Source code for mmselfsup.models.algorithms.barlowtwins
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Tuple
import torch
from mmselfsup.registry import MODELS
from mmselfsup.structures import SelfSupDataSample
from .base import BaseModel
[docs]@MODELS.register_module()
class BarlowTwins(BaseModel):
"""BarlowTwins.
Implementation of `Barlow Twins: Self-Supervised Learning via Redundancy
Reduction <https://arxiv.org/abs/2103.03230>`_.
Part of the code is borrowed from:
`<https://github.com/facebookresearch/barlowtwins/blob/main/main.py>`_.
"""
[docs] def extract_feat(self, inputs: List[torch.Tensor],
**kwargs) -> Tuple[torch.Tensor]:
"""Function to extract features from backbone.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[SelfSupDataSample]): All elements required
during the forward function.
Returns:
Tuple[torch.Tensor]: Backbone outputs.
"""
x = self.backbone(inputs[0])
return x
[docs] 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.
"""
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 = self.head(z1, z2)
losses = dict(loss=loss)
return losses