of python type, or a variable in python type.
|
||||
|
||||
Returns:
|
||||
Type of MindSpore type.
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ class Parameter(Tensor_):
|
|||
otherwise, the parameter name may be different than expected.
|
||||
|
||||
Args:
|
||||
default_input (Union[Tensor, Number]): Parameter data, to be set initialized.
|
||||
default_input (Union[Tensor, int, float, numpy.ndarray, list]): Parameter data, to be set initialized.
|
||||
name (str): Name of the child parameter. Default: None.
|
||||
requires_grad (bool): True if the parameter requires gradient. Default: True.
|
||||
layerwise_parallel (bool): When layerwise_parallel is true in data parallel mode,
|
||||
|
|
@ -82,7 +82,7 @@ class Parameter(Tensor_):
|
|||
mode. It works only when enable parallel optimizer in `mindspore.context.set_auto_parallel_context()`.
|
||||
Default: True.
|
||||
|
||||
Example:
|
||||
Examples:
|
||||
>>> from mindspore import Parameter, Tensor
|
||||
>>> from mindspore.common import initializer as init
|
||||
>>> from mindspore.ops import operations as P
|
||||
|
|
@ -161,13 +161,13 @@ class Parameter(Tensor_):
|
|||
elif isinstance(default_input, (np.ndarray, list)):
|
||||
Tensor_.__init__(self, default_input)
|
||||
else:
|
||||
raise TypeError(f"Parameter input must be [`Tensor`, `Number`]."
|
||||
raise TypeError(f"Parameter input must be [`Tensor`, `int`, `float`, `numpy.ndarray`, `list`]."
|
||||
f"But with type {type(default_input)}.")
|
||||
|
||||
def __deepcopy__(self, memodict):
|
||||
new_obj = Parameter(self)
|
||||
new_obj.name = self.name
|
||||
new_obj._inited_param = self._inited_param # pylint: disable=W0212
|
||||
new_obj._inited_param = self._inited_param # pylint: disable=W0212
|
||||
return new_obj
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -488,11 +488,11 @@ class Parameter(Tensor_):
|
|||
Initialize the parameter data.
|
||||
|
||||
Args:
|
||||
layout (list[list[int]]): Parameter slice layout [dev_mat, tensor_map, slice_shape].
|
||||
|
||||
- dev_mat (list[int]): Device matrix.
|
||||
- tensor_map (list[int]): Tensor map.
|
||||
- slice_shape (list[int]): Shape of slice.
|
||||
layout (Union[None, list(list(int))]): Parameter slice
|
||||
layout [dev_mat, tensor_map, slice_shape]. Default: None.
|
||||
- dev_mat (list(int)): Device matrix.
|
||||
- tensor_map (list(int)): Tensor map.
|
||||
- slice_shape (list(int)): Shape of slice.
|
||||
set_sliced (bool): True if the parameter is set sliced after initializing the data.
|
||||
Default: False.
|
||||
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ class Tensor(Tensor_):
|
|||
>>> assert isinstance(t1, Tensor)
|
||||
>>> assert t1.shape == (1, 2, 3)
|
||||
>>> assert t1.dtype == mindspore.float32
|
||||
...
|
||||
>>>
|
||||
>>> # initialize a tensor with a float scalar
|
||||
>>> t2 = Tensor(0.1)
|
||||
>>> assert isinstance(t2, Tensor)
|
||||
|
|
@ -113,7 +113,7 @@ class Tensor(Tensor_):
|
|||
def __deepcopy__(self, memodict):
|
||||
new_obj = Tensor(self)
|
||||
new_obj.init = self.init
|
||||
new_obj._virtual_flag = self._virtual_flag # pylint:disable=w0212
|
||||
new_obj._virtual_flag = self._virtual_flag # pylint:disable=w0212
|
||||
return new_obj
|
||||
|
||||
def __repr__(self):
|
||||
|
|
@ -127,7 +127,7 @@ class Tensor(Tensor_):
|
|||
def __eq__(self, other):
|
||||
if not isinstance(other, (int, float, Tensor)):
|
||||
return False
|
||||
# bool type is not supported for `Equal` operator in backend.
|
||||
# bool type is not supported for `Equal` operator in backend.
|
||||
if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_):
|
||||
if isinstance(other, Tensor):
|
||||
return Tensor(np.array(self.asnumpy() == other.asnumpy()))
|
||||
|
|
@ -248,7 +248,6 @@ class Tensor(Tensor_):
|
|||
return out[0]
|
||||
raise TypeError("Not support len of a 0-D tensor")
|
||||
|
||||
|
||||
def __mod__(self, other):
|
||||
return tensor_operator_registry.get('__mod__')(self, other)
|
||||
|
||||
|
|
@ -353,10 +352,8 @@ class Tensor(Tensor_):
|
|||
|
||||
Args:
|
||||
axis (Union[None, int, tuple(int)): Dimensions of reduction,
|
||||
when axis is None or empty tuple, reduce all dimensions.
|
||||
Default: (), reduce all dimensions.
|
||||
keep_dims (bool): Whether to keep the reduced dimensions.
|
||||
Default : False, don't keep these reduced dimensions.
|
||||
when axis is None or empty tuple, reduce all dimensions. Default: ().
|
||||
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
|
||||
|
||||
Returns:
|
||||
Tensor, has the same data type as x.
|
||||
|
|
@ -373,10 +370,8 @@ class Tensor(Tensor_):
|
|||
|
||||
Args:
|
||||
axis (Union[None, int, tuple(int)): Dimensions of reduction,
|
||||
when axis is None or empty tuple, reduce all dimensions.
|
||||
Default: (), reduce all dimensions.
|
||||
keep_dims (bool): Whether to keep the reduced dimensions.
|
||||
Default : False, don't keep these reduced dimensions.
|
||||
when axis is None or empty tuple, reduce all dimensions. Default: ().
|
||||
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
|
||||
|
||||
Returns:
|
||||
Tensor, has the same data type as x.
|
||||
|
|
@ -392,7 +387,7 @@ class Tensor(Tensor_):
|
|||
Reshape the tensor according to the input shape.
|
||||
|
||||
Args:
|
||||
shape (Union(tuple[int], \*int)): Dimension of the output tensor.
|
||||
shape (Union[tuple(int), int]): Dimension of the output tensor.
|
||||
|
||||
Returns:
|
||||
Tensor, has the same dimension as the input shape.
|
||||
|
|
@ -411,7 +406,7 @@ class Tensor(Tensor_):
|
|||
Expand the dimension of target tensor to the dimension of input tensor.
|
||||
|
||||
Args:
|
||||
shape (Tensor): The input tensor. The shape of input tensor must obey
|
||||
x (Tensor): The input tensor. The shape of input tensor must obey
|
||||
the broadcasting rule.
|
||||
|
||||
Returns:
|
||||
|
|
@ -436,10 +431,8 @@ class Tensor(Tensor_):
|
|||
|
||||
Args:
|
||||
axis (Union[None, int, tuple(int), list(int)]): Dimensions of reduction,
|
||||
when axis is None or empty tuple, reduce all dimensions.
|
||||
Default: (), reduce all dimensions.
|
||||
keep_dims (bool): Whether to keep the reduced dimensions.
|
||||
Default : False, don't keep these reduced dimensions.
|
||||
when axis is None or empty tuple, reduce all dimensions. Default: ().
|
||||
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
|
||||
|
||||
Returns:
|
||||
Tensor, has the same data type as x.
|
||||
|
|
@ -460,10 +453,10 @@ class Tensor(Tensor_):
|
|||
then tensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
|
||||
|
||||
Args:
|
||||
axes(Union[None, tuple(int), list(int), \*int], optional): If axes is None or
|
||||
axes(Union[None, tuple(int), list(int), int], optional): If axes is None or
|
||||
blank, tensor.transpose() will reverse the order of the axes. If axes is tuple(int)
|
||||
or list(int), tensor.transpose() will transpose the tensor to the new axes order.
|
||||
If axes is \*int, this form is simply intended as a convenience alternative to the
|
||||
If axes is int, this form is simply intended as a convenience alternative to the
|
||||
tuple/list form.
|
||||
|
||||
Returns:
|
||||
|
|
@ -502,13 +495,13 @@ class Tensor(Tensor_):
|
|||
return reshape_op(self, (-1,))
|
||||
|
||||
def flatten(self, order='C'):
|
||||
"""
|
||||
r"""
|
||||
Returns a copy of the tensor collapsed into one dimension.
|
||||
|
||||
Args:
|
||||
order (str, optional): Can choose between \'C\' and \'F\'. \'C\' means to
|
||||
flatten in row-major (C-style) order. \'F\' means to flatten in column-major
|
||||
(Fortran- style) order. Only \'C\' and \'F\' are supported.
|
||||
order (str, optional): Can choose between 'C' and 'F'. 'C' means to
|
||||
flatten in row-major (C-style) order. 'F' means to flatten in column-major
|
||||
(Fortran-style) order. Only 'C' and 'F' are supported. Default: 'C'.
|
||||
|
||||
Returns:
|
||||
Tensor, has the same data type as input.
|
||||
|
|
@ -559,7 +552,7 @@ class Tensor(Tensor_):
|
|||
Removes single-dimensional entries from the shape of a tensor.
|
||||
|
||||
Args:
|
||||
axis (Union[None, int, list(int), tuple(list)], optional): Default is None.
|
||||
axis (Union[None, int, list(int), tuple(int)], optional): Default is None.
|
||||
|
||||
Returns:
|
||||
Tensor, with all or a subset of the dimensions of length 1 removed.
|
||||
|
|
@ -576,11 +569,11 @@ class Tensor(Tensor_):
|
|||
|
||||
Args:
|
||||
dtype (Union[:class:`mindspore.dtype`, str]): Designated tensor dtype, can be in format
|
||||
of :class:`mindspore.dtype.float32` or \'float32\'. Default is :class:`mindspore.dtype.float32`
|
||||
|
||||
of :class:`mindspore.dtype.float32` or `float32`.
|
||||
Default: :class:`mindspore.dtype.float32`.
|
||||
copy (bool, optional): By default, astype always returns a newly allocated
|
||||
tensor. If this is set to false, the input tensor is returned instead
|
||||
of a copy if possible.
|
||||
of a copy if possible. Default: True.
|
||||
|
||||
Returns:
|
||||
Tensor, with the designated dtype.
|
||||
|
|
@ -591,7 +584,6 @@ class Tensor(Tensor_):
|
|||
return self
|
||||
return tensor_operator_registry.get('cast')(self, dtype)
|
||||
|
||||
|
||||
def init_check(self):
|
||||
if self.has_init:
|
||||
self.init_data()
|
||||
|
|
@ -606,7 +598,7 @@ class Tensor(Tensor_):
|
|||
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.
|
||||
shape (list(int)): Shape of the slice, it is used when initialize a slice of the parameter.
|
||||
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.
|
||||
"""
|
||||
|
|
@ -655,7 +647,6 @@ class Tensor(Tensor_):
|
|||
self.assign_value(Tensor(data, dtype=self.dtype))
|
||||
return self
|
||||
|
||||
|
||||
def to_tensor(self, slice_index=None, shape=None, opt_shard_group=None):
|
||||
"""Return init_data()."""
|
||||
logger.warning("WARN_DEPRECATED: The usage of to_tensor is deprecated."
|
||||
|
|
@ -683,7 +674,7 @@ class RowTensor:
|
|||
Args:
|
||||
indices (Tensor): A 1-D integer Tensor of shape [D0].
|
||||
values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn].
|
||||
dense_shape (tuple): An integer tuple which contains the shape
|
||||
dense_shape (tuple(int)): An integer tuple which contains the shape
|
||||
of the corresponding dense tensor.
|
||||
|
||||
Returns:
|
||||
|
|
@ -743,11 +734,11 @@ class SparseTensor:
|
|||
|
||||
Args:
|
||||
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
|
||||
where N and ndims are the number of values and number of dimensions in
|
||||
where N and ndims are the number of `values` and number of dimensions in
|
||||
the SparseTensor, respectively.
|
||||
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
|
||||
supplies the values for each element in indices.
|
||||
dense_shape (tuple): A integer tuple of size `ndims`,
|
||||
supplies the values for each element in `indices`.
|
||||
dense_shape (tuple(int)): A integer tuple of size `ndims`,
|
||||
which specifies the dense_shape of the sparse tensor.
|
||||
|
||||
Returns:
|
||||
|
|
|
|||
Loading…
Reference in New Issue