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PaddleOCR
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switch learning_rate and lr

This commit is contained in:
WenmuZhou 2020-11-05 20:49:44 +08:00
parent d092a5a22f
commit 695c4db7ea
1 changed files with 23 additions and 21 deletions
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44
ppocr/optimizer/learning_rate.py
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@ -17,7 +17,7 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from paddle.optimizer import lr as lr_scheduler
from paddle.optimizer import lr
class Linear(object):
@ -32,7 +32,7 @@ class Linear(object):
"""
def __init__(self,
lr,
learning_rate,
epochs,
step_each_epoch,
end_lr=0.0,
@ -41,7 +41,7 @@ class Linear(object):
last_epoch=-1,
**kwargs):
super(Linear, self).__init__()
self.lr = lr
self.learning_rate = learning_rate
self.epochs = epochs * step_each_epoch
self.end_lr = end_lr
self.power = power
@ -49,18 +49,18 @@ class Linear(object):
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.PolynomialLR(
learning_rate=self.lr,
learning_rate = lr.PolynomialDecay(
learning_rate=self.learning_rate,
decay_steps=self.epochs,
end_lr=self.end_lr,
power=self.power,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -77,27 +77,29 @@ class Cosine(object):
"""
def __init__(self,
lr,
learning_rate,
step_each_epoch,
epochs,
warmup_epoch=0,
last_epoch=-1,
**kwargs):
super(Cosine, self).__init__()
self.lr = lr
self.learning_rate = learning_rate
self.T_max = step_each_epoch * epochs
self.last_epoch = last_epoch
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.CosineAnnealingLR(
learning_rate=self.lr, T_max=self.T_max, last_epoch=self.last_epoch)
learning_rate = lr.CosineAnnealingDecay(
learning_rate=self.learning_rate,
T_max=self.T_max,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -115,7 +117,7 @@ class Step(object):
"""
def __init__(self,
lr,
learning_rate,
step_size,
step_each_epoch,
gamma,
@ -124,23 +126,23 @@ class Step(object):
**kwargs):
super(Step, self).__init__()
self.step_size = step_each_epoch * step_size
self.lr = lr
self.learning_rate = learning_rate
self.gamma = gamma
self.last_epoch = last_epoch
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.StepLR(
learning_rate=self.lr,
learning_rate = lr.StepDecay(
learning_rate=self.learning_rate,
step_size=self.step_size,
gamma=self.gamma,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -169,12 +171,12 @@ class Piecewise(object):
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.PiecewiseLR(
learning_rate = lr.PiecewiseDecay(
boundaries=self.boundaries,
values=self.values,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,