diff --git a/mindspore/common/dtype.py b/mindspore/common/dtype.py index 6c381793fb..f4f34fd5ff 100644 --- a/mindspore/common/dtype.py +++ b/mindspore/common/dtype.py @@ -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. diff --git a/mindspore/common/parameter.py b/mindspore/common/parameter.py index 57fb005aa8..57efd5bc6d 100644 --- a/mindspore/common/parameter.py +++ b/mindspore/common/parameter.py @@ -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. diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py index 24c0f2c668..4bf629e805 100644 --- a/mindspore/common/tensor.py +++ b/mindspore/common/tensor.py @@ -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: