nip.utils.torch.BatchNorm1dSimulateBatchDims

class nip.utils.torch.BatchNorm1dSimulateBatchDims(num_features: int, eps: float = 1e-05, momentum: float = 0.1, affine: bool = True, track_running_stats: bool = True, device=None, dtype=None)

Batch normalization layer with arbitrary batch dimensions.

See torch.nn.BatchNorm1d for documentation.

Assumes an input of shape (… features).

Methods Summary

forward(x)

Apply the module to the input tensor, simulating multiple batch dimensions.

Attributes

T_destination
call_super_init
dump_patches
feature_dims
num_features
eps
momentum
affine
track_running_stats
training

Methods

forward(x: Tensor) → Tensor

Apply the module to the input tensor, simulating multiple batch dimensions.

Parameters:

x (Tensor) – The input tensor.

Returns:

out (Tensor) – The output tensor after applying the module.