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add space after '

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chenfei 2021-06-15 17:16:24 +08:00
parent a3d6da8987
commit a9fe1fbfd3
2 changed files with 13 additions and 13 deletions
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18
mindspore/train/amp.py
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@ -124,26 +124,26 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
- O0: Do not change.
- O2: Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32,
using dynamic loss scale.
- O3: Cast network to float16, with additional property `keep_batchnorm_fp32=False`.
- O3: Cast network to float16, with additional property `keep_batchnorm_fp32=False` .
- auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set
level to O3 Ascend. The recommended level is choose by the export experience, cannot
always general. User should specify the level for special network.
O2 is recommended on GPU, O3 is recommended on Ascend.Property of `keep_batchnorm_fp32` , `cast_model_type`
and `loss_scale_manager` determined by `level` setting may be overwritten by settings in `kwargs`.
and `loss_scale_manager` determined by `level` setting may be overwritten by settings in `kwargs` .
cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`.If set, the network
will be casted to `cast_model_type`(`mstype.float16` or `mstype.float32`), but not to be casted to the type
determined by `level` setting.
keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32` when the network is set to cast to `float16`.
cast_model_type (:class: `mindspore.dtype` ): Supports `mstype.float16` or `mstype.float32` .If set, the network
will be casted to `cast_model_type` ( `mstype.float16` or `mstype.float32` ), but not to be casted to the
type determined by `level` setting.
keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32` when the network is set to cast to `float16` .
If set, the `level` setting will take no effect on this property.
loss_scale_manager (Union[None, LossScaleManager]): If None, not scale the loss, otherwise scale the loss by
`LossScaleManager`. If set, the `level` setting will take no effect on this property.
`LossScaleManager` . If set, the `level` setting will take no effect on this property.
Raises:
1.Auto mixed precision only supported on device GPU and Ascend.If device is CPU, a `ValueError` exception
will be raised.
2.If device is CPU, property `loss_scale_manager` only can be set as `None` or `FixedLossScaleManager`(with
property `drop_overflow_update=False`), or a `ValueError` exception will be raised.
2.If device is CPU, property `loss_scale_manager` only can be set as `None` or `FixedLossScaleManager` (with
property `drop_overflow_update=False` ), or a `ValueError` exception will be raised.
"""
validator.check_value_type('network', network, nn.Cell)
validator.check_value_type('optimizer', optimizer, (nn.Optimizer, acc.FreezeOpt))

8
mindspore/train/model.py
View File

@ -69,7 +69,7 @@ class Model:
metrics (Union[dict, set]): A Dictionary or a set of metrics to be evaluated by the model during
training and testing. eg: {'accuracy', 'recall'}. Default: None.
eval_network (Cell): Network for evaluation. If not defined, `network` and `loss_fn` would be wrapped as
`eval_network`. Default: None.
`eval_network` . Default: None.
eval_indexes (list): When defining the `eval_network`, if `eval_indexes` is None, all outputs of the
`eval_network` would be passed to metrics, otherwise `eval_indexes` must contain three
elements, including the positions of loss value, predicted value and label. The loss
@ -77,18 +77,18 @@ class Model:
to other metric. Default: None.
Args:
amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network` , 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`.
- O3: Cast network to float16, with additional property `keep_batchnorm_fp32=False` .
- auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set
level to O3 Ascend. The recommended level is choose by the export experience, cannot
always general. User should specify the level for special network.
O2 is recommended on GPU, O3 is recommended on Ascend.The more detailed explanation of `amp_level` setting
can be found at `mindspore.amp.build_train_network`.
can be found at `mindspore.amp.build_train_network` .
Examples:
>>> from mindspore import Model, nn
>>>