<reponame>CodeGuy-007/lightly<gh_stars>1-10
""" Symmetrized Negative Cosine Similarity Loss Functions """

# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved

import torch
import warnings

class SymNegCosineSimilarityLoss(torch.nn.Module):
    """Implementation of the Symmetrized Loss used in the SimSiam[0] paper.

    [0] SimSiam, 2020, https://arxiv.org/abs/2011.10566
    
    Examples:

        >>> # initialize loss function
        >>> loss_fn = SymNegCosineSimilarityLoss()
        >>>
        >>> # generate two random transforms of images
        >>> t0 = transforms(images)
        >>> t1 = transforms(images)
        >>>
        >>> # feed through SimSiam model
        >>> out0, out1 = model(t0, t1)
        >>>
        >>> # calculate loss
        >>> loss = loss_fn(out0, out1)

    """

    def __init__(self) -> None:
        super().__init__()
        warnings.warn(
            Warning(
                "SymNegCosineSimiliarityLoss will be deprecated in favor of "
                + "NegativeCosineSimilarity in the future."
            ),
            PendingDeprecationWarning,
        )

    def _neg_cosine_simililarity(self, x, y):
        v = - torch.nn.functional.cosine_similarity(x, y.detach(), dim=-1).mean()
        return v

    def forward(self, 
                out0: torch.Tensor, 
                out1: torch.Tensor):
        """Forward pass through Symmetric Loss.

            Args:
                out0:
                    Output projections of the first set of transformed images.
                    Expects the tuple to be of the form (z0, p0), where z0 is
                    the output of the backbone and projection mlp, and p0 is the
                    output of the prediction head.
                out1:
                    Output projections of the second set of transformed images.
                    Expects the tuple to be of the form (z1, p1), where z1 is
                    the output of the backbone and projection mlp, and p1 is the
                    output of the prediction head.
 
            Returns:
                Contrastive Cross Entropy Loss value.

            Raises:
                ValueError if shape of output is not multiple of batch_size.
        """
        z0, p0 = out0
        z1, p1 = out1

        loss = self._neg_cosine_simililarity(p0, z1) / 2 + \
               self._neg_cosine_simililarity(p1, z0) / 2

        return loss
    
""" Symmetrized Negative Cosine Similarity Loss Functions """

# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved

import torch
import warnings

class SymNegCosineSimilarityLoss(torch.nn.Module):
    """Implementation of the Symmetrized Loss used in the SimSiam[0] paper.

    [0] SimSiam, 2020, https://arxiv.org/abs/2011.10566
    
    Examples:

        >>> # initialize loss function
        >>> loss_fn = SymNegCosineSimilarityLoss()
        >>>
        >>> # generate two random transforms of images
        >>> t0 = transforms(images)
        >>> t1 = transforms(images)
        >>>
        >>> # feed through SimSiam model
        >>> out0, out1 = model(t0, t1)
        >>>
        >>> # calculate loss
        >>> loss = loss_fn(out0, out1)

    """

    def __init__(self) -> None:
        super().__init__()
        warnings.warn(
            Warning(
                "SymNegCosineSimiliarityLoss will be deprecated in favor of "
                + "NegativeCosineSimilarity in the future."
            ),
            PendingDeprecationWarning,
        )

    def _neg_cosine_simililarity(self, x, y):
        v = - torch.nn.functional.cosine_similarity(x, y.detach(), dim=-1).mean()
        return v

    def forward(self, 
                out0: torch.Tensor, 
                out1: torch.Tensor):
        """Forward pass through Symmetric Loss.

            Args:
                out0:
                    Output projections of the first set of transformed images.
                    Expects the tuple to be of the form (z0, p0), where z0 is
                    the output of the backbone and projection mlp, and p0 is the
                    output of the prediction head.
                out1:
                    Output projections of the second set of transformed images.
                    Expects the tuple to be of the form (z1, p1), where z1 is
                    the output of the backbone and projection mlp, and p1 is the
                    output of the prediction head.
 
            Returns:
                Contrastive Cross Entropy Loss value.

            Raises:
                ValueError if shape of output is not multiple of batch_size.
        """
        z0, p0 = out0
        z1, p1 = out1

        loss = self._neg_cosine_simililarity(p0, z1) / 2 + \
               self._neg_cosine_simililarity(p1, z0) / 2

        return loss
Symmetrized Negative Cosine Similarity Loss Functions # Copyright (c) 2020. Lightly AG and its affiliates. # All Rights Reserved Implementation of the Symmetrized Loss used in the SimSiam[0] paper.      [0] SimSiam, 2020, https://arxiv.org/abs/2011.10566          Examples:          >>> # initialize loss function         >>> loss_fn = SymNegCosineSimilarityLoss()         >>>         >>> # generate two random transforms of images         >>> t0 = transforms(images)         >>> t1 = transforms(images)         >>>         >>> # feed through SimSiam model         >>> out0, out1 = model(t0, t1)         >>>         >>> # calculate loss         >>> loss = loss_fn(out0, out1) Forward pass through Symmetric Loss.              Args:                 out0:                     Output projections of the first set of transformed images.                     Expects the tuple to be of the form (z0, p0), where z0 is                     the output of the backbone and projection mlp, and p0 is the                     output of the prediction head.                 out1:                     Output projections of the second set of transformed images.                     Expects the tuple to be of the form (z1, p1), where z1 is                     the output of the backbone and projection mlp, and p1 is the                     output of the prediction head.               Returns:                 Contrastive Cross Entropy Loss value.              Raises:                 ValueError if shape of output is not multiple of batch_size.