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76 lines
3.3 KiB
76 lines
3.3 KiB
5 years ago
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""" Split BatchNorm
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A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
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a separate BN layer. The first split is passed through the parent BN layers with weight/bias
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keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
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namespace.
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This allows easily removing the auxiliary BN layers after training to efficiently
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achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2,
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'Disentangled Learning via An Auxiliary BN'
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Hacked together by Ross Wightman
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"""
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5 years ago
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import torch
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import torch.nn as nn
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class SplitBatchNorm2d(torch.nn.BatchNorm2d):
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def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
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track_running_stats=True, num_splits=2):
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super().__init__(num_features, eps, momentum, affine, track_running_stats)
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assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)'
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self.num_splits = num_splits
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self.aux_bn = nn.ModuleList([
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nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)])
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def forward(self, input: torch.Tensor):
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if self.training: # aux BN only relevant while training
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split_size = input.shape[0] // self.num_splits
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assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits"
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split_input = input.split(split_size)
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x = [super().forward(split_input[0])]
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for i, a in enumerate(self.aux_bn):
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x.append(a(split_input[i + 1]))
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return torch.cat(x, dim=0)
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else:
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return super().forward(input)
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def convert_splitbn_model(module, num_splits=2):
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"""
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Recursively traverse module and its children to replace all instances of
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``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.
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Args:
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module (torch.nn.Module): input module
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num_splits: number of separate batchnorm layers to split input across
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Example::
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>>> # model is an instance of torch.nn.Module
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>>> model = timm.models.convert_splitbn_model(model, num_splits=2)
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"""
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mod = module
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if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm):
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return module
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if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
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mod = SplitBatchNorm2d(
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module.num_features, module.eps, module.momentum, module.affine,
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module.track_running_stats, num_splits=num_splits)
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mod.running_mean = module.running_mean
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mod.running_var = module.running_var
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mod.num_batches_tracked = module.num_batches_tracked
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if module.affine:
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mod.weight.data = module.weight.data.clone().detach()
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mod.bias.data = module.bias.data.clone().detach()
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for aux in mod.aux_bn:
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aux.running_mean = module.running_mean.clone()
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aux.running_var = module.running_var.clone()
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aux.num_batches_tracked = module.num_batches_tracked.clone()
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if module.affine:
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aux.weight.data = module.weight.data.clone().detach()
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aux.bias.data = module.bias.data.clone().detach()
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for name, child in module.named_children():
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mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits))
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del module
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return mod
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