OrderedDict | popitem(last=True)

1.OrderedDict保存的东西

import torch
state_dict = torch.load("resnet18.pth")

for i in state_dict:
    print(i)

------------------------------------------
conv1.weight
bn1.running_mean
bn1.running_var
bn1.weight
bn1.bias
layer1.0.conv1.weight
layer1.0.bn1.running_mean
layer1.0.bn1.running_var
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.conv2.weight
layer1.0.bn2.running_mean
layer1.0.bn2.running_var
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.1.conv1.weight
layer1.1.bn1.running_mean
layer1.1.bn1.running_var
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.conv2.weight
layer1.1.bn2.running_mean
layer1.1.bn2.running_var
layer1.1.bn2.weight
layer1.1.bn2.bias
layer2.0.conv1.weight
layer2.0.bn1.running_mean
layer2.0.bn1.running_var
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.conv2.weight
layer2.0.bn2.running_mean
layer2.0.bn2.running_var
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.downsample.0.weight
layer2.0.downsample.1.running_mean
layer2.0.downsample.1.running_var
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.1.conv1.weight
layer2.1.bn1.running_mean
layer2.1.bn1.running_var
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.conv2.weight
layer2.1.bn2.running_mean
layer2.1.bn2.running_var
layer2.1.bn2.weight
layer2.1.bn2.bias
layer3.0.conv1.weight
layer3.0.bn1.running_mean
layer3.0.bn1.running_var
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.conv2.weight
layer3.0.bn2.running_mean
layer3.0.bn2.running_var
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.downsample.0.weight
layer3.0.downsample.1.running_mean
layer3.0.downsample.1.running_var
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.1.conv1.weight
layer3.1.bn1.running_mean
layer3.1.bn1.running_var
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.conv2.weight
layer3.1.bn2.running_mean
layer3.1.bn2.running_var
layer3.1.bn2.weight
layer3.1.bn2.bias
layer4.0.conv1.weight
layer4.0.bn1.running_mean
layer4.0.bn1.running_var
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.conv2.weight
layer4.0.bn2.running_mean
layer4.0.bn2.running_var
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.downsample.0.weight
layer4.0.downsample.1.running_mean
layer4.0.downsample.1.running_var
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.1.conv1.weight
layer4.1.bn1.running_mean
layer4.1.bn1.running_var
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.conv2.weight
layer4.1.bn2.running_mean
layer4.1.bn2.running_var
layer4.1.bn2.weight
layer4.1.bn2.bias
fc.weight
fc.bias

2.last = False,先进先出

import torch
state_dict = torch.load("resnet18.pth")

state_dict2 = state_dict.popitem(last = False)
for i in state_dict:
    print(i)
-------------------------------
bn1.running_mean
bn1.running_var
bn1.weight
bn1.bias
layer1.0.conv1.weight
layer1.0.bn1.running_mean
layer1.0.bn1.running_var
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.conv2.weight
layer1.0.bn2.running_mean
layer1.0.bn2.running_var
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.1.conv1.weight
layer1.1.bn1.running_mean
layer1.1.bn1.running_var
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.conv2.weight
layer1.1.bn2.running_mean
layer1.1.bn2.running_var
layer1.1.bn2.weight
layer1.1.bn2.bias
layer2.0.conv1.weight
layer2.0.bn1.running_mean
layer2.0.bn1.running_var
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.conv2.weight
layer2.0.bn2.running_mean
layer2.0.bn2.running_var
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.downsample.0.weight
layer2.0.downsample.1.running_mean
layer2.0.downsample.1.running_var
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.1.conv1.weight
layer2.1.bn1.running_mean
layer2.1.bn1.running_var
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.conv2.weight
layer2.1.bn2.running_mean
layer2.1.bn2.running_var
layer2.1.bn2.weight
layer2.1.bn2.bias
layer3.0.conv1.weight
layer3.0.bn1.running_mean
layer3.0.bn1.running_var
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.conv2.weight
layer3.0.bn2.running_mean
layer3.0.bn2.running_var
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.downsample.0.weight
layer3.0.downsample.1.running_mean
layer3.0.downsample.1.running_var
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.1.conv1.weight
layer3.1.bn1.running_mean
layer3.1.bn1.running_var
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.conv2.weight
layer3.1.bn2.running_mean
layer3.1.bn2.running_var
layer3.1.bn2.weight
layer3.1.bn2.bias
layer4.0.conv1.weight
layer4.0.bn1.running_mean
layer4.0.bn1.running_var
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.conv2.weight
layer4.0.bn2.running_mean
layer4.0.bn2.running_var
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.downsample.0.weight
layer4.0.downsample.1.running_mean
layer4.0.downsample.1.running_var
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.1.conv1.weight
layer4.1.bn1.running_mean
layer4.1.bn1.running_var
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.conv2.weight
layer4.1.bn2.running_mean
layer4.1.bn2.running_var
layer4.1.bn2.weight
layer4.1.bn2.bias
fc.weight
fc.bias

3.last = True,后进先出

import torch
state_dict = torch.load("resnet18.pth")

state_dict2 = state_dict.popitem(last = True)
for i in state_dict:
    print(i)

-------------------------------
conv1.weight
bn1.running_mean
bn1.running_var
bn1.weight
bn1.bias
layer1.0.conv1.weight
layer1.0.bn1.running_mean
layer1.0.bn1.running_var
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.conv2.weight
layer1.0.bn2.running_mean
layer1.0.bn2.running_var
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.1.conv1.weight
layer1.1.bn1.running_mean
layer1.1.bn1.running_var
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.conv2.weight
layer1.1.bn2.running_mean
layer1.1.bn2.running_var
layer1.1.bn2.weight
layer1.1.bn2.bias
layer2.0.conv1.weight
layer2.0.bn1.running_mean
layer2.0.bn1.running_var
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.conv2.weight
layer2.0.bn2.running_mean
layer2.0.bn2.running_var
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.downsample.0.weight
layer2.0.downsample.1.running_mean
layer2.0.downsample.1.running_var
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.1.conv1.weight
layer2.1.bn1.running_mean
layer2.1.bn1.running_var
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.conv2.weight
layer2.1.bn2.running_mean
layer2.1.bn2.running_var
layer2.1.bn2.weight
layer2.1.bn2.bias
layer3.0.conv1.weight
layer3.0.bn1.running_mean
layer3.0.bn1.running_var
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.conv2.weight
layer3.0.bn2.running_mean
layer3.0.bn2.running_var
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.downsample.0.weight
layer3.0.downsample.1.running_mean
layer3.0.downsample.1.running_var
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.1.conv1.weight
layer3.1.bn1.running_mean
layer3.1.bn1.running_var
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.conv2.weight
layer3.1.bn2.running_mean
layer3.1.bn2.running_var
layer3.1.bn2.weight
layer3.1.bn2.bias
layer4.0.conv1.weight
layer4.0.bn1.running_mean
layer4.0.bn1.running_var
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.conv2.weight
layer4.0.bn2.running_mean
layer4.0.bn2.running_var
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.downsample.0.weight
layer4.0.downsample.1.running_mean
layer4.0.downsample.1.running_var
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.1.conv1.weight
layer4.1.bn1.running_mean
layer4.1.bn1.running_var
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.conv2.weight
layer4.1.bn2.running_mean
layer4.1.bn2.running_var
layer4.1.bn2.weight
layer4.1.bn2.bias
fc.weight