mindspore_gs

class mindspore_gs.CompAlgo(config=None)

Base class of algorithms in GoldenStick.

Parameters

config (dict) –

User config for network compression, default is None. Algorithm config specification is default by derived class, base attributes are listed below:

• save_mindir (bool): If True, export MindIR automatically after training, else not. Default: False.

• save_mindir_path (str): The path to export MindIR, the path includes the directory and file name, which can be a relative path or an absolute path, the user needs to ensure write permission. Default: './network'.

abstract apply(network: Cell, **kwargs)

Define how to compress input network. This method must be overridden by all subclasses.

Parameters

• network (Cell) – Network to be compressed.

• kwargs (Dict) – Extensible parameter for subclasses.

Returns

Compressed network.

callbacks(*args, **kwargs)

Define what task need to be done when training. Must be called at the end of child callbacks.

Parameters

• args (Union[list, tuple, optional]) – Arguments passed to the function.

• kwargs (Union[dict, optional]) – The keyword arguments.

Returns

List of instance of Callbacks.

convert(net_opt: Cell, ckpt_path='')

Define how to convert a compressed network to a standard network before exporting to MindIR.

Parameters

• net_opt (Cell) – Network to be converted which is transformed by CompAlgo.apply.

• ckpt_path (str) – Path to checkpoint file for net_opt. Default is "", which means not loading checkpoint file to net_opt. this parameter would be deprecated in future version.

Returns

An instance of Cell represents converted network.

Examples

>>> from mindspore_gs.quantization import SimulatedQuantizationAwareTraining as SimQAT
>>> ## 1) Define network to be trained
>>> network = LeNet(10)
>>> ## 2) Define MindSpore Golden Stick Algorithm, here we use base algorithm.
>>> algo = SimQAT()
>>> ## 3) Apply MindSpore Golden Stick algorithm to origin network.
>>> network = algo.apply(network)
>>> ## 4) Then you can start training, after which you can convert a compressed network to a standard
>>> ##    network, there are two ways to do that.
>>> ## 4.1) Convert without checkpoint.
>>> net_deploy = algo.convert(network)
>>> ## 4.2) Convert with checkpoint.
>>> net_deploy = algo.convert(network, ckpt_path)

loss(loss_fn: callable)

Define how to adjust loss-function for algorithm. Subclass is not need to overridden this method if current algorithm not care loss-function.

Parameters

loss_fn (callable) – Original loss function.

Returns

Adjusted loss function.

set_save_mindir(save_mindir: bool)

Set whether to automatically export MindIR after training.

Parameters

save_mindir (bool) – If True, export MindIR automatically after training, else not.

Raises

TypeError – If need_save is not bool.

Examples

>>> import mindspore as ms
>>> from mindspore_gs.quantization import SimulatedQuantizationAwareTraining as SimQAT
>>> import numpy as np
>>> ## 1) Define network to be trained
>>> network = LeNet(10)
>>> ## 2) Define MindSpore Golden Stick Algorithm, here we use base algorithm.
>>> algo = SimQAT()
>>> ## 3) Enable automatically export MindIR after training.
>>> algo.set_save_mindir(save_mindir=True)
>>> ## 4) Set MindIR output path.
>>> algo.set_save_mindir_path(save_mindir_path="./lenet")
>>> ## 5) Apply MindSpore Golden Stick algorithm to origin network.
>>> network = algo.apply(network)
>>> ## 6) Set up Model.
>>> train_dataset = create_custom_dataset()
>>> net_loss = ms.nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
>>> net_opt = ms.nn.Momentum(network.trainable_params(), 0.01, 0.9)
>>> model = ms.Model(network, net_loss, net_opt, metrics={"Accuracy": ms.train.Accuracy()})
>>> ## 7) Config callback in model.train, start training, then MindIR will be exported.
>>> model.train(1, train_dataset, callbacks=algo.callbacks())

set_save_mindir_path(save_mindir_path: str)

Set the path to export MindIR, only takes effect if save_mindir is True.

Parameters

save_mindir_path (str) – The path to export MindIR, the path includes the directory and file name, which can be a relative path or an absolute path, the user needs to ensure write permission.

Raises

ValueError – if save_mindir_path is not Non-empty str.