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mmselfsup.models.heads.latent_pred_head 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule, get_dist_info

from ..builder import HEADS, build_neck


@HEADS.register_module()
class LatentPredictHead(BaseModule):
    """Head for latent feature prediction.

    This head builds a predictor, which can be any registered neck component.
    For example, BYOL and SimSiam call this head and build NonLinearNeck.
    It also implements similarity loss between two forward features.

    Args:
        predictor (dict): Config dict for the predictor.
    """

    def __init__(self, predictor: dict) -> None:
        super(LatentPredictHead, self).__init__()
        self.predictor = build_neck(predictor)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> dict:
        """Forward head.

        Args:
            input (Tensor): NxC input features.
            target (Tensor): NxC target features.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        pred = self.predictor([input])[0]
        target = target.detach()

        pred_norm = nn.functional.normalize(pred, dim=1)
        target_norm = nn.functional.normalize(target, dim=1)
        loss = -(pred_norm * target_norm).sum(dim=1).mean()
        return dict(loss=loss)


@HEADS.register_module
class LatentClsHead(BaseModule):
    """Head for latent feature classification.

    Args:
        in_channels (int): Number of input channels.
        num_classes (int): Number of classes.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(
        self,
        in_channels: int,
        num_classes: int,
        init_cfg: dict = dict(
            type='Normal',
            std=0.01,
            layer='Linear',
        )
    ) -> None:
        super(LatentClsHead, self).__init__(init_cfg)
        self.predictor = nn.Linear(in_channels, num_classes)
        self.criterion = nn.CrossEntropyLoss()

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> dict:
        """Forward head.

        Args:
            input (Tensor): NxC input features.
            target (Tensor): NxC target features.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        pred = self.predictor(input)
        with torch.no_grad():
            label = torch.argmax(self.predictor(target), dim=1).detach()
        loss = self.criterion(pred, label)
        return dict(loss=loss)


@HEADS.register_module()
class LatentCrossCorrelationHead(BaseModule):
    """Head for latent feature cross correlation. Part of the code is borrowed
    from:
    `https://github.com/facebookresearch/barlowtwins/blob/main/main.py>`_.

    Args:
        in_channels (int): Number of input channels.
        lambd (float): Weight on off-diagonal terms. Defaults to 0.0051.
    """

    def __init__(self, in_channels: int, lambd: float = 0.0051) -> None:
        super(LatentCrossCorrelationHead, self).__init__()
        self.lambd = lambd
        _, self.world_size = get_dist_info()
        self.bn = nn.BatchNorm1d(in_channels, affine=False)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> dict:
        """Forward head.

        Args:
            input (Tensor): NxC input features.
            target (Tensor): NxC target features.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        # cross-correlation matrix
        cross_correlation_matrix = self.bn(input).T @ self.bn(target)
        cross_correlation_matrix.div_(input.size(0) * self.world_size)

        if torch.distributed.is_initialized():
            torch.distributed.all_reduce(cross_correlation_matrix)

        # loss
        on_diag = torch.diagonal(cross_correlation_matrix).add_(-1).pow_(
            2).sum()
        off_diag = self.off_diagonal(cross_correlation_matrix).pow_(2).sum()
        loss = on_diag + self.lambd * off_diag
        return dict(loss=loss)

    def off_diagonal(self, x: torch.Tensor) -> torch.Tensor:
        """Rreturn a flattened view of the off-diagonal elements of a square
        matrix."""
        n, m = x.shape
        assert n == m
        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()