def next(self):
batch = self.__next__()
inks = [a[0] for a in batch]
label = [a[1] for a in batch]
inks = F.pad_sequence(inks)
new_batch = list(zip(inks.data, label))
return new_batch
def __call__(self, x, test=False, finetune=False):
"""Invokes the forward propagation of BatchNormalization.
BatchNormalization accepts additional arguments, which controlls three
different running mode.
Args:
x (Variable): An input variable.
test (bool): If ``True``, BatchNormalization runs in testing mode;
it normalizes the input using precomputed statistics.
finetune (bool): If ``True``, BatchNormalization runs in finetuning
mode; it accumulates the input array to compute population
statistics for normalization, and normalizes the input using
batch statistics.
If ``test`` and ``finetune`` are both ``False``, then BatchNormalization
runs in training mode; it computes moving averages of mean and variance
for evaluation during training, and normalizes the input using batch
statistics.
"""
self.use_batch_mean = not test or finetune
self.is_finetune = finetune
return Function.__call__(self, x)
def __call__(self, x, test=False, finetune=False):
"""Invokes the forward propagation of BatchNormalization.
BatchNormalization accepts additional arguments, which controlls three
different running mode.
Args:
x (Variable): An input variable.
test (bool): If ``True``, BatchNormalization runs in testing mode;
it normalizes the input using precomputed statistics.
finetune (bool): If ``True``, BatchNormalization runs in finetuning
mode; it accumulates the input array to compute population
statistics for normalization, and normalizes the input using
batch statistics.
If ``test`` and ``finetune`` are both ``False``, then BatchNormalization
runs in training mode; it computes moving averages of mean and variance
for evaluation during training, and normalizes the input using batch
statistics.
"""
self.use_batch_mean = not test or finetune
self.is_finetune = finetune
return Function.__call__(self, x)
def __call__(self, x, test=False, finetune=False,update_batch_estimations=True):
self.use_batch_mean = not test
self.is_finetune = finetune
self.update_batch_estimations = update_batch_estimations
return Function.__call__(self, x)
