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-rw-r--r--libs/criteria.py25
1 files changed, 25 insertions, 0 deletions
diff --git a/libs/criteria.py b/libs/criteria.py
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+++ b/libs/criteria.py
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+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+
+class InfoNCELoss(nn.Module):
+ def __init__(self, temp=0.01):
+ super().__init__()
+ self.temp = temp
+
+ def forward(self, feat1: Tensor, feat2: Tensor) -> tuple[Tensor, Tensor]:
+ bz = feat1.size(0)
+ feat1_norm = F.normalize(feat1)
+ feat2_norm = F.normalize(feat2)
+ logits = feat1_norm @ feat2_norm.T
+ pos_logits_mask = torch.eye(bz, dtype=torch.bool)
+ pos_logits = logits[pos_logits_mask].unsqueeze(-1)
+ neg_logits = logits[~pos_logits_mask].view(bz, -1)
+ # Put the positive at first (0-th) and maximize its likelihood
+ logits = torch.cat([pos_logits, neg_logits], dim=1)
+ labels = torch.zeros(bz, dtype=torch.long, device=feat1.device)
+ loss_contra = F.cross_entropy(logits / self.temp, labels)
+ acc_contra = (logits.argmax(dim=1) == labels).float().mean()
+
+ return loss_contra, acc_contra