SmoothL1Loss¶

class dragon.vm.torch.nn.SmoothL1Loss(
  beta=1.0,
  size_average=None,
  reduce=None,
  reduction='mean'
)[source]¶

Compute the element-wise error transited from L1 and L2. [Girshick, 2015].

The SmoothL1Loss function is defined as:

\[\text{SmoothL1Loss}(x, y) = \begin{cases} 0.5 * (x - y)^{2} / beta, & \text{ if } |x - y| < beta \\ |x - y| - 0.5 * beta, & \text{ otherwise } \end{cases} \]

Examples:

m = torch.nn.SmoothL1Loss(beta=0.11)
loss = m(torch.ones(2, 3), torch.zeros(2, 3))

See also

torch.nn.functional.smooth_l1_loss(…)