python batchnorm2d_PyTorch中的nn.BatchNorm2d

class _NormBase(Module):   #源码

"""Common base of _InstanceNorm and _BatchNorm"""

_version = 2

__constants__ = ['track_running_stats', 'momentum', 'eps',

'num_features', 'affine']

def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,

track_running_stats=True):

super(_NormBase, self).__init__()

self.num_features = num_features

self.eps = eps

self.momentum = momentum

self.affine = affine

self.track_running_stats = track_running_stats

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_parameter('running_mean', None)

self.register_parameter('running_var', None)

self.register_parameter('num_batches_tracked', None)

self.reset_parameters()

torch.nn.BatchNorm1d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)