

import torch
from torch import nn


class CenterLoss(nn.Module):
    """Center loss.

    Reference:
    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.

    Args:
        num_classes (int): number of classes.
        feat_dim (int): feature dimension.
    """

    def __init__(self, num_classes=751, feat_dim=2048):
        super().__init__()
        self.num_classes = num_classes
        self.feat_dim = feat_dim
        self.centers = nn.Parameter(
            torch.randn(self.num_classes, self.feat_dim))

    def forward(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        assert x.size(0) == labels.size(
            0), "features.size(0) is not equal to labels.size(0)"

        batch_size = x.size(0)
        # (batch_size, num_classes)
        x_2 = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(
            batch_size, self.num_classes)
        # (batch_size, num_classes)
        center_2 = torch.pow(self.centers, 2).sum(
            dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
        distmat = x_2 + center_2

        # distmat - 2 * x \times self.centers.t()
        distmat.addmm_(1, -2, x, self.centers.t())

        classes = torch.arange(self.num_classes).long().to(x.device)
        labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
        mask = labels.eq(classes.expand(batch_size, self.num_classes))

        dist = distmat * mask.float()
        loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size
        return loss


if __name__ == '__main__':
    use_gpu = False
    center_loss = CenterLoss()
    features = torch.rand(16, 2048)
    targets = torch.Tensor(
        [0, 1, 2, 3, 2, 3, 1, 4, 5, 3, 2, 1, 0, 0, 5, 4]).long()
    if use_gpu:
        features = torch.rand(16, 2048).cuda()
        targets = torch.Tensor(
            [0, 1, 2, 3, 2, 3, 1, 4, 5, 3, 2, 1, 0, 0, 5, 4]).cuda()

    loss = center_loss(features, targets)
    print(loss)
