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nutszebra_optimizer.py
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nutszebra_optimizer.py
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import chainer
from chainer import optimizers
import nutszebra_basic_print
class Optimizer(object):
def __init__(self, model=None):
self.model = model
self.optimizer = None
def __call__(self, i):
pass
def update(self):
self.optimizer.update()
class OptimizerResnet(Optimizer):
def __init__(self, model=None, schedule=(int(32000. / (50000. / 128)), int(48000. / (50000. / 128))), lr=0.1, momentum=0.9, weight_decay=1.0e-4, warm_up_lr=0.01):
super(OptimizerResnet, self).__init__(model)
optimizer = optimizers.MomentumSGD(warm_up_lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.schedule = schedule
self.lr = lr
self.warmup_lr = warm_up_lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
if i == 1:
lr = self.lr
print('finishded warming up')
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
if i in self.schedule:
lr = self.optimizer.lr / 10
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerDense(Optimizer):
def __init__(self, model=None, schedule=(150, 225), lr=0.1, momentum=0.9, weight_decay=1.0e-4):
super(OptimizerDense, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.schedule = schedule
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
if i in self.schedule:
lr = self.optimizer.lr / 10
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerWideRes(Optimizer):
def __init__(self, model=None, schedule=(60, 120, 160), lr=0.1, momentum=0.9, weight_decay=5.0e-4):
super(OptimizerWideRes, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.schedule = schedule
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
if i in self.schedule:
lr = self.optimizer.lr * 0.2
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerSwapout(Optimizer):
def __init__(self, model=None, schedule=(196, 224), lr=0.1, momentum=0.9, weight_decay=1.0e-4):
super(OptimizerSwapout, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.schedule = schedule
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
if i in self.schedule:
lr = self.optimizer.lr / 10
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerXception(Optimizer):
def __init__(self, model=None, lr=0.045, momentum=0.9, weight_decay=1.0e-5, period=2):
super(OptimizerXception, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
self.period = int(period)
def __call__(self, i):
if i % self.period == 0:
lr = self.optimizer.lr * 0.94
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerVGG(Optimizer):
def __init__(self, model=None, lr=0.01, momentum=0.9, weight_decay=5.0e-4):
super(OptimizerVGG, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
# 150 epoch means (0.94 ** 75) * lr
# if lr is 0.01, then (0.94 ** 75) * 0.01 is 0.0001 at the end
if i % 2 == 0:
lr = self.optimizer.lr * 0.94
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerGooglenet(Optimizer):
def __init__(self, model=None, lr=0.0015, momentum=0.9, weight_decay=2.0e-4):
super(OptimizerGooglenet, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
def __call__(self, i):
if i % 8 == 0:
lr = self.optimizer.lr * 0.96
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr
class OptimizerNetworkInNetwork(Optimizer):
def __init__(self, model=None, lr=0.1, momentum=0.9, weight_decay=1.0e-4, schedule=(int(1.0e5 / (50000. / 128)), )):
super(OptimizerNetworkInNetwork, self).__init__(model)
optimizer = optimizers.MomentumSGD(lr, momentum)
weight_decay = chainer.optimizer.WeightDecay(weight_decay)
optimizer.setup(self.model)
optimizer.add_hook(weight_decay)
self.optimizer = optimizer
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
self.schedule = schedule
def __call__(self, i):
if i in self.schedule:
lr = self.optimizer.lr / 10
print('lr is changed: {} -> {}'.format(self.optimizer.lr, lr))
self.optimizer.lr = lr