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mmpretrain.models.heads.latent_heads 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from mmengine.dist import all_reduce, get_world_size
from mmengine.model import BaseModule

from mmpretrain.registry import MODELS


[文档]@MODELS.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: loss (dict): Config dict for the loss. predictor (dict): Config dict for the predictor. init_cfg (dict or List[dict], optional): Initialization config dict. Defaults to None. """ def __init__(self, loss: dict, predictor: dict, init_cfg: Optional[Union[dict, List[dict]]] = None) -> None: super().__init__(init_cfg=init_cfg) self.loss_module = MODELS.build(loss) self.predictor = MODELS.build(predictor)
[文档] def loss(self, input: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """Forward head. Args: input (torch.Tensor): NxC input features. target (torch.Tensor): NxC target features. Returns: torch.Tensor: The latent predict loss. """ pred = self.predictor([input])[0] target = target.detach() loss = self.loss_module(pred, target) return loss
[文档]@MODELS.register_module() class LatentCrossCorrelationHead(BaseModule): """Head for latent feature cross correlation. Part of the code is borrowed from `script <https://github.com/facebookresearch/barlowtwins/blob/main/main.py>`_. Args: in_channels (int): Number of input channels. loss (dict): Config dict for module of loss functions. init_cfg (dict or List[dict], optional): Initialization config dict. Defaults to None. """ def __init__(self, in_channels: int, loss: dict, init_cfg: Optional[Union[dict, List[dict]]] = None) -> None: super().__init__(init_cfg=init_cfg) self.world_size = get_world_size() self.bn = nn.BatchNorm1d(in_channels, affine=False) self.loss_module = MODELS.build(loss)
[文档] def loss(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """Forward head. Args: input (torch.Tensor): NxC input features. target (torch.Tensor): NxC target features. Returns: torch.Tensor: The cross correlation loss. """ # cross-correlation matrix cross_correlation_matrix = self.bn(input).T @ self.bn(target) cross_correlation_matrix.div_(input.size(0) * self.world_size) all_reduce(cross_correlation_matrix) loss = self.loss_module(cross_correlation_matrix) return loss
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