mindspore.ConvertModelUtils

class mindspore.ConvertModelUtils[source]

Convert model to thor model.

static convert_to_thor_model(model, network, loss_fn=None, optimizer=None, metrics=None, amp_level='O0', loss_scale_manager=None, keep_batchnorm_fp32=False)[source]

This interface is used to convert model to thor model.

Parameters

  • model (Object) – High-Level API for Training.

  • network (Cell) – A training network.

  • loss_fn (Cell) – Objective function. Default: None.

  • optimizer (Cell) – Optimizer used to updating the weights. Default: None.

  • metrics (Union[dict, set]) – A Dictionary or a set of metrics to be evaluated by the model during training. eg: {‘accuracy’, ‘recall’}. Default: None.

  • amp_level (str) –

    Level for mixed precision training. Supports [“O0”, “O2”, “O3”, “auto”]. Default: “O0”.

    • O0: Do not change.

    • O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.

    • O3: Cast network to float16, with additional property ‘keep_batchnorm_fp32=False’.

    • auto: Set level to recommended level in different devices. O2 is recommended on GPU, O3 is recommended on Ascend. The recommended level is based on the expert experience, cannot always generalize. User should specify the level for special network.

  • loss_scale_manager (Union[None, LossScaleManager]) – If it is None, the loss would not be scaled. Otherwise, scale the loss by LossScaleManager and optimizer can not be None. It is a key argument. e.g. Use loss_scale_manager=None to set the value.

  • keep_batchnorm_fp32 (bool) – Keep Batchnorm running in float32. If True, the level setting before will be overwritten. Default: False.

Returns

High-Level API for Training.

Return type

model (Object)

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore import nn
>>> from mindspore import Tensor
>>> from mindspore.nn import thor
>>>
>>> net = Net()
>>> dataset = create_dataset()
>>> temp = Tensor([4e-4, 1e-4, 1e-5, 1e-5], mstype.float32)
>>> opt = thor(net, learning_rate=temp, damping=temp, momentum=0.9, loss_scale=128, frequency=4)
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> loss_scale = ms.FixedLossScaleManager(128, drop_overflow_update=False)
>>> model = ms.Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
...               amp_level="O2", keep_batchnorm_fp32=False)
>>> model = ConvertModelUtils.convert_to_thor_model(model=model, network=net, loss_fn=loss, optimizer=opt,
...                                                 loss_scale_manager=loss_scale, metrics={'acc'},
...                                                 amp_level="O2", keep_batchnorm_fp32=False)
>>> loss_cb = ms.LossMonitor()
>>> model.train(1, dataset, callbacks=loss_cb, sink_size=4, dataset_sink_mode=True)