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!9552 change the import level of dynamic_lr 

From: @wangnan39
Reviewed-by: @kingxian,@zh_qh,@kingxian
Signed-off-by: @kingxian,@kingxian
This commit is contained in:
mindspore-ci-bot 2020-12-10 11:15:08 +08:00 committed by Gitee
parent 7349dd2bb9 0fe9e2e4cb
commit 9ecf062581
2 changed files with 6 additions and 6 deletions
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2
mindspore/nn/__init__.py
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@ -19,6 +19,7 @@ Pre-defined building blocks or computing units to construct Neural Networks.
"""
from . import layer, loss, optim, metrics, wrap, probability, sparse
from .learning_rate_schedule import *
from .dynamic_lr import *
from .cell import Cell, GraphKernel
from .layer import *
from .loss import *
@ -36,5 +37,6 @@ __all__.extend(metrics.__all__)
__all__.extend(wrap.__all__)
__all__.extend(sparse.__all__)
__all__.extend(learning_rate_schedule.__all__)
__all__.extend(dynamic_lr.__all__)
__all__.sort()

10
tests/ut/python/nn/optim/test_adam.py
View File

@ -22,8 +22,6 @@ from mindspore.common.api import _executor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Adam, AdamWeightDecay
from mindspore.ops import operations as P
import mindspore.nn.learning_rate_schedule as lr_schedules
from mindspore.nn.dynamic_lr import polynomial_decay_lr
context.set_context(enable_sparse=True)
@ -137,7 +135,7 @@ def test_adam_group1():
net_with_loss = WithLossCell(net, loss)
all_params = net.trainable_params()
poly_decay_lr = polynomial_decay_lr(0.01, 0.0001, total_step=10, step_per_epoch=1, decay_epoch=3, power=1.0)
poly_decay_lr = nn.polynomial_decay_lr(0.01, 0.0001, total_step=10, step_per_epoch=1, decay_epoch=3, power=1.0)
group_params = [{'params': [all_params[0]], 'lr': poly_decay_lr, 'weight_decay': 0.9},
{'params': [all_params[1]]}]
@ -157,7 +155,7 @@ def test_adam_group2():
net_with_loss = WithLossCell(net, loss)
all_params = net.trainable_params()
schedule_lr = lr_schedules.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
schedule_lr = nn.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
group_params = [{'params': [all_params[0]], 'lr': 0.02, 'weight_decay': 0.9},
{'params': [all_params[1]]}]
optimizer = nn.Adam(group_params, learning_rate=schedule_lr)
@ -175,7 +173,7 @@ def test_adamweightdecay_group():
net_with_loss = WithLossCell(net, loss)
all_params = net.trainable_params()
schedule_lr = lr_schedules.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
schedule_lr = nn.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
group_params = [{'params': [all_params[0]], 'lr': 0.02, 'weight_decay': 0.9},
{'params': [all_params[1]]}]
optimizer = nn.AdamWeightDecay(group_params, learning_rate=schedule_lr)
@ -193,7 +191,7 @@ def test_adamoffload_group():
net_with_loss = WithLossCell(net, loss)
all_params = net.trainable_params()
schedule_lr = lr_schedules.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
schedule_lr = nn.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
group_params = [{'params': [all_params[0]], 'lr': 0.02, 'weight_decay': 0.9},
{'params': [all_params[1]]}]
optimizer = nn.AdamOffload(group_params, learning_rate=schedule_lr)