The actual path of ``dtype`` is ``/mindspore/common/dtype.py``.
Run the following command to import the package:
...
...
@@ -49,19 +51,19 @@ For other defined types, see the following table.
============================ =================
Type Description
============================ =================
``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW.
``MetaTensor`` A tensor only has data type and shape.
``bool_`` Bool number.
``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py).
``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py).
``bool_`` Boolean ``True`` or ``False``.
``int_`` Integer scalar.
``uint`` Unsigned integer scalar.
``float_`` Floating-point scalar.
``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` .
``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types.
``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types.
``function`` Function. Return in two ways, one returns ``Func`` directly, the other returns ``Func(args: List[T0,T1,...,Tn], retval: T)`` .
``type_type`` Type of type.
``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None.
``type_type`` Type definition of type.
``type_none`` No matching return type, corresponding to the ``type(None)`` in Python.
``symbolic_key`` The value of a variable managed by embd, which is used as a key of the variable in ``env_type`` .
``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` .
``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient.
The actual path of ``dtype`` is ``/mindspore/common/dtype.py``.
Run the following command to import the package:
...
...
@@ -49,19 +51,19 @@ For other defined types, see the following table.
============================ =================
Type Description
============================ =================
``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW.
``MetaTensor`` A tensor only has data type and shape.
``bool_`` Bool number.
``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py).
``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py).
``bool_`` Boolean ``True`` or ``False``.
``int_`` Integer scalar.
``uint`` Unsigned integer scalar.
``float_`` Floating-point scalar.
``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` .
``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types.
``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types.
``function`` Function. Return in two ways, one returns ``Func`` directly, the other returns ``Func(args: List[T0,T1,...,Tn], retval: T)`` .
``type_type`` Type of type.
``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None.
``type_type`` Type definition of type.
``type_none`` No matching return type, corresponding to the ``type(None)`` in Python.
``symbolic_key`` The value of a variable managed by embd, which is used as a key of the variable in ``env_type`` .
``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` .
``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient.
