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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmengine.dist import all_gather, get_rank
from mmengine.model import BaseModule
from mmpretrain.registry import MODELS
@MODELS.register_module()
class MoCoV3Head(BaseModule):
"""Head for MoCo v3 Pre-training.
This head builds a predictor, which can be any registered neck component.
It also implements latent contrastive loss between two forward features.
Part of the code is modified from:
`<https://github.com/facebookresearch/moco-v3/blob/main/moco/builder.py>`_.
Args:
predictor (dict): Config dict for module of predictor.
loss (dict): Config dict for module of loss functions.
temperature (float): The temperature hyper-parameter that
controls the concentration level of the distribution.
Defaults to 1.0.
"""
def __init__(self,
predictor: dict,
loss: dict,
temperature: float = 1.0) -> None:
super().__init__()
self.predictor = MODELS.build(predictor)
self.loss_module = MODELS.build(loss)
self.temperature = temperature
def loss(self, base_out: torch.Tensor,
momentum_out: torch.Tensor) -> torch.Tensor:
"""Generate loss.
Args:
base_out (torch.Tensor): NxC features from base_encoder.
momentum_out (torch.Tensor): NxC features from momentum_encoder.
Returns:
torch.Tensor: The loss tensor.
"""
# predictor computation
pred = self.predictor([base_out])[0]
# normalize
pred = nn.functional.normalize(pred, dim=1)
target = nn.functional.normalize(momentum_out, dim=1)
# get negative samples
target = torch.cat(all_gather(target), dim=0)
# Einstein sum is more intuitive
logits = torch.einsum('nc,mc->nm', [pred, target]) / self.temperature
# generate labels
batch_size = logits.shape[0]
labels = (torch.arange(batch_size, dtype=torch.long) +
batch_size * get_rank()).to(logits.device)
loss = self.loss_module(logits, labels)
return loss
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