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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 / Copyright 2020 Ross Wightman |
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""" |
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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: |
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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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