Upload ProDiff/Experiments/trajectory_exp_may_data_TKY_len3_ddpm_20250724-100624/code_snapshot/loss.py with huggingface_hub

ProDiff/Experiments/trajectory_exp_may_data_TKY_len3_ddpm_20250724-100624/code_snapshot/loss.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ContrastiveLoss(nn.Module):
+    """Contrastive loss function.
+    
+    Encourages 'anchor' to be close to 'positive' samples and far from 'negative' samples.
+    """
+    def __init__(self, margin=1.0):
+        """Initializes ContrastiveLoss.
+
+        Args:
+            margin (float, optional): The margin for the loss. Defaults to 1.0.
+        """
+        super(ContrastiveLoss, self).__init__()
+        self.margin = margin
+
+    def forward(self, anchor, positive, negative):
+        """Computes the contrastive loss.
+
+        Args:
+            anchor (torch.Tensor): Embeddings of the anchor samples.
+            positive (torch.Tensor): Embeddings of the positive samples.
+            negative (torch.Tensor): Embeddings of the negative samples.
+
+        Returns:
+            torch.Tensor: The mean contrastive loss.
+        """
+        pos_dist = F.pairwise_distance(anchor, positive)
+        neg_dist = F.pairwise_distance(anchor, negative)
+        # Loss = max(0, pos_dist - neg_dist + margin)
+        loss = torch.mean(F.relu(pos_dist - neg_dist + self.margin))
+        return loss