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Source code for mmselfsup.models.memories.simple_memory
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import BaseModule, get_dist_info
from mmselfsup.utils import AliasMethod
from ..builder import MEMORIES
[docs]@MEMORIES.register_module()
class SimpleMemory(BaseModule):
"""Simple memory bank (e.g., for NPID).
This module includes the memory bank that stores running average
features of all samples in the dataset.
Args:
length (int): Number of features stored in the memory bank.
feat_dim (int): Dimension of stored features.
momentum (float): Momentum coefficient for updating features.
"""
def __init__(self, length, feat_dim, momentum, **kwargs):
super(SimpleMemory, self).__init__()
self.rank, self.num_replicas = get_dist_info()
self.feature_bank = torch.randn(length, feat_dim).cuda()
self.feature_bank = nn.functional.normalize(self.feature_bank).cuda()
self.momentum = momentum
self.multinomial = AliasMethod(torch.ones(length))
self.multinomial.cuda()
[docs] def update(self, ind, feature):
"""Update features in memory bank.
Args:
ind (Tensor): Indices for the batch of features.
feature (Tensor): Batch of features.
"""
feature_norm = nn.functional.normalize(feature)
ind, feature_norm = self._gather(ind, feature_norm)
feature_old = self.feature_bank[ind, ...]
feature_new = (1 - self.momentum) * feature_old + \
self.momentum * feature_norm
feature_new_norm = nn.functional.normalize(feature_new)
self.feature_bank[ind, ...] = feature_new_norm
def _gather(self, ind, feature):
"""Gather indices and features.
Args:
ind (Tensor): Indices for the batch of features.
feature (Tensor): Batch of features.
Returns:
Tensor: Gathered indices.
Tensor: Gathered features.
"""
ind_gathered = [
torch.ones_like(ind).cuda() for _ in range(self.num_replicas)
]
feature_gathered = [
torch.ones_like(feature).cuda() for _ in range(self.num_replicas)
]
dist.all_gather(ind_gathered, ind)
dist.all_gather(feature_gathered, feature)
ind_gathered = torch.cat(ind_gathered, dim=0)
feature_gathered = torch.cat(feature_gathered, dim=0)
return ind_gathered, feature_gathered