Faster-RCNN中nn.Sequential(*list(vgg.classifier._modules.values())[:-1])的理解

Faster-RCNN中nn.Sequential(*list(vgg.classifier._modules.values())[:-1])的理解

原博：https://blog.csdn.net/a1103688841/article/details/89383215
先总结一下，self.RCNN_base = nn.Sequential(*list(vgg.features._modules.values())[:-1])，详解如下：

vgg是已经构建好的vgg模型。

vgg.features是取出vgg16网络中的features大层。其中vgg网络可以分为3大层，一层是（features），一层是（avgpool），最后一层是（classifier）。

vgg.features._modules是将取出来的网络转为字典显示

vgg.features._modules.values()是将字典的对应键值都取出，就是各个小层的网络参数都取出。

list(vgg.features._modules.values())是将这些网络层参数强制转为list类型。

list(vgg.features._modules.values())[:-1])是遍历这些网络的小层，舍弃最后一个小层结构

self.RCNN_base = nn.Sequential(*list(vgg.features._modules.values())[:-1])是取出vgg16网络中的features的大层，舍弃features的大层中最后一个池化层，然后将这些层结构赋值给RCNN_base。

下面根据代码进行详细解释：

import torchvision.models as models
vgg16 = models.vgg16()
import pprint
pprint.pprint(vgg16)
#以下是完整vgg16打印结果
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
 
===============================================================
 
pprint.pprint(vgg16.classifier)
#打印出名字叫（calssifier）的层。可以从一开始的vgg16打印结果可以看到，这个网络有3大层，一层是（features），一层是（avgpool），最后一层是（classifier）。
Sequential(
  (0): Linear(in_features=25088, out_features=4096, bias=True)
  (1): ReLU(inplace)
  (2): Dropout(p=0.5)
  (3): Linear(in_features=4096, out_features=4096, bias=True)
  (4): ReLU(inplace)
  (5): Dropout(p=0.5)
  (6): Linear(in_features=4096, out_features=1000, bias=True)
)
===============================================================
 
pprint.pprint(vgg16.classifier._modules)
#将取出来的大层转为字典存储
OrderedDict([('0', Linear(in_features=25088, out_features=4096, bias=True)),
             ('1', ReLU(inplace)),
             ('2', Dropout(p=0.5)),
             ('3', Linear(in_features=4096, out_features=4096, bias=True)),
             ('4', ReLU(inplace)),
             ('5', Dropout(p=0.5)),
             ('6', Linear(in_features=4096, out_features=1000, bias=True))])
===================================================================
 
pprint.pprint(vgg16.classifier._modules.values())
#打印大层对应的键值
odict_values([Linear(in_features=25088, out_features=4096, bias=True), ReLU(inplace), Dropout(p=0.5), Linear(in_features=4096, out_features=4096, bias=True), ReLU(inplace), Dropout(p=0.5), Linear(in_features=4096, out_features=1000, bias=True)])
====================================================================
 
pprint.pprint(list(vgg16.classifier._modules.values())[:-1])
#将字典转为类别，舍弃最后一层。（注意这里取大层是叫“classifier”）
[Linear(in_features=25088, out_features=4096, bias=True),
 ReLU(inplace),
 Dropout(p=0.5),
 Linear(in_features=4096, out_features=4096, bias=True),
 ReLU(inplace),
 Dropout(p=0.5)]
=========================================================================
 
import torch.nn as nn
vgg16.classifier = nn.Sequential(*list(vgg16.classifier._modules.values())[:-1])
#注意这里等式左边是大层classifier被赋值
#右边是将classifier取出转为列表，接着舍弃最后一层
print(vgg16)
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
  )
)
===================================================================
 
faster_rcnn=nn.Sequential(*list(vgg16.features._modules.values())[:-1])
pprint.pprint(list(vgg16.features._modules.values()))
#这里是将大层features的网络结构取出
print('*'*20)
pprint.pprint(list(vgg16.features._modules.values())[:-1])
#这里是将大层features的网络结构取出且舍弃最后一个层结构
[Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)]
********************
[Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 ReLU(inplace)]
========================================================================
 
print(faster_rcnn)
#打印最后得到的层结构
Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU(inplace)
  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU(inplace)
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU(inplace)
  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU(inplace)
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU(inplace)
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU(inplace)
  (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (18): ReLU(inplace)
  (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (20): ReLU(inplace)
  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (22): ReLU(inplace)
  (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (25): ReLU(inplace)
  (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (27): ReLU(inplace)
  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (29): ReLU(inplace)