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mmpretrain.models.selfsup.simsiam 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List

import torch

from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .base import BaseSelfSupervisor


[文档]@MODELS.register_module() class SimSiam(BaseSelfSupervisor): """SimSiam. Implementation of `Exploring Simple Siamese Representation Learning <https://arxiv.org/abs/2011.10566>`_. The operation of fixing learning rate of predictor is in `engine/hooks/simsiam_hook.py`. """
[文档] def loss(self, inputs: List[torch.Tensor], data_samples: List[DataSample], **kwargs) -> Dict[str, torch.Tensor]: """The forward function in training. Args: inputs (List[torch.Tensor]): The input images. data_samples (List[DataSample]): All elements required during the forward function. Returns: Dict[str, torch.Tensor]: A dictionary of loss components. """ assert isinstance(inputs, list) img_v1 = inputs[0] img_v2 = inputs[1] z1 = self.neck(self.backbone(img_v1))[0] # NxC z2 = self.neck(self.backbone(img_v2))[0] # NxC loss_1 = self.head.loss(z1, z2) loss_2 = self.head.loss(z2, z1) losses = dict(loss=0.5 * (loss_1 + loss_2)) return losses
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