unsky/focal-loss and speed up 30% than Original implement during the training.
This is the optimized version of focal loss in MXNet, which is modified from- The use of focal loss is same as unsky/focal-loss)
from focal_loss_OptimizedVersion import *
label = mx.sym.Variable('focalloss_label')
net = mx.symbol.Custom(data=net, op_type='FocalLoss', labels = label, name='focalloss', alpha=0.25, gamma=2)
- Apart from
focal_loss_OptimizedVersion.py
, I alse providemetric.py
for presenting focal loss value by taking image classification as example:
from metric import *
eval_metric = mx.metric.CompositeEvalMetric()
eval_metric.add(FocalLoss())
model = mx.mod.Module(
context=mx.gpu(0),
symbol=symbol,
label_names=('focalloss_label',)
)
model.fit(...,
eval_metric=eval_metric,
...)
Attention: The value of alpha and gamma in metric.py
should be equal to mx.symbol.Custom(...,alpha, gamma)