未验证 提交 d1f3229b 编写于 作者: Z Zhang Ting 提交者: GitHub

remove init_on_cpu because it does not take effect, test=develop (#29)

上级 31a8b805
......@@ -20,7 +20,6 @@ import math
import paddle.fluid as fluid
import paddle.fluid.layers.ops as ops
from paddle.fluid.initializer import init_on_cpu
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
lr_strategy = 'cosine_decay'
......@@ -40,10 +39,9 @@ def cosine_decay(learning_rate, step_each_epoch, epochs=120):
"""
global_step = _decay_step_counter()
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
decayed_lr = learning_rate * \
(ops.cos(epoch * (math.pi / epochs)) + 1)/2
epoch = ops.floor(global_step / step_each_epoch)
decayed_lr = learning_rate * \
(ops.cos(epoch * (math.pi / epochs)) + 1)/2
return decayed_lr
......@@ -63,17 +61,16 @@ def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
warmup_epoch = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(5), force_cpu=True)
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
......@@ -95,19 +92,18 @@ def exponential_decay_with_warmup(learning_rate,
warmup_epoch = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(warm_up_epoch), force_cpu=True)
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.assign(input=decayed_lr, output=lr)
with switch.default():
div_res = (global_step - warmup_epoch * step_each_epoch
) / decay_epochs
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
fluid.layers.assign(input=decayed_lr, output=lr)
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.assign(input=decayed_lr, output=lr)
with switch.default():
div_res = (global_step - warmup_epoch * step_each_epoch
) / decay_epochs
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
fluid.layers.assign(input=decayed_lr, output=lr)
return lr
......
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