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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from ..utils import CosineEMA
from .base import BaseSelfSupervisor
@MODELS.register_module()
class BYOL(BaseSelfSupervisor):
"""BYOL.
Implementation of `Bootstrap Your Own Latent: A New Approach to
Self-Supervised Learning <https://arxiv.org/abs/2006.07733>`_.
Args:
backbone (dict): Config dict for module of backbone.
neck (dict): Config dict for module of deep features
to compact feature vectors.
head (dict): Config dict for module of head functions.
base_momentum (float): The base momentum coefficient for the target
network. Defaults to 0.004.
pretrained (str, optional): The pretrained checkpoint path, support
local path and remote path. Defaults to None.
data_preprocessor (dict, optional): The config for preprocessing
input data. If None or no specified type, it will use
"SelfSupDataPreprocessor" as type.
See :class:`SelfSupDataPreprocessor` for more details.
Defaults to None.
init_cfg (Union[List[dict], dict], optional): Config dict for weight
initialization. Defaults to None.
"""
def __init__(self,
backbone: dict,
neck: dict,
head: dict,
base_momentum: float = 0.004,
pretrained: Optional[str] = None,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
backbone=backbone,
neck=neck,
head=head,
pretrained=pretrained,
data_preprocessor=data_preprocessor,
init_cfg=init_cfg)
# create momentum model
self.target_net = CosineEMA(
nn.Sequential(self.backbone, self.neck), momentum=base_momentum)
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]
# compute online features
proj_online_v1 = self.neck(self.backbone(img_v1))[0]
proj_online_v2 = self.neck(self.backbone(img_v2))[0]
# compute target features
with torch.no_grad():
# update the target net
self.target_net.update_parameters(
nn.Sequential(self.backbone, self.neck))
proj_target_v1 = self.target_net(img_v1)[0]
proj_target_v2 = self.target_net(img_v2)[0]
loss_1 = self.head.loss(proj_online_v1, proj_target_v2)
loss_2 = self.head.loss(proj_online_v2, proj_target_v1)
losses = dict(loss=2. * (loss_1 + loss_2))
return losses
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