mindspore_lite

Context

mindspore_lite.Context

Context is used to store environment variables during execution.

mindspore_lite.DeviceInfo

DeviceInfo base class.

mindspore_lite.CPUDeviceInfo

Helper class to set cpu device info, and it inherits DeviceInfo base class.

mindspore_lite.GPUDeviceInfo

Helper class to set gpu device info, and it inherits DeviceInfo base class.

mindspore_lite.AscendDeviceInfo

Helper class to set Ascend device infos, and it inherits DeviceInfo base class.

Converter

class mindspore_lite.FmkType

When converting a third-party or MindSpore model to a MindSpore Lite model, FmkType defines Input model’s framework type.

For details, see FmkType. Run the following command to import the package:

from mindspore_lite import FmkType

  • Type

    Currently, the following third-party model framework types are supported: TF type, CAFFE type, ONNX type, MINDIR type, TFLITE type and PYTORCH type. The following table lists the details.

    Definition

    Description

    FmkType.TF

    TensorFlow model’s framework type, and the model uses .pb as suffix

    FmkType.CAFFE

    Caffe model’s framework type, and the model uses .prototxt as suffix

    FmkType.ONNX

    ONNX model’s framework type, and the model uses .onnx as suffix

    FmkType.MINDIR

    MindSpore model’s framework type, and the model uses .mindir as suffix

    FmkType.TFLITE

    TensorFlow Lite model’s framework type, and the model uses .tflite as suffix

    FmkType.PYTORCH

    PyTorch model’s framework type, and the model uses .pt or .pth as suffix

mindspore_lite.Converter

Converter is used to convert third-party models.

Model

class mindspore_lite.ModelType

When loading or building a model from file, ModelType defines the type of input model file.

For details, see ModelType. Run the following command to import the package:

from mindspore_lite import ModelType

  • Type

    Currently, the following type of input model file are supported: ModelType.MINDIR type and ModelType.MINDIR_LITE type. The following table lists the details.

    Definition

    Description

    ModelType.MINDIR

    MindSpore model’s type, which model uses .mindir as suffix

    ModelType.MINDIR_LITE

    MindSpore Lite model’s type, which model uses .ms as suffix

mindspore_lite.Model

The Model class is used to define a MindSpore model, facilitating computational graph management.

mindspore_lite.RunnerConfig

RunnerConfig Class defines runner config of one or more servables.

mindspore_lite.ModelParallelRunner

The ModelParallelRunner class is used to define a MindSpore ModelParallelRunner, facilitating Model management.

Tensor

class mindspore_lite.DataType

Create a data type object of MindSporeLite.

For details, see DataType. Run the following command to import the package:

from mindspore_lite import DataType

  • Type

    Currently, MindSpore Lite supports Int type, Uint type and Float type. The following table lists the details.

    Definition

    Description

    DataType.UNKNOWN

    No matching any of the following known types.

    DataType.BOOL

    Boolean True or False

    DataType.INT8

    8-bit integer

    DataType.INT16

    16-bit integer

    DataType.INT32

    32-bit integer

    DataType.INT64

    64-bit integer

    DataType.UINT8

    unsigned 8-bit integer

    DataType.UINT16

    unsigned 16-bit integer

    DataType.UINT32

    unsigned 32-bit integer

    DataType.UINT64

    unsigned 64-bit integer

    DataType.FLOAT16

    16-bit floating-point number

    DataType.FLOAT32

    32-bit floating-point number

    DataType.FLOAT64

    64-bit floating-point number

    DataType.INVALID

    The maximum threshold value of DataType to prevent invalid types, corresponding to the INT32_MAX in C++.

  • Usage

    Since mindspore_lite.Tensor in Python API directly wraps C++ API with pybind11 technology, DataType has a one-to-one correspondence between the Python API and the C++ API, and the way to modify DataType is in the set and to get methods of the tensor class. These include:

    • set_data_type: Query in data_type_py_cxx_map with DataType in Python API as key, and get DataType in C++ API, pass it to set_data_type method in C++ API.

    • get_data_type: Get DataType in C++ API by get_data_type method in C++ API, Query in data_type_cxx_py_map with DataType in C++ API as key, return DataType in Python API.

    Here is an example:

    from mindspore_lite import DataType
from mindspore_lite import Tensor

tensor = Tensor()
tensor.set_data_type(DataType.FLOAT32)
data_type = tensor.get_data_type()
print(data_type)

    The result is as follows:

    DataType.FLOAT32
class mindspore_lite.Format

MindSpore Lite’s tensor type. For example: Format.NCHW.

For details, see Format. Run the following command to import the package:

from mindspore_lite import Format

  • Type

    See the following table for supported formats:

    Definition

    Description

    Format.DEFAULT

    default format

    Format.NCHW

    Store tensor data in the order of batch N, channel C, height H and width W

    Format.NHWC

    Store tensor data in the order of batch N, height H, width W and channel C

    Format.NHWC4

    C-axis 4-byte aligned Format.NHWC

    Format.HWKC

    Store tensor data in the order of height H, width W, kernel num K and channel C

    Format.HWCK

    Store tensor data in the order of height H, width W, channel C and kernel num K

    Format.KCHW

    Store tensor data in the order of kernel num K, channel C, height H and width W

    Format.CKHW

    Store tensor data in the order of channel C, kernel num K, height H and width W

    Format.KHWC

    Store tensor data in the order of kernel num K, height H, width W and channel C

    Format.CHWK

    Store tensor data in the order of channel C, height H, width W and kernel num K

    Format.HW

    Store tensor data in the order of height H and width W

    Format.HW4

    w-axis 4-byte aligned Format.HW

    Format.NC

    Store tensor data in the order of batch N and channel C

    Format.NC4

    C-axis 4-byte aligned Format.NC

    Format.NC4HW4

    C-axis 4-byte aligned and W-axis 4-byte aligned Format.NCHW

    Format.NCDHW

    Store tensor data in the order of batch N, channel C, depth D, height H and width W

    Format.NWC

    Store tensor data in the order of batch N, width W and channel C

    Format.NCW

    Store tensor data in the order of batch N, channel C and width W

    Format.NDHWC

    Store tensor data in the order of batch N, depth D, height H, width W and channel C

    Format.NC8HW8

    C-axis 8-byte aligned and W-axis 8-byte aligned Format.NCHW

  • Usage

    Since mindspore_lite.Tensor in Python API directly wraps C++ API with pybind11 technology, Format has a one-to-one correspondence between the Python API and the C++ API, and the way to modify Format is in the set and get methods of the tensor class. These includes:

    • set_format: Query in format_py_cxx_map with Format in Python API as key, and get Format in C++ API, pass it to set_format method in C++ API.

    • get_format: Get Format in C++ API by get_format method in C++ API, Query in format_cxx_py_map with Format in C++ API as key, return Format in Python API.

    Here is an example:

    from mindspore_lite import Format
from mindspore_lite import Tensor

tensor = Tensor()
tensor.set_format(Format.NHWC)
tensor_format = tensor.get_format()
print(tensor_format)

    The result is as follows:

    Format.NHWC

mindspore_lite.Tensor

The Tensor class defines a tensor in MindSporeLite.