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Source code for mmselfsup.models.algorithms.barlowtwins
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
[docs]@ALGORITHMS.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>`_.
Args:
backbone (dict): Config dict for module of backbone. Defaults to None.
neck (dict): Config dict for module of deep features to compact
feature vectors. Defaults to None.
head (dict): Config dict for module of loss functions.
Defaults to None.
init_cfg (dict): Config dict for weight initialization.
Defaults to None.
"""
def __init__(self,
backbone: dict = None,
neck: dict = None,
head: dict = None,
init_cfg: Optional[dict] = None,
**kwargs) -> None:
super(BarlowTwins, self).__init__(init_cfg)
assert backbone is not None
self.backbone = build_backbone(backbone)
assert neck is not None
self.neck = build_neck(neck)
assert head is not None
self.head = build_head(head)
[docs] def extract_feat(self, img: torch.Tensor) -> torch.Tensor:
"""Function to extract features from backbone.
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
Returns:
tuple[Tensor]: backbone outputs.
"""
x = self.backbone(img)
return x
[docs] def forward_train(self, img: List[torch.Tensor]) -> dict:
"""Forward computation during training.
Args:
img (List[Tensor]): A list of input images with shape
(N, C, H, W). Typically these should be mean centered
and std scaled.
Returns:
dict[str, Tensor]: A dictionary of loss components
"""
assert isinstance(img, list)
img_v1 = img[0]
img_v2 = img[1]
z1 = self.neck(self.backbone(img_v1))[0] # NxC
z2 = self.neck(self.backbone(img_v2))[0] # NxC
losses = self.head(z1, z2)['loss']
return dict(loss=losses)