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update in the frobenius norm, adding and squrt

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Alejandro Moreo Fernandez 2021-02-09 15:38:18 +01:00
parent 38867371fa
commit 9f3567c4f8
1 changed files with 2 additions and 11 deletions
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13
src/losses.py
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@ -107,16 +107,6 @@ class SupConLoss1View(nn.Module):
self.base_temperature = base_temperature self.base_temperature = base_temperature
def forward(self, features, labels): def forward(self, features, labels):
"""Compute loss for model. If both `labels` and `mask` are None,
it degenerates to SimCLR unsupervised loss:
https://arxiv.org/pdf/2002.05709.pdf
Args:
features: hidden vector of shape [bsz, ndim].
labels: ground truth of shape [bsz].
Returns:
A loss scalar.
"""
device = (torch.device('cuda') device = (torch.device('cuda')
if features.is_cuda if features.is_cuda
else torch.device('cpu')) else torch.device('cpu'))
@ -146,7 +136,8 @@ class SupConLoss1View(nn.Module):
#return neg_loss, pos_loss #return neg_loss, pos_loss
#balanced_loss = pos_loss + neg_loss #balanced_loss = pos_loss + neg_loss
#return balanced_loss #return balanced_loss
return torch.mean((cross_upper-mask_upper)**2), neg_loss, pos_loss n=len(mask_upper)
return (1/n)*torch.sqrt(torch.sum((cross_upper-mask_upper)**2)), neg_loss, pos_loss
def mse(input, target, label): def mse(input, target, label):