InstanceNorm2d¶
- class torch.nn.InstanceNorm2d(num_features, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False, device=None, dtype=None)[source]¶
Applies Instance Normalization over a 4D input (a mini-batch of 2D 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 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
isTrue
. 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 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
InstanceNorm2d
andLayerNorm
are very similar, but have some subtle differences.InstanceNorm2d
is applied on each channel of channeled data like RGB images, butLayerNorm
is usually applied on entire sample and often in NLP tasks. Additionally,LayerNorm
applies elementwise affine transform, whileInstanceNorm2d
usually don’t apply affine transform.- Parameters
num_features (int) – $C$ from an expected input of size $(N, C, H, W)$ or $(C, H, W)$
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:False
.track_running_stats (bool) – 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: $(N, C, H, W)$ or $(C, H, W)$
Output: $(N, C, H, W)$ or $(C, H, W)$ (same shape as input)
Examples:
>>> # Without Learnable Parameters >>> m = nn.InstanceNorm2d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm2d(100, affine=True) >>> input = torch.randn(20, 100, 35, 45) >>> output = m(input)