oneflow.nn.GroupNorm

class oneflow.nn.GroupNorm(num_groups: int, num_channels: int, eps: float = 1e-05, affine: bool = True)

Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization

\[y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta\]

The input channels are separated into num_groups groups, each containing num_channels / num_groups channels. The mean and standard-deviation are calculated separately over the each group. \(\gamma\) and \(\beta\) are learnable per-channel affine transform parameter vectors of size num_channels if affine is True. The standard-deviation is calculated via the biased estimator, equivalent to torch.var(input, unbiased=False).

This layer uses statistics computed from input data in both training and evaluation modes.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.GroupNorm.html.

Parameters
  • num_groups (int) – number of groups to separate the channels into

  • num_channels (int) – number of channels expected in input

  • eps – a value added to the denominator for numerical stability. Default: 1e-5

  • affine – a boolean value that when set to True, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: True.

Shape:
  • Input: \((N, C, *)\) where \(C=\text{num_channels}\)

  • Output: \((N, C, *)\) (same shape as input)

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> input = flow.Tensor(np.random.randn(20, 6, 10, 10))
>>> # Separate 6 channels into 3 groups
>>> m = flow.nn.GroupNorm(3, 6)
>>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)
>>> m = flow.nn.GroupNorm(6, 6)
>>> # Put all 6 channels into a single group (equivalent with LayerNorm)
>>> m = flow.nn.GroupNorm(1, 6)
>>> # Activating the module
>>> output = m(input)
__init__(num_groups: int, num_channels: int, eps: float = 1e-05, affine: bool = True)None

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__(*args, **kwargs)

Call self as a function.

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattr__(name)

__getattribute__(name, /)

Return getattr(self, name).

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(num_groups, num_channels[, eps, affine])

Initialize self.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

Create and return a new object.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_apply(fn[, applied_dict])

_get_name()

_load_from_state_dict(state_dict, prefix, …)

_named_members(get_members_fn[, prefix, recurse])

_save_to_state_dict(destination, prefix, …)

_shallow_repr()

add_module(name, module)

Adds a child module to the current module.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

buffers([recurse])

Returns an iterator over module buffers.

children()

Returns an iterator over immediate children modules.

cpu()

Moves all model parameters and buffers to the CPU.

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Sets the module in evaluation mode.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(input)

half()

Casts all floating point parameters and buffers to half datatype.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

modules()

Returns an iterator over all modules in the network.

named_buffers([prefix, recurse])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix])

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Returns an iterator over module parameters.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_forward_hook(hook)

Registers a forward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_parameter(name, param)

Adds a parameter to the module.

reset_parameters()

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

to([device])

Moves the parameters and buffers.

to_consistent(*args, **kwargs)

This interface is no longer available, please use oneflow.nn.Module.to_global() instead.

to_global([placement, sbp])

Convert the parameters and buffers to global.

train([mode])

Sets the module in training mode.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.