| """ Split BatchNorm |
| |
| A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through |
| a separate BN layer. The first split is passed through the parent BN layers with weight/bias |
| keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn' |
| namespace. |
| |
| This allows easily removing the auxiliary BN layers after training to efficiently |
| achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2, |
| 'Disentangled Learning via An Auxiliary BN' |
| |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
| import torch |
| import torch.nn as nn |
|
|
|
|
| class SplitBatchNorm2d(torch.nn.BatchNorm2d): |
|
|
| def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, |
| track_running_stats=True, num_splits=2): |
| super().__init__(num_features, eps, momentum, affine, track_running_stats) |
| assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)' |
| self.num_splits = num_splits |
| self.aux_bn = nn.ModuleList([ |
| nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)]) |
|
|
| def forward(self, input: torch.Tensor): |
| if self.training: |
| split_size = input.shape[0] // self.num_splits |
| assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits" |
| split_input = input.split(split_size) |
| x = [super().forward(split_input[0])] |
| for i, a in enumerate(self.aux_bn): |
| x.append(a(split_input[i + 1])) |
| return torch.cat(x, dim=0) |
| else: |
| return super().forward(input) |
|
|
|
|
| def convert_splitbn_model(module, num_splits=2): |
| """ |
| Recursively traverse module and its children to replace all instances of |
| ``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`. |
| Args: |
| module (torch.nn.Module): input module |
| num_splits: number of separate batchnorm layers to split input across |
| Example:: |
| >>> # model is an instance of torch.nn.Module |
| >>> model = timm.models.convert_splitbn_model(model, num_splits=2) |
| """ |
| mod = module |
| if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm): |
| return module |
| if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): |
| mod = SplitBatchNorm2d( |
| module.num_features, module.eps, module.momentum, module.affine, |
| module.track_running_stats, num_splits=num_splits) |
| mod.running_mean = module.running_mean |
| mod.running_var = module.running_var |
| mod.num_batches_tracked = module.num_batches_tracked |
| if module.affine: |
| mod.weight.data = module.weight.data.clone().detach() |
| mod.bias.data = module.bias.data.clone().detach() |
| for aux in mod.aux_bn: |
| aux.running_mean = module.running_mean.clone() |
| aux.running_var = module.running_var.clone() |
| aux.num_batches_tracked = module.num_batches_tracked.clone() |
| if module.affine: |
| aux.weight.data = module.weight.data.clone().detach() |
| aux.bias.data = module.bias.data.clone().detach() |
| for name, child in module.named_children(): |
| mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits)) |
| del module |
| return mod |
|
|
