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。
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)