class torch.nn.InstanceNorm1d(num_features: int, eps: float = 1e-05, momentum: float = 0.1, affine: bool = False, track_running_stats: bool = False)
[source]
Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch. $\gamma$ and $\beta$ are learnable parameter vectors of size C
(where C
is the input size) if affine
is True
. The standard-deviation 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 to True
, 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 default momentum
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
InstanceNorm1d
and LayerNorm
are very similar, but have some subtle differences. InstanceNorm1d
is applied on each channel of channeled data like multidimensional time series, but LayerNorm
is usually applied on entire sample and often in NLP tasks. Additionally, LayerNorm
applies elementwise affine transform, while InstanceNorm1d
usually don’t apply affine transform.
True
, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default: False
.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: False
Examples:
>>> # Without Learnable Parameters >>> m = nn.InstanceNorm1d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm1d(100, affine=True) >>> input = torch.randn(20, 100, 40) >>> output = m(input)
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.InstanceNorm1d.html