instance_norm¶

Normalizes each (sample, channel) slice independently across its spatial dims — mean and variance are computed per-sample rather than across the batch.

ATen source: aten.instance_norm (preserved as composite by get_decomp_table())

Inputs¶

Name	Shape	Description
`input`	`(N, C, *spatial)`	1, 2, or 3 spatial dims (e.g., `(N, C, H, W)` for 2D)
`gamma`	`(C,)`	Per-channel scale applied after normalization
`beta`	`(C,)`	Per-channel shift applied after the scale

Attributes¶

Name	Type	Description
`eps`	`float`	Numerical-stability epsilon
`version`	`int`	Composite op version

Output¶

Name	Shape	Description
`output`	`(N, C, *spatial)`	Same shape as `input`

Data types¶

fp16, fp32.

PyTorch example¶

import torch

N, C, H, W = 2, 6, 10, 10
input = torch.randn(N, C, H, W)
gamma = torch.randn(C)
beta = torch.randn(C)

output = torch.ops.aten.instance_norm.default(
    input, gamma, beta,
    None, None,        # running_mean / running_var unused in inference
    True,              # use_input_stats
    0.1,               # momentum (ignored in inference)
    1e-5,              # eps
    True,              # cudnn_enabled (ignored)
)

Reference¶

torch.nn.InstanceNorm2d