Libra R-CNN Detection Model

gluoncv/loss.py

@@ -6,10 +6,49 @@
 from mxnet import gluon
 from mxnet import nd
 from mxnet.gluon.loss import Loss, _apply_weighting, _reshape_like
+import numpy as np
 
 __all__ = ['FocalLoss', 'SSDMultiBoxLoss', 'YOLOV3Loss',
  'MixSoftmaxCrossEntropyLoss', 'ICNetLoss', 'MixSoftmaxCrossEntropyOHEMLoss',
- 'SegmentationMultiLosses', 'DistillationSoftmaxCrossEntropyLoss', 'SiamRPNLoss']
+ 'SegmentationMultiLosses', 'DistillationSoftmaxCrossEntropyLoss', 'SiamRPNLoss', 'BalancedL1Loss']
+
+class BalancedL1Loss(Loss):
+ r"""Balanced L1 Loss for balanced classification.
+ Balanced L1 loss was described in https://arxiv.org/abs/1904.02701
+ Libra R-CNN: Towards Balanced Learning for Object Detection.
+
+ Parameters
+ ----------
+ rho : float, default 1
+ Threshold for trimmed mean estimator.
+ alpha : float, default 0.5
+ Threshold for increases the gradients of inliers.
+ gamma : float, default 1.5
+ Threshold for an overall promotion magnification.
+ weight : float or None
+ Global scalar weight for loss.
+ batch_axis : int, default 0
+ The axis that represents mini-batch.
+
+ """
+
+ def __init__(self, rho=1, alpha=0.5, gamma=1.5, weight=None, batch_axis=0, **kwargs):
+ super(BalancedL1Loss, self).__init__(weight, batch_axis, **kwargs)
+ self._rho = rho
+ self._alpha = alpha
+ self._gamma = gamma
+
+ def hybrid_forward(self, F, pred, label, sample_weight=None):
+ label = _reshape_like(F, label, pred)
+ loss = F.abs(label - pred)
+ b = np.exp(self._gamma / self._alpha) - 1
+ loss = F.where(
+ loss < self._rho, self._alpha / b *
+ (b * loss + 1) * F.log(b * loss / self._rho + 1) - self._alpha * loss,
+ self._gamma * loss + self._gamma / b - self._alpha * self._rho)
+ loss = _apply_weighting(F, loss, self._weight, sample_weight)
+
+ return F.mean(loss, axis=self._batch_axis, exclude=True)
 
 class FocalLoss(Loss):
  """Focal Loss for inbalanced classification.