mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
import torch
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import torch.nn as nn
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from openselfsup.utils import print_log
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from . import builder
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from .registry import MODELS
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@MODELS.register_module
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class BYOL(nn.Module):
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'''BYOL unofficial implementation. Paper: https://arxiv.org/abs/2006.07733
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'''
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def __init__(self,
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backbone,
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neck=None,
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head=None,
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pretrained=None,
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base_momentum=0.996,
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**kwargs):
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super(BYOL, self).__init__()
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self.online_net = nn.Sequential(
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builder.build_backbone(backbone), builder.build_neck(neck))
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self.target_net = nn.Sequential(
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builder.build_backbone(backbone), builder.build_neck(neck))
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self.backbone = self.online_net[0]
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for param in self.target_net.parameters():
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param.requires_grad = False
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self.head = builder.build_head(head)
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self.init_weights(pretrained=pretrained)
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self.base_momentum = base_momentum
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self.momentum = base_momentum
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def init_weights(self, pretrained=None):
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if pretrained is not None:
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print_log('load model from: {}'.format(pretrained), logger='root')
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self.online_net[0].init_weights(pretrained=pretrained) # backbone
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self.online_net[1].init_weights(init_linear='kaiming') # projection
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for param_ol, param_tgt in zip(self.online_net.parameters(),
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self.target_net.parameters()):
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param_tgt.data.copy_(param_ol.data)
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# init the predictor in the head
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self.head.init_weights()
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@torch.no_grad()
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def _momentum_update(self):
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"""
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Momentum update of the target network.
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"""
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for param_ol, param_tgt in zip(self.online_net.parameters(),
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self.target_net.parameters()):
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param_tgt.data = param_tgt.data * self.momentum + \
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param_ol.data * (1. - self.momentum)
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def forward_train(self, img, **kwargs):
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assert img.dim() == 5, \
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"Input must have 5 dims, got: {}".format(img.dim())
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img_v1 = img[:, 0, ...].contiguous()
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img_v2 = img[:, 1, ...].contiguous()
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# compute query features
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proj_online_v1 = self.online_net(img_v1)[0]
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proj_online_v2 = self.online_net(img_v2)[0]
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with torch.no_grad():
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proj_target_v1 = self.target_net(img_v1)[0].clone().detach()
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proj_target_v2 = self.target_net(img_v2)[0].clone().detach()
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loss = self.head(proj_online_v1, proj_target_v2)['loss'] + \
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self.head(proj_online_v2, proj_target_v1)['loss']
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self._momentum_update()
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return dict(loss=loss)
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def forward_test(self, img, **kwargs):
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pass
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def forward(self, img, mode='train', **kwargs):
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if mode == 'train':
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return self.forward_train(img, **kwargs)
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elif mode == 'test':
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return self.forward_test(img, **kwargs)
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elif mode == 'extract':
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return self.backbone(img)
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else:
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raise Exception("No such mode: {}".format(mode))
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