Pytorch中的Batch Normalization layer踩坑

1. 注意momentum的定义

Pytorch中的BN层的动量平滑和常见的动量法计算方式是相反的，默认的momentum=0.1
$${\hat{x}}_{\text{ new }}=(1-\text{ momentum })\times \hat{x}+\text{ momemtum }\times x_t$$
BN层里的表达式为：
$$y=\frac{x-\mathrm{E}[x]}{\sqrt{\mathrm{Var}[x]+ϵ}}\ast \gamma +\beta$$
其中γ和β是可以学习的参数。在Pytorch中，BN层的类的参数有：

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CLASS torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

每个参数具体含义参见文档，需要注意的是，affine定义了BN层的参数γ和β是否是可学习的(不可学习默认是常数1和0).

2. 注意BN层中含有统计数据数值，即均值和方差

track_running_stats – a boolean value that when set to True, this module tracks the running mean and variance, and when set to False, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: True

在训练过程中model.train()，train过程的BN的统计数值—均值和方差是通过当前batch数据估计的。

并且测试时，model.eval()后，若track_running_stats=True，模型此刻所使用的统计数据是Running status 中的，即通过指数衰减规则，积累到当前的数值。否则依然使用基于当前batch数据的估计值。

3. BN层的统计数据更新是在每一次训练阶段model.train()后的forward()方法中自动实现的，而不是在梯度计算与反向传播中更新optim.step()中完成

4. 冻结BN及其统计数据

从上面的分析可以看出来，正确的冻结BN的方式是在模型训练时，把BN单独挑出来，重新设置其状态为eval (在model.train()之后覆盖training状态）.

解决方案：转载自

You should use apply instead of searching its children, while named_children() doesn’t iteratively search submodules.

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def set_bn_eval(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
      m.eval()

model.apply(set_bn_eval)

或者，重写module中的train()方法：转载自

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def train(self, mode=True):
        """
        Override the default train() to freeze the BN parameters
        """
        super(MyNet, self).train(mode)
        if self.freeze_bn:
            print("Freezing Mean/Var of BatchNorm2D.")
            if self.freeze_bn_affine:
                print("Freezing Weight/Bias of BatchNorm2D.")
        if self.freeze_bn:
            for m in self.backbone.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eval()
                    if self.freeze_bn_affine:
                        m.weight.requires_grad = False
                        m.bias.requires_grad = False

5. Fix/frozen Batch Norm when training may lead to RuntimeError: expected scalar type Half but found Float

解决办法：转载自

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import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
from torch.autograd import Variable
from apex.fp16_utils import *

def fix_bn(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
        m.eval()

model = models.resnet50(pretrained=True)
model.cuda()
model = network_to_half(model)
model.train()
model.apply(fix_bn) # fix batchnorm
input = Variable(torch.FloatTensor(8, 3, 224, 224).cuda().half())
output = model(input)
output_mean = torch.mean(output)
output_mean.backward()

Please do

复制代码def fix_bn(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: m.eval().half()

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def fix_bn(m):
    classname = m.__class__.__name__
    if classname.find('BatchNorm') != -1:
        m.eval().half()

Reason for this is, for regular training it is better (performance-wise) to use cudnn batch norm, which requires its weights to be in fp32, thus batch norm modules are not converted to half in network_to_half. However, cudnn does not support batchnorm backward in the eval mode , which is what you are doing, and to use pytorch implementation for this, weights have to be of the same type as inputs.

最后

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