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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
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