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.