Hi,

I am trying to implement center loss in fast.ai, but struggling with format.

https://github.com/KaiyangZhou/pytorch-center-loss ( Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.)

PyTorch implementation loos like this:

```
class CenterLoss(nn.Module):
"""Center loss.
Reference:
Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
Args:
num_classes (int): number of classes.
feat_dim (int): feature dimension.
"""
def __init__(self, num_classes=10, feat_dim=2, use_gpu=True):
super(CenterLoss, self).__init__()
self.num_classes = num_classes
self.feat_dim = feat_dim
self.use_gpu = use_gpu
if self.use_gpu:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
else:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
def forward(self, x, labels):
"""
Args:
x: feature matrix with shape (batch_size, feat_dim).
labels: ground truth labels with shape (batch_size).
"""
batch_size = x.size(0)
distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
distmat.addmm_(1, -2, x, self.centers.t())
classes = torch.arange(self.num_classes).long()
if self.use_gpu: classes = classes.cuda()
labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
mask = labels.eq(classes.expand(batch_size, self.num_classes))
dist = []
for i in range(batch_size):
value = distmat[i][mask[i]]
value = value.clamp(min=1e-12, max=1e+12) # for numerical stability
dist.append(value)
dist = torch.cat(dist)
loss = dist.mean()
return loss
```

I cannot implement it as just function, because it has its own parameters to optimize and added to loss. I also cannot just use it as final layer, because I need softmax and usual output as well. Could anyone please point out where to look for a clue?