Optimize batching

1 files changed, 5 insertions, 5 deletions

diff --git a/libs/criteria.py b/libs/criteria.py
index 6954cf3..baa36ce 100644
--- a/libs/criteria.py
+++ b/libs/criteria.py
@@ -8,17 +8,17 @@ class InfoNCELoss(nn.Module):
         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)
+    def forward(self, feature: Tensor) -> tuple[Tensor, Tensor]:
+        bz = feature.size(0) // 2
+        feat_norm = F.normalize(feature)
+        feat1_norm, feat2_norm = feat_norm.split(bz)
         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)
+        labels = torch.zeros(bz, dtype=torch.long, device=feature.device)
         loss_contra = F.cross_entropy(logits / self.temp, labels)
         acc_contra = (logits.argmax(dim=1) == labels).float().mean()