def weighted_sigmoid_rule(nb_epoch: int, steps: int = 10, **kwargs):
""" The probability to apply each augmentation increases or decreases following a sigmoid curve.
Those probabilities are weighted using three lambda value.
:param nb_epoch: The number of epoch the rule will be effective.
:param steps: The number of time the probabilities will be updated.
:param lamdda_sup_max: Weight of the Lsup loss.
:param lambda_cot_max: Weight of the Lcot loss.
:param lambda_dif_max: Weight of the Ldiff loss.
"""
lcm = kwargs.get("lambda_cot_max", 1)
ldm = kwargs.get("lambda_diff_max", 1)
lsm = kwargs.get("lambda_sup_max", 1)
hop_length = np.linspace(0, nb_epoch, steps)
sup_steps = np.asarray(
[lsm * sigmoid_rampdown(x, nb_epoch) for x in hop_length])
cot_steps = np.asarray(
[lcm * sigmoid_rampup(x, nb_epoch) for x in hop_length])
diff_steps = np.asarray(
[ldm * sigmoid_rampup(x, nb_epoch) for x in hop_length])
# normalize
for i in range(steps):
summed = sup_steps[i] + cot_steps[i] + diff_steps[i]
sup_steps[i] /= summed
cot_steps[i] /= summed
diff_steps[i] /= summed
return sup_steps, cot_steps, diff_steps
def get_current_consistency_weight(final_consistency_weight, epoch,
step_in_epoch, total_steps_in_epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
epoch = epoch - args.consistency_rampup_starts
epoch = epoch + step_in_epoch / total_steps_in_epoch
return final_consistency_weight * ramps.sigmoid_rampup(
epoch, args.consistency_rampup_ends - args.consistency_rampup_starts)
def get_current_consistency_weight(self, epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
if epoch < self.config.t_start:
self.consistency_weight = 0.
else:
self.consistency_weight = ramps.sigmoid_rampup(
epoch, self.config.c_rampup)
def sigmoid_rule(nb_epoch: int, steps: int = 10, **kwargs):
""" The probability to apply each augmentation increases or decreases following a sigmoid curve.
:param nb_epoch: The number of epoch the rule will be effective.
:param steps: The number of time the probabilities will be updated.
"""
hop_length = np.linspace(0, nb_epoch, steps)
sup_steps = np.asarray([sigmoid_rampdown(x, nb_epoch) for x in hop_length])
cot_steps = np.asarray([sigmoid_rampup(x, nb_epoch) for x in hop_length])
diff_steps = np.asarray([sigmoid_rampup(x, nb_epoch) for x in hop_length])
# normalize
for i in range(steps):
summed = sup_steps[i] + cot_steps[i] + diff_steps[i]
sup_steps[i] /= summed
cot_steps[i] /= summed
diff_steps[i] /= summed
return sup_steps, cot_steps, diff_steps
def get_current_consistency_weight(epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
return FLAGS.consistency_weight * ramps.sigmoid_rampup(epoch, FLAGS.consistency_rampup)
def get_current_consistency_weight(epoch):
return args.consistency * ramps.sigmoid_rampup(epoch,
args.consistency_rampup)
def get_current_consistency_weight(args, epoch, step_in_epoch, total_steps_in_epoch):
epoch = epoch - args.consistency_rampup_starts
epoch = epoch + step_in_epoch / total_steps_in_epoch
return args.lmbda * ramps.sigmoid_rampup(epoch, args.consistency_rampup_ends - args.consistency_rampup_starts)