contrastive_loss.py

import torch
import torch.nn as nn
import math


class InstanceLoss(nn.Module):
    def __init__(self, batch_size, temperature, device):
        super(InstanceLoss, self).__init__()
        self.batch_size = batch_size
        self.temperature = temperature
        self.device = device

        self.mask = self.mask_correlated_samples(batch_size)
        self.criterion = nn.CrossEntropyLoss(reduction="sum")

    def mask_correlated_samples(self, batch_size):
        N = 2 * batch_size
        mask = torch.ones((N, N))
        mask = mask.fill_diagonal_(0)
        for i in range(batch_size):
            mask[i, batch_size + i] = 0
            mask[batch_size + i, i] = 0
        mask = mask.bool()
        return mask

    def forward(self, z_i, z_j):
        N = 2 * self.batch_size
        z = torch.cat((z_i, z_j), dim=0)

        sim = torch.matmul(z, z.T) / self.temperature
        sim_i_j = torch.diag(sim, self.batch_size)
        sim_j_i = torch.diag(sim, -self.batch_size)

        positive_samples = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(N, 1)
        negative_samples = sim[self.mask].reshape(N, -1)

        labels = torch.zeros(N).to(positive_samples.device).long()
        logits = torch.cat((positive_samples, negative_samples), dim=1)
        loss = self.criterion(logits, labels)
        loss /= N

        return loss




# class ClusterLoss(nn.Module):
#     def __init__(self, class_num, temperature, device):
#         super(ClusterLoss, self).__init__()
#         self.class_num = class_num
#         self.temperature = temperature
#         self.device = device
#
#         self.mask = self.mask_correlated_clusters(class_num)
#         self.criterion = nn.CrossEntropyLoss(reduction="sum")
#         self.similarity_f = nn.CosineSimilarity(dim=2)
#
#     def mask_correlated_clusters(self, class_num):
#         N = 2 * class_num
#         mask = torch.ones((N, N))
#         mask = mask.fill_diagonal_(0)
#         for i in range(class_num):
#             mask[i, class_num + i] = 0
#             mask[class_num + i, i] = 0
#         mask = mask.bool()
#         return mask
#
#     def forward(self, c_i, c_j):
#         p_i = c_i.sum(0).view(-1)
#         p_i /= p_i.sum()
#         ne_i = math.log(p_i.size(0)) + (p_i * torch.log(p_i)).sum()
#         p_j = c_j.sum(0).view(-1)
#         p_j /= p_j.sum()
#         ne_j = math.log(p_j.size(0)) + (p_j * torch.log(p_j)).sum()
#         ne_loss = ne_i + ne_j
#
#         c_i = c_i.t()
#         c_j = c_j.t()
#         N = 2 * self.class_num
#         c = torch.cat((c_i, c_j), dim=0)
#
#         sim = self.similarity_f(c.unsqueeze(1), c.unsqueeze(0)) / self.temperature
#         sim_i_j = torch.diag(sim, self.class_num)
#         sim_j_i = torch.diag(sim, -self.class_num)
#
#         positive_clusters = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(N, 1)
#         negative_clusters = sim[self.mask].reshape(N, -1)
#
#         labels = torch.zeros(N).to(positive_clusters.device).long()
#         logits = torch.cat((positive_clusters, negative_clusters), dim=1)
#         loss = self.criterion(logits, labels)
#         loss /= N
#
#         return loss + ne_loss