pytorch-获取模型中间结果，*list的含义

获取模型的中间结果的简单方法

以alexnet为例

import torchvision.models as models
import torch.nn as nn

if __name__ == '__main__':
    alexnet = models.alexnet(pretrained=True)
    print(alexnet)
    alexnet.classifier = nn.Sequential(*list(alexnet.classifier.children())[::2])  # 返回直接子模块上的迭代器,[::2],针对所有，取步长为2
    print(alexnet.classifier) #改变后的 alexnet.classifier模块
    print(alexnet) # 改变后的

结果：

print(alexnet)：

AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)

print(alexnet.classifier)：

Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): ReLU(inplace=True)
(2): Linear(in_features=4096, out_features=4096, bias=True)
(3): Linear(in_features=4096, out_features=1000, bias=True)
)

print(alexnet)：

AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): ReLU(inplace=True)
(2): Linear(in_features=4096, out_features=4096, bias=True)
(3): Linear(in_features=4096, out_features=1000, bias=True)
)
)

补基础知识：*list：提取列表里面的元素

lst =[1,2,3]
print(*lst[:-1])# 1 2，提取列表里面的元素

def add(a, b):
    return a + b
data = [4, 3]
print(add(*data)) # 7 # equals to print add(4, 3)
data = {'a': 5, 'b': 7}
print(add(**data)) # 12 # equals to print add(5, 7)