mindspore.export

mindspore.export(net, *inputs, file_name, file_format='AIR', **kwargs)[source]

Export the mindspore network into an offline model in the specified format.

Note

  1. When exporting AIR, ONNX format, the size of a single tensor can not exceed 2GB.

  2. When file_name does not have a suffix, the system will automatically add one according to the file_format.

Parameters

  • net (Cell) – MindSpore network.

  • inputs (Tensor) – Inputs of the net, if the network has multiple inputs, incoming tuple(Tensor).

  • file_name (str) – File name of the model to be exported.

  • file_format (str) –

    MindSpore currently supports ‘AIR’, ‘ONNX’ and ‘MINDIR’ format for exported model.

    • AIR: Ascend Intermediate Representation. An intermediate representation format of Ascend model.

    • ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.

    • MINDIR: MindSpore Native Intermediate Representation for Anf. An intermediate representation format for MindSpore models.

  • kwargs (dict) –

    Configuration options dictionary.

    • quant_mode (str): If the network is quantization aware training network, the quant_mode should be set to “QUANT”, else the quant_mode should be set to “NONQUANT”.

    • mean (float): The mean of input data after preprocessing, used for quantizing the first layer of network. Default: 127.5.

    • std_dev (float): The variance of input data after preprocessing, used for quantizing the first layer of network. Default: 127.5.

    • enc_key (byte): Byte type key used for encryption. Tha valid length is 16, 24, or 32.

    • enc_mode (str): Specifies the encryption mode, take effect when enc_key is set. Option: ‘AES-GCM’ | ‘AES-CBC’. Default: ‘AES-GCM’.

    • dataset (Dataset): Specifies the preprocess methods of network.

Examples

>>> import numpy as np
>>> from mindspore import export, Tensor
>>>
>>> net = LeNet()
>>> input_tensor = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32))
>>> export(net, Tensor(input_tensor), file_name='lenet', file_format='MINDIR')