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Unverified Commit 7cfc839e authored by Jerry Jiarui XU's avatar Jerry Jiarui XU Committed by GitHub
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Add MMSyncBN in registry (#420) 

* Add MMSyncBN in registery

* skip mmsyncbn test
parent 50f69e70
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Showing with 89 additions and 96 deletions
mmcv/ops/sync_bn.py
View file @ 7cfc839e
......@@ -6,6 +6,7 @@ from torch.autograd.function import once_differentiable
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
from mmcv.cnn import NORM_LAYERS
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', [
......@@ -109,99 +110,89 @@ class SyncBatchNormFunction(Function):
None, None, None, None
if dist.is_available():
class SyncBatchNorm(Module):
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
group=dist.group.WORLD):
super(SyncBatchNorm, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track_running_stats = track_running_stats
self.group = group
self.group_size = dist.get_world_size(group)
if self.affine:
self.weight = Parameter(torch.Tensor(num_features))
self.bias = Parameter(torch.Tensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.register_buffer('num_batches_tracked',
torch.tensor(0, dtype=torch.long))
else:
self.register_buffer('running_mean', None)
self.register_buffer('running_var', None)
self.register_buffer('num_batches_tracked', None)
self.reset_parameters()
def reset_running_stats(self):
if self.track_running_stats:
self.running_mean.zero_()
self.running_var.fill_(1)
self.num_batches_tracked.zero_()
def reset_parameters(self):
self.reset_running_stats()
if self.affine:
self.weight.data.uniform_() # pytorch use ones_()
self.bias.data.zero_()
def forward(self, input):
if input.dim() < 2:
raise ValueError(
f'expected at least 2D input, got {input.dim()}D input')
if self.momentum is None:
exponential_average_factor = 0.0
else:
exponential_average_factor = self.momentum
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(
self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training or not self.track_running_stats:
return SyncBatchNormFunction.apply(input, self.running_mean,
self.running_var,
self.weight, self.bias,
exponential_average_factor,
self.eps, self.group,
self.group_size)
else:
return F.batch_norm(input, self.running_mean, self.running_var,
self.weight, self.bias, False,
exponential_average_factor, self.eps)
def __repr__(self):
s = self.__class__.__name__
s += f'({self.num_features}, '
s += f'eps={self.eps}, '
s += f'momentum={self.momentum}, '
s += f'affine={self.affine}, '
s += f'track_running_stats={self.track_running_stats}, '
s += f'group_size={self.group_size})'
return s
else:
class SyncBatchNorm(Module):
def __init__(self, *args, **kwargs):
raise NotImplementedError(
'SyncBatchNorm is not supported in this OS since the '
'distributed package is not available')
@NORM_LAYERS.register_module(name='MMSyncBN')
class SyncBatchNorm(Module):
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
group=None):
super(SyncBatchNorm, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track_running_stats = track_running_stats
self.group = dist.group.WORLD if group is None else group
self.group_size = dist.get_world_size(group)
if self.affine:
self.weight = Parameter(torch.Tensor(num_features))
self.bias = Parameter(torch.Tensor(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.register_buffer('num_batches_tracked',
torch.tensor(0, dtype=torch.long))
else:
self.register_buffer('running_mean', None)
self.register_buffer('running_var', None)
self.register_buffer('num_batches_tracked', None)
self.reset_parameters()
def reset_running_stats(self):
if self.track_running_stats:
self.running_mean.zero_()
self.running_var.fill_(1)
self.num_batches_tracked.zero_()
def reset_parameters(self):
self.reset_running_stats()
if self.affine:
self.weight.data.uniform_() # pytorch use ones_()
self.bias.data.zero_()
def forward(self, input):
if input.dim() < 2:
raise ValueError(
f'expected at least 2D input, got {input.dim()}D input')
if self.momentum is None:
exponential_average_factor = 0.0
else:
exponential_average_factor = self.momentum
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(
self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training or not self.track_running_stats:
return SyncBatchNormFunction.apply(input, self.running_mean,
self.running_var, self.weight,
self.bias,
exponential_average_factor,
self.eps, self.group,
self.group_size)
else:
return F.batch_norm(input, self.running_mean, self.running_var,
self.weight, self.bias, False,
exponential_average_factor, self.eps)
def __repr__(self):
s = self.__class__.__name__
s += f'({self.num_features}, '
s += f'eps={self.eps}, '
s += f'momentum={self.momentum}, '
s += f'affine={self.affine}, '
s += f'track_running_stats={self.track_running_stats}, '
s += f'group_size={self.group_size})'
return s
tests/test_cnn/test_build_layers.py
View file @ 7cfc839e
......@@ -135,6 +135,8 @@ def test_build_norm_layer():
'IN3d': 'in',
}
for type_name, module in NORM_LAYERS.module_dict.items():
if type_name == 'MMSyncBN': # skip MMSyncBN
continue
for postfix in ['_test', 1]:
cfg = dict(type=type_name)
if type_name == 'GN':
......
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