mindspore.dtype =============== Data Type ---------- .. class:: mindspore.dtype The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. Run the following command to import the package: .. code-block:: import mindspore.common.dtype as mstype or .. code-block:: from mindspore import dtype as mstype Numeric Type ~~~~~~~~~~~~ Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. The following table lists the details. ============================================== ============================= Definition Description ============================================== ============================= ``mindspore.int8`` , ``mindspore.byte`` 8-bit integer ``mindspore.int16`` , ``mindspore.short`` 16-bit integer ``mindspore.int32`` , ``mindspore.intc`` 32-bit integer ``mindspore.int64`` , ``mindspore.intp`` 64-bit integer ``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer ``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer ``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer ``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer ``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number ``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number ``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number ============================================== ============================= Other Type ~~~~~~~~~~ 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. ``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. ``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`` . ``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. ============================ ================= Tree Topology ~~~~~~~~~~~~~~ The relationships of the above types are as follows: .. code-block:: └─── mindspore.dtype ├─── number │ ├─── bool_ │ ├─── int_ │ │ ├─── int8, byte │ │ ├─── int16, short │ │ ├─── int32, intc │ │ └─── int64, intp │ ├─── uint │ │ ├─── uint8, ubyte │ │ ├─── uint16, ushort │ │ ├─── uint32, uintc │ │ └─── uint64, uintp │ └─── float_ │ ├─── float16 │ ├─── float32 │ └─── float64 ├─── tensor │ ├─── Array[float32] │ └─── ... ├─── list_ │ ├─── List[int32,float32] │ └─── ... ├─── tuple_ │ ├─── Tuple[int32,float32] │ └─── ... ├─── function │ ├─── Func │ ├─── Func[(int32, float32), int32] │ └─── ... ├─── MetaTensor ├─── type_type ├─── type_none ├─── symbolic_key └─── env_type