Note
You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.
Source code for mmselfsup.models.algorithms.mocov3
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
[docs]@ALGORITHMS.register_module()
class MoCoV3(BaseModel):
"""MoCo v3.
Implementation of `An Empirical Study of Training Self-Supervised Vision
Transformers <https://arxiv.org/abs/2104.02057>`_.
Args:
backbone (dict): Config dict for module of backbone.
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.
base_momentum (float): Momentum coefficient for the momentum-updated
encoder. Defaults to 0.99.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None
"""
def __init__(self,
backbone,
neck,
head,
base_momentum=0.99,
init_cfg=None,
**kwargs):
super(MoCoV3, self).__init__(init_cfg)
assert neck is not None
self.base_encoder = nn.Sequential(
build_backbone(backbone), build_neck(neck))
self.momentum_encoder = nn.Sequential(
build_backbone(backbone), build_neck(neck))
self.backbone = self.base_encoder[0]
self.neck = self.base_encoder[1]
assert head is not None
self.head = build_head(head)
self.base_momentum = base_momentum
self.momentum = base_momentum
[docs] def init_weights(self):
"""Initialize base_encoder with init_cfg defined in backbone."""
super(MoCoV3, self).init_weights()
for param_b, param_m in zip(self.base_encoder.parameters(),
self.momentum_encoder.parameters()):
param_m.data.copy_(param_b.data)
param_m.requires_grad = False
[docs] @torch.no_grad()
def momentum_update(self):
"""Momentum update of the momentum encoder."""
for param_b, param_m in zip(self.base_encoder.parameters(),
self.momentum_encoder.parameters()):
param_m.data = param_m.data * self.momentum + param_b.data * (
1. - self.momentum)
[docs] def extract_feat(self, img):
"""Function to extract features from backbone.
Args:
img (Tensor): Input images. 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, **kwargs):
"""Forward computation during training.
Args:
img (list[Tensor]): A list of input images. Typically these should
be mean centered and std scaled.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert isinstance(img, list)
view_1 = img[0].cuda(non_blocking=True)
view_2 = img[1].cuda(non_blocking=True)
# compute query features, [N, C] each
q1 = self.base_encoder(view_1)[0]
q2 = self.base_encoder(view_2)[0]
# compute key features, [N, C] each, no gradient
with torch.no_grad():
# here we use hook to update momentum encoder, which is a little
# bit different with the official version but it has negligible
# influence on the results
k1 = self.momentum_encoder(view_1)[0]
k2 = self.momentum_encoder(view_2)[0]
losses = self.head(q1, k2)['loss'] + self.head(q2, k1)['loss']
return dict(loss=losses)