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Source code for catalyst.contrib.losses.circle

from typing import Tuple

import torch
from torch import nn, Tensor


def _convert_label_to_similarity(
    normed_features: Tensor, labels: Tensor
) -> Tuple[Tensor, Tensor]:
    similarity_matrix = normed_features @ normed_features.transpose(1, 0)
    label_matrix = labels.unsqueeze(1) == labels.unsqueeze(0)

    positive_matrix = label_matrix.triu(diagonal=1)
    negative_matrix = label_matrix.logical_not().triu(diagonal=1)

    similarity_matrix = similarity_matrix.view(-1)
    positive_matrix = positive_matrix.view(-1)
    negative_matrix = negative_matrix.view(-1)
    sp, sn = (
        similarity_matrix[positive_matrix],
        similarity_matrix[negative_matrix],
    )
    return sp, sn


[docs]class CircleLoss(nn.Module): """ CircleLoss from `Circle Loss: A Unified Perspective of Pair Similarity Optimization`_ paper. Adapter from: https://github.com/TinyZeaMays/CircleLoss Example: >>> import torch >>> from torch.nn import functional as F >>> from catalyst.contrib.losses import CircleLoss >>> >>> features = F.normalize(torch.rand(256, 64, requires_grad=True)) >>> labels = torch.randint(high=10, size=(256)) >>> criterion = CircleLoss(margin=0.25, gamma=256) >>> criterion(features, labels) .. _`Circle Loss: A Unified Perspective of Pair Similarity Optimization`: https://arxiv.org/abs/2002.10857 """
[docs] def __init__(self, margin: float, gamma: float) -> None: """ Args: margin: margin to use gamma: gamma to use """ super().__init__() self.margin = margin self.gamma = gamma self.soft_plus = nn.Softplus()
def forward(self, normed_features: Tensor, labels: Tensor) -> Tensor: """ Args: normed_features: batch with samples features of shape [bs; feature_len] labels: batch with samples correct labels of shape [bs; ] Returns: torch.Tensor: circle loss """ sp, sn = _convert_label_to_similarity(normed_features, labels) ap = torch.clamp_min(-sp.detach() + 1 + self.margin, min=0.0) an = torch.clamp_min(sn.detach() + self.margin, min=0.0) delta_p = 1 - self.margin delta_n = self.margin logit_p = -ap * (sp - delta_p) * self.gamma logit_n = an * (sn - delta_n) * self.gamma loss = self.soft_plus( torch.logsumexp(logit_n, dim=0) + torch.logsumexp(logit_p, dim=0) ) return loss
__all__ = ["CircleLoss"]