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import torch
import annotator.uniformer.mmcv as mmcv
class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
"""A general BatchNorm layer without input dimension check.
Reproduced from @kapily's work:
(https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547)
The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
is `_check_input_dim` that is designed for tensor sanity checks.
The check has been bypassed in this class for the convenience of converting
SyncBatchNorm.
"""
def _check_input_dim(self, input):
return
def revert_sync_batchnorm(module):
"""Helper function to convert all `SyncBatchNorm` (SyncBN) and
`mmcv.ops.sync_bn.SyncBatchNorm`(MMSyncBN) layers in the model to
`BatchNormXd` layers.
Adapted from @kapily's work:
(https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547)
Args:
module (nn.Module): The module containing `SyncBatchNorm` layers.
Returns:
module_output: The converted module with `BatchNormXd` layers.
"""
module_output = module
module_checklist = [torch.nn.modules.batchnorm.SyncBatchNorm]
if hasattr(mmcv, 'ops'):
module_checklist.append(mmcv.ops.SyncBatchNorm)
if isinstance(module, tuple(module_checklist)):
module_output = _BatchNormXd(module.num_features, module.eps,
module.momentum, module.affine,
module.track_running_stats)
if module.affine:
# no_grad() may not be needed here but
# just to be consistent with `convert_sync_batchnorm()`
with torch.no_grad():
module_output.weight = module.weight
module_output.bias = module.bias
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
module_output.training = module.training
# qconfig exists in quantized models
if hasattr(module, 'qconfig'):
module_output.qconfig = module.qconfig
for name, child in module.named_children():
module_output.add_module(name, revert_sync_batchnorm(child))
del module
return module_output
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