mmselfsup.models.algorithms.eva 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
import torch
from mmselfsup.registry import MODELS
from mmselfsup.structures import SelfSupDataSample
from .base import BaseModel
[文档]@MODELS.register_module()
class EVA(BaseModel):
"""EVA.
Implementation of `EVA: Exploring the Limits of Masked Visual
Representation Learning at Scale <https://arxiv.org/abs/2211.07636>`_.
"""
[文档] def loss(self, inputs: List[torch.Tensor],
data_samples: List[SelfSupDataSample],
**kwargs) -> Dict[str, torch.Tensor]:
"""The forward function in training.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[SelfSupDataSample]): All elements required
during the forward function.
Returns:
Dict[str, torch.Tensor]: A dictionary of loss components.
"""
clip_feature, _ = self.target_generator(inputs[0])
latent, mask, ids_restore = self.backbone(inputs[0])
pred = self.neck(latent, ids_restore)
clip_feature = clip_feature[:, 1:, :]
loss = self.head(pred, clip_feature, mask)
losses = dict(loss=loss)
return losses