# megengine.module.normalization 源代码

```# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
#
# Unless required by applicable law or agreed to in writing,
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np

import megengine.functional as F
from megengine import Parameter

from .init import ones_, zeros_
from .module import Module

[文档]class GroupNorm(Module):
"""Simple implementation of GroupNorm. Only support 4d tensor now.
Reference: https://arxiv.org/pdf/1803.08494.pdf.
"""

def __init__(self, num_groups, num_channels, eps=1e-5, affine=True, **kwargs):
super().__init__(**kwargs)
assert num_channels % num_groups == 0
self.num_groups = num_groups
self.num_channels = num_channels
self.eps = eps
self.affine = affine
if self.affine:
self.weight = Parameter(np.ones(num_channels, dtype=np.float32))
self.bias = Parameter(np.zeros(num_channels, dtype=np.float32))
else:
self.weight = None
self.bias = None
self.reset_parameters()

def reset_parameters(self):
if self.affine:
ones_(self.weight)
zeros_(self.bias)

def forward(self, x):
N, C, H, W = x.shape
assert C == self.num_channels

x = x.reshape(N, self.num_groups, -1)
mean = x.mean(axis=2, keepdims=True)
var = (x * x).mean(axis=2, keepdims=True) - mean * mean

x = (x - mean) / F.sqrt(var + self.eps)
x = x.reshape(N, C, H, W)
if self.affine:
x = self.weight.reshape(1, -1, 1, 1) * x + self.bias.reshape(1, -1, 1, 1)

return x

def _module_info_string(self) -> str:
s = (
"groups={num_groups}, channels={num_channels}, "
"eps={eps}, affine={affine}"
)
return s.format(**self.__dict__)

[文档]class InstanceNorm(Module):
"""Simple implementation of InstanceNorm. Only support 4d tensor now.
Reference: https://arxiv.org/abs/1607.08022.
Note that InstanceNorm equals using GroupNome with num_groups=num_channels.
"""

def __init__(self, num_channels, eps=1e-05, affine=True, **kwargs):
super().__init__(**kwargs)
self.num_channels = num_channels
self.eps = eps
self.affine = affine
if self.affine:
self.weight = Parameter(np.ones(num_channels, dtype="float32"))
self.bias = Parameter(np.zeros(num_channels, dtype="float32"))
else:
self.weight = None
self.bias = None
self.reset_parameters()

def reset_parameters(self):
if self.affine:
ones_(self.weight)
zeros_(self.bias)

def forward(self, x):
N, C, H, W = x.shape
assert C == self.num_channels
x = x.reshape(N, C, -1)
mean = x.mean(axis=2, keepdims=True)
var = (x ** 2).mean(axis=2, keepdims=True) - mean * mean

x = (x - mean) / F.sqrt(var + self.eps)
x = x.reshape(N, C, H, W)
if self.affine:
x = self.weight.reshape(1, -1, 1, 1) * x + self.bias.reshape(1, -1, 1, 1)

return x

def _module_info_string(self) -> str:
s = "channels={num_channels}, eps={eps}, affine={affine}"
return s.format(**self.__dict__)

[文档]class LayerNorm(Module):
"""Simple implementation of LayerNorm. Support tensor of any shape as input.
Reference: https://arxiv.org/pdf/1803.08494.pdf.
"""

def __init__(self, normalized_shape, eps=1e-05, affine=True, **kwargs):
super().__init__(**kwargs)
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape,)
self.normalized_shape = tuple(normalized_shape)
self.eps = eps
self.affine = affine
if self.affine:
self.weight = Parameter(np.ones(self.normalized_shape, dtype="float32"))
self.bias = Parameter(np.zeros(self.normalized_shape, dtype="float32"))
else:
self.weight = None
self.bias = None

self.reset_parameters()

def reset_parameters(self):
if self.affine:
ones_(self.weight)
zeros_(self.bias)

def forward(self, x):
x = F.nn.layer_norm(
x, self.normalized_shape, self.affine, self.weight, self.bias, self.eps
)
return x

def _module_info_string(self) -> str:
s = "normalized_shape={normalized_shape}, eps={eps}, affine={affine}"
return s.format(**self.__dict__)
```