def forward(self, inputs, device):
x, gamma, beta, mean, var = inputs
with testing.assert_warns(DeprecationWarning):
y = batch_renormalization.fixed_batch_renormalization(x,
gamma,
beta,
mean,
var,
eps=self.eps)
return y,
def check_forward(self, args):
with chainer.using_config('train', self.train):
y = batch_renormalization.fixed_batch_renormalization(
*[chainer.Variable(i) for i in args], eps=self.eps)
self.assertEqual(y.data.dtype, self.dtype)
y_expect = _batch_renormalization(self.expander, self.gamma, self.beta,
self.x, self.mean, self.var, 1, 0)
testing.assert_allclose(y_expect, y.data, **self.check_forward_options)
def check_forward(self, args):
with chainer.using_config('train', self.train):
y = batch_renormalization.fixed_batch_renormalization(
*[chainer.Variable(i) for i in args],
eps=self.eps)
self.assertEqual(y.data.dtype, self.dtype)
y_expect = _batch_renormalization(
self.expander, self.gamma, self.beta, self.x, self.mean, self.var,
1, 0)
testing.assert_allclose(
y_expect, y.data, **self.check_forward_options)
def __call__(self, x, finetune=False):
if self.gamma is not None:
gamma = self.gamma
else:
with cuda.get_device(self._device_id):
gamma = variable.Variable(
self.xp.ones(self.avg_mean.shape, dtype=x.dtype))
if self.beta is not None:
beta = self.beta
else:
with cuda.get_device(self._device_id):
beta = variable.Variable(
self.xp.zeros(self.avg_mean.shape, dtype=x.dtype))
if configuration.config.train:
if finetune:
self.N += 1
decay = 1. - 1. / self.N
else:
decay = self.decay
func = batch_renormalization.BatchRenormalizationFunction(
self.eps, self.avg_mean, self.avg_var, decay, self.rmax,
self.dmax, self.freeze_running_statistics)
if self.freeze_running_statistics:
func.r = self.r
func.d = self.d
ret = func(x, gamma, beta)
if self.freeze_running_statistics and self.r is None:
self.r = func.r
self.d = func.d
self.avg_mean[:] = func.running_mean
self.avg_var[:] = func.running_var
else:
# Use running average statistics or fine-tuned statistics.
mean = variable.Variable(self.avg_mean)
var = variable.Variable(self.avg_var)
ret = batch_renormalization.fixed_batch_renormalization(
x, gamma, beta, mean, var, self.eps)
return ret
def __call__(self, x, finetune=False):
if self.gamma is not None:
gamma = self.gamma
else:
with cuda.get_device_from_id(self._device_id):
gamma = variable.Variable(self.xp.ones(
self.avg_mean.shape, dtype=x.dtype))
if self.beta is not None:
beta = self.beta
else:
with cuda.get_device_from_id(self._device_id):
beta = variable.Variable(self.xp.zeros(
self.avg_mean.shape, dtype=x.dtype))
if configuration.config.train:
if finetune:
self.N += 1
decay = 1. - 1. / self.N
else:
decay = self.decay
func = batch_renormalization.BatchRenormalizationFunction(
self.eps, self.avg_mean, self.avg_var, decay,
self.rmax, self.dmax, self.freeze_running_statistics)
if self.freeze_running_statistics:
func.r = self.r
func.d = self.d
ret = func(x, gamma, beta)
if self.freeze_running_statistics and self.r is None:
self.r = func.r
self.d = func.d
self.avg_mean[:] = func.running_mean
self.avg_var[:] = func.running_var
else:
# Use running average statistics or fine-tuned statistics.
mean = variable.Variable(self.avg_mean)
var = variable.Variable(self.avg_var)
ret = batch_renormalization.fixed_batch_renormalization(
x, gamma, beta, mean, var, self.eps)
return ret
def _forward(self, *args):
with testing.assert_warns(DeprecationWarning):
return batch_renormalization.fixed_batch_renormalization(
*args, eps=self.eps)
def _forward(self, *args):
with testing.assert_warns(DeprecationWarning):
return batch_renormalization.fixed_batch_renormalization(
*args, eps=self.eps)
def forward(self, inputs, device):
x, gamma, beta, mean, var = inputs
with testing.assert_warns(DeprecationWarning):
y = batch_renormalization.fixed_batch_renormalization(
x, gamma, beta, mean, var, eps=self.eps)
return y,
def _forward(self, *args):
return batch_renormalization.fixed_batch_renormalization(*args,
eps=self.eps)
