pytorch固定BN层参数[通俗易懂]
背景：基于PyTorch的模型，想固定主分支参数，只训练子分支，结果发现在不同epoch相同的测试数据经过主分支输出的结果不同。
原因：未固定主分支BN层中的running_mean和running_var。
解决方法：将需要固定的BN层状态设置为eval。
问题示例：
环境：torch：1.7.0
# -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.bn1 = nn.BatchNorm2d(6)
self.conv2 = nn.Conv2d(6, 16, 3)
self.bn2 = nn.BatchNorm2d(16)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 6 * 6, 120) # 6*6 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 5)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.bn1(self.conv1(x))), (2, 2))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
def print_parameter_grad_info(net):
print('-------parameters requires grad info--------')
for name, p in net.named_parameters():
print(f'{name}:\t{p.requires_grad}')
def print_net_state_dict(net):
for key, v in net.state_dict().items():
print(f'{key}')
if __name__ == "__main__":
net = Net()
print_parameter_grad_info(net)
net.requires_grad_(False)
print_parameter_grad_info(net)
torch.random.manual_seed(5)
test_data = torch.rand(1, 1, 32, 32)
train_data = torch.rand(5, 1, 32, 32)
# print(test_data)
# print(train_data[0, ...])
for epoch in range(2):
# training phase, 假设每个epoch只迭代一次
net.train()
pre = net(train_data)
# 计算损失和参数更新等
# ....
# test phase
net.eval()
x = net(test_data)
print(f'epoch:{epoch}', x)运行结果：
-------parameters requires grad info--------
conv1.weight: True
conv1.bias: True
bn1.weight: True
bn1.bias: True
conv2.weight: True
conv2.bias: True
bn2.weight: True
bn2.bias: True
fc1.weight: True
fc1.bias: True
fc2.weight: True
fc2.bias: True
fc3.weight: True
fc3.bias: True
-------parameters requires grad info--------
conv1.weight: False
conv1.bias: False
bn1.weight: False
bn1.bias: False
conv2.weight: False
conv2.bias: False
bn2.weight: False
bn2.bias: False
fc1.weight: False
fc1.bias: False
fc2.weight: False
fc2.bias: False
fc3.weight: False
fc3.bias: False
epoch:0 tensor([[-0.0755, 0.1138, 0.0966, 0.0564, -0.0224]])
epoch:1 tensor([[-0.0763, 0.1113, 0.0970, 0.0574, -0.0235]])可以看到：
net.requires_grad_(False)已经将网络中的各参数设置成了不需要梯度更新的状态，但是同样的测试数据test_data在不同epoch中前向之后出现了不同的结果。
调用print_net_state_dict可以看到BN层中的参数running_mean和running_var并没在可优化参数net.parameters中
bn1.weight
bn1.bias
bn1.running_mean
bn1.running_var
bn1.num_batches_tracked但在training pahse的前向过程中，这两个参数被更新了。导致整个网络在freeze的情况下，同样的测试数据出现了不同的结果
Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default
momentumof 0.1. source
因此在training phase时对BN层显式设置eval状态：
if __name__ == "__main__":
net = Net()
net.requires_grad_(False)
torch.random.manual_seed(5)
test_data = torch.rand(1, 1, 32, 32)
train_data = torch.rand(5, 1, 32, 32)
# print(test_data)
# print(train_data[0, ...])
for epoch in range(2):
# training phase, 假设每个epoch只迭代一次
net.train()
net.bn1.eval()
net.bn2.eval()
pre = net(train_data)
# 计算损失和参数更新等
# ....
# test phase
net.eval()
x = net(test_data)
print(f'epoch:{epoch}', x)可以看到结果正常了：
epoch:0 tensor([[ 0.0944, -0.0372, 0.0059, -0.0625, -0.0048]])
epoch:1 tensor([[ 0.0944, -0.0372, 0.0059, -0.0625, -0.0048]])参考：
Cannot freeze batch normalization parameters
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