group_norm

paddle.nn.functional. group_norm ( x: Tensor, num_groups: int, epsilon: float = 1e-05, weight: Tensor | None = None, bias: Tensor | None = None, data_format: DataLayout1D | DataLayout2D | DataLayout3D = 'NCHW', name: str | None = None ) → Tensor [source]

nn.GroupNorm is recommended. For more information, please refer to GroupNorm .

Parameters

  • x (Tensor) – Input Tensor with shape: attr:(batch, num_features, *).

  • num_groups (int) – The number of groups that divided from channels.

  • epsilon (float, optional) – The small value added to the variance to prevent division by zero. Default: 1e-05.

  • weight (Tensor, optional) – The weight Tensor of group_norm, with shape: attr:[num_channels]. Default: None.

  • bias (Tensor, optional) – The bias Tensor of group_norm, with shape: attr:[num_channels]. Default: None.

  • data_format (str, optional) – Specify the input data format. Support “NCL”, “NCHW”, “NCDHW”, “NLC”, “NHWC” or “NDHWC”. Default: “NCHW”.

  • name (str|None, optional) – Name for the GroupNorm, default is None. For more information, please refer to Name..

Returns

Tensor, the output has the same shape with x.

Examples

>>> import paddle
>>> paddle.seed(100)
>>> x = paddle.arange(48, dtype="float32").reshape((2, 6, 2, 2))
>>> group_norm_out = paddle.nn.functional.group_norm(x, num_groups=6)

>>> print(group_norm_out)
Tensor(shape=[2, 6, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]]],
 [[[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]],
  [[-1.34163547, -0.44721183],
   [ 0.44721183,  1.34163547]]]])