- class torch.nn.LazyInstanceNorm2d(eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=None, dtype=None)[source]¶
torch.nn.InstanceNorm2dmodule with lazy initialization of the
num_featuresargument of the
InstanceNorm2dthat is inferred from the
input.size(1). The attributes that will be lazily initialized are weight, bias, running_mean and running_var.
torch.nn.modules.lazy.LazyModuleMixinfor further documentation on lazy modules and their limitations.
num_features – from an expected input of size or
eps (float) – a value added to the denominator for numerical stability. Default: 1e-5
momentum (float) – the value used for the running_mean and running_var computation. Default: 0.1
affine (bool) – a boolean value that when set to
True, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default:
track_running_stats (bool) – a boolean value that when set to
True, this module tracks the running mean and variance, and when set to
False, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default:
Output: or (same shape as input)