InstanceNorm3d¶

class
torch.nn.
InstanceNorm3d
(num_features, eps=1e05, momentum=0.1, affine=False, track_running_stats=False, device=None, dtype=None)[source]¶ Applies Instance Normalization over a 5D input (a minibatch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
$y = \frac{x  \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta$The mean and standarddeviation are calculated perdimension separately for each object in a minibatch. $\gamma$ and $\beta$ are learnable parameter vectors of size C (where C is the input size) if
affine
isTrue
. The standarddeviation is calculated via the biased estimator, equivalent to torch.var(input, unbiased=False).By default, this layer uses instance statistics computed from input data in both training and evaluation modes.
If
track_running_stats
is set toTrue
, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a defaultmomentum
of 0.1.Note
This
momentum
argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is $\hat{x}_\text{new} = (1  \text{momentum}) \times \hat{x} + \text{momentum} \times x_t$, where $\hat{x}$ is the estimated statistic and $x_t$ is the new observed value.Note
InstanceNorm3d
andLayerNorm
are very similar, but have some subtle differences.InstanceNorm3d
is applied on each channel of channeled data like 3D models with RGB color, butLayerNorm
is usually applied on entire sample and often in NLP tasks. Additionally,LayerNorm
applies elementwise affine transform, whileInstanceNorm3d
usually don’t apply affine transform. Parameters
num_features – $C$ from an expected input of size $(N, C, D, H, W)$ or $(C, D, H, W)$
eps – a value added to the denominator for numerical stability. Default: 1e5
momentum – the value used for the running_mean and running_var computation. Default: 0.1
affine – a boolean value that when set to
True
, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default:False
.track_running_stats – a boolean value that when set to
True
, this module tracks the running mean and variance, and when set toFalse
, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default:False
 Shape:
Input: $(N, C, D, H, W)$ or $(C, D, H, W)$
Output: $(N, C, D, H, W)$ or $(C, D, H, W)$ (same shape as input)
Examples:
>>> # Without Learnable Parameters >>> m = nn.InstanceNorm3d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm3d(100, affine=True) >>> input = torch.randn(20, 100, 35, 45, 10) >>> output = m(input)