Source code for tllib.translation.spgan.loss

Modified from
@author: Baixu Chen
import torch
import torch.nn.functional as F

[docs]class ContrastiveLoss(torch.nn.Module): r"""Contrastive loss from `Dimensionality Reduction by Learning an Invariant Mapping (CVPR 2006) <>`_. Given output features :math:`f_1, f_2`, we use :math:`D` to denote the pairwise euclidean distance between them, :math:`Y` to denote the ground truth labels, :math:`m` to denote a pre-defined margin, then contrastive loss is calculated as .. math:: (1 - Y)\frac{1}{2}D^2 + (Y)\frac{1}{2}\{\text{max}(0, m-D)^2\} Args: margin (float, optional): margin for contrastive loss. Default: 2.0 Inputs: - output1 (tensor): feature representations of the first set of samples (:math:`f_1` here). - output2 (tensor): feature representations of the second set of samples (:math:`f_2` here). - label (tensor): labels (:math:`Y` here). Shape: - output1, output2: :math:`(minibatch, F)` where F means the dimension of input features. - label: :math:`(minibatch, )` """ def __init__(self, margin=2.0): super(ContrastiveLoss, self).__init__() self.margin = margin def forward(self, output1, output2, label): euclidean_distance = F.pairwise_distance(output1, output2) loss = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2)) return loss


Access comprehensive documentation for Transfer Learning Library

View Docs


Get started for Transfer Learning Library

Get Started

Paper List

Get started for transfer learning

View Resources