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mmpretrain.models.selfsup.eva 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 EVA(BaseSelfSupervisor): """EVA. Implementation of `EVA: Exploring the Limits of Masked Visual Representation Learning at Scale <https://arxiv.org/abs/2211.07636>`_. """ def extract_feat(self, inputs: torch.Tensor): return self.backbone(inputs, mask=None)
[文档] def loss(self, inputs: torch.Tensor, data_samples: List[DataSample], **kwargs) -> Dict[str, torch.Tensor]: """The forward function in training. Args: inputs (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. """ clip_feature, _ = self.target_generator(inputs) latent, mask, ids_restore = self.backbone(inputs) pred = self.neck(latent, ids_restore) clip_feature = clip_feature[:, 1:, :] loss = self.head.loss(pred, clip_feature, mask) losses = dict(loss=loss) return losses
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