# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
from annotator.uniformer.mmcv.cnn import NORM_LAYERS
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', [
'sync_bn_forward_mean', 'sync_bn_forward_var', 'sync_bn_forward_output',
'sync_bn_backward_param', 'sync_bn_backward_data'
])
class SyncBatchNormFunction(Function):
@staticmethod
def symbolic(g, input, running_mean, running_var, weight, bias, momentum,
eps, group, group_size, stats_mode):
return g.op(
'mmcv::MMCVSyncBatchNorm',
input,
running_mean,
running_var,
weight,
bias,
momentum_f=momentum,
eps_f=eps,
group_i=group,
group_size_i=group_size,
stats_mode=stats_mode)
@staticmethod
def forward(self, input, running_mean, running_var, weight, bias, momentum,
eps, group, group_size, stats_mode):
self.momentum = momentum
self.eps = eps
self.group = group
self.group_size = group_size
self.stats_mode = stats_mode
assert isinstance(
input, (torch.HalfTensor, torch.FloatTensor,
torch.cuda.HalfTensor, torch.cuda.FloatTensor)), \
f'only support Half or Float Tensor, but {input.type()}'
output = torch.zeros_like(input)
input3d = input.flatten(start_dim=2)
output3d = output.view_as(input3d)
num_channels = input3d.size(1)
# ensure mean/var/norm/std are initialized as zeros
# ``torch.empty()`` does not guarantee that
mean = torch.zeros(
num_channels, dtype=torch.float, device=input3d.device)
var = torch.zeros(
num_channels, dtype=torch.float, device=input3d.device)
norm = torch.zeros_like(
input3d, dtype=torch.float, device=input3d.device)
std = torch.zeros(
num_channels, dtype=torch.float, device=input3d.device)
batch_size = input3d.size(0)
if batch_size > 0:
ext_module.sync_bn_forward_mean(input3d, mean)
batch_flag = torch.ones([1], device=mean.device, dtype=mean.dtype)
else:
# skip updating mean and leave it as zeros when the input is empty
batch_flag = torch.zeros([1], device=mean.device, dtype=mean.dtype)
# synchronize mean and the batch flag
vec = torch.cat([mean, batch_flag])
if self.stats_mode == 'N':
vec *= batch_size
if self.group_size > 1:
dist.all_reduce(vec, group=self.group)
total_batch = vec[-1].detach()
mean = vec[:num_channels]
if self.stats_mode == 'default':
mean = mean / self.group_size
elif self.stats_mode == 'N':
mean = mean / total_batch.clamp(min=1)
else:
raise NotImplementedError
# leave var as zeros when the input is empty
if batch_size > 0:
ext_module.sync_bn_forward_var(input3d, mean, var)
if self.stats_mode == 'N':
var *= batch_size
if self.group_size > 1:
dist.all_reduce(var, group=self.group)
if self.stats_mode == 'default':
var /= self.group_size
elif self.stats_mode == 'N':
var /= total_batch.clamp(min=1)
else:
raise NotImplementedError
# if the total batch size over all the ranks is zero,
# we should not update the statistics in the current batch
update_flag = total_batch.clamp(max=1)
momentum = update_flag * self.momentum
ext_module.sync_bn_forward_output(
input3d,
mean,
var,
weight,
bias,
running_mean,
running_var,
norm,
std,
output3d,
eps=self.eps,
momentum=momentum,
group_size=self.group_size)
self.save_for_backward(norm, std, weight)
return output
@staticmethod
@once_differentiable
def backward(self, grad_output):
norm, std, weight = self.saved_tensors
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(weight)
grad_input = torch.zeros_like(grad_output)
grad_output3d = grad_output.flatten(start_dim=2)
grad_input3d = grad_input.view_as(grad_output3d)
batch_size = grad_input3d.size(0)
if batch_size > 0:
ext_module.sync_bn_backward_param(grad_output3d, norm, grad_weight,
grad_bias)
# all reduce
if self.group_size > 1:
dist.all_reduce(grad_weight, group=self.group)
dist.all_reduce(grad_bias, group=self.group)
grad_weight /= self.group_size
grad_bias /= self.group_size
if batch_size > 0:
ext_module.sync_bn_backward_data(grad_output3d, weight,
grad_weight, grad_bias, norm, std,
grad_input3d)
return grad_input, None, None, grad_weight, grad_bias, \
None, None, None, None, None
@NORM_LAYERS.register_module(name='MMSyncBN')
class SyncBatchNorm(Module):
"""Synchronized Batch Normalization.
Args:
num_features (int): number of features/chennels in input tensor
eps (float, optional): a value added to the denominator for numerical
stability. Defaults to 1e-5.
momentum (float, optional): the value used for the running_mean and
running_var computation. Defaults to 0.1.
affine (bool, optional): whether to use learnable affine parameters.
Defaults to True.
track_running_stats (bool, optional): whether to track the running
mean and variance during training. When set to False, this
module does not track such statistics, and initializes statistics
buffers ``running_mean`` and ``running_var`` as ``None``. When
these buffers are ``None``, this module always uses batch
statistics in both training and eval modes. Defaults to True.
group (int, optional): synchronization of stats happen within
each process group individually. By default it is synchronization
across the whole world. Defaults to None.
stats_mode (str, optional): The statistical mode. Available options
includes ``'default'`` and ``'N'``. Defaults to 'default'.
When ``stats_mode=='default'``, it computes the overall statistics
using those from each worker with equal weight, i.e., the
statistics are synchronized and simply divied by ``group``. This
mode will produce inaccurate statistics when empty tensors occur.
When ``stats_mode=='N'``, it compute the overall statistics using
the total number of batches in each worker ignoring the number of
group, i.e., the statistics are synchronized and then divied by
the total batch ``N``. This mode is beneficial when empty tensors
occur during training, as it average the total mean by the real
number of batch.
"""
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
group=None,
stats_mode='default'):
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
group = dist.group.WORLD if group is None else group
self.group = group
self.group_size = dist.get_world_size(group)
assert stats_mode in ['default', 'N'], \
f'"stats_mode" only accepts "default" and "N", got "{stats_mode}"'
self.stats_mode = stats_mode
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, self.stats_mode)
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},'
s += f'stats_mode={self.stats_mode})'
return s