Source code for mmaction.models.common.sub_batchnorm3d

# Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy

import torch
import torch.nn as nn

from mmaction.registry import MODELS

[docs]@MODELS.register_module() class SubBatchNorm3D(nn.Module): """Sub BatchNorm3d splits the batch dimension into N splits, and run BN on each of them separately (so that the stats are computed on each subset of examples (1/N of batch) independently). During evaluation, it aggregates the stats from all splits into one BN. Args: num_features (int): Dimensions of BatchNorm. """ def __init__(self, num_features, **cfg): super(SubBatchNorm3D, self).__init__() self.num_features = num_features self.cfg_ = deepcopy(cfg) self.num_splits = self.cfg_.pop('num_splits', 1) self.num_features_split = self.num_features * self.num_splits # only keep one set of affine params, not in .bn or .split_bn self.cfg_['affine'] = False = nn.BatchNorm3d(num_features, **self.cfg_) self.split_bn = nn.BatchNorm3d(self.num_features_split, **self.cfg_) self.init_weights(cfg)
[docs] def init_weights(self, cfg): """Initialize weights.""" if cfg.get('affine', True): self.weight = torch.nn.Parameter(torch.ones(self.num_features)) self.bias = torch.nn.Parameter(torch.zeros(self.num_features)) self.affine = True else: self.affine = False
def _get_aggregated_mean_std(self, means, stds, n): """Calculate aggregated mean and std.""" mean = means.view(n, -1).sum(0) / n std = stds.view(n, -1).sum(0) / n + ( (means.view(n, -1) - mean)**2).view(n, -1).sum(0) / n return mean.detach(), std.detach()
[docs] def aggregate_stats(self): """Synchronize running_mean, and running_var to Call this before eval, then call model.eval(); When eval, forward function will call instead of self.split_bn, During this time the running_mean, and running_var of has been obtained from self.split_bn. """ if self.split_bn.track_running_stats: aggre_func = self._get_aggregated_mean_std, = aggre_func( self.split_bn.running_mean, self.split_bn.running_var, self.num_splits) = self.split_bn.num_batches_tracked.detach( )
[docs] def forward(self, x): """Defines the computation performed at every call.""" if n, c, t, h, w = x.shape assert n % self.num_splits == 0 x = x.view(n // self.num_splits, c * self.num_splits, t, h, w) x = self.split_bn(x) x = x.view(n, c, t, h, w) else: x = if self.affine: x = x * self.weight.view(-1, 1, 1, 1) x = x + self.bias.view(-1, 1, 1, 1) return x
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