Cnn Features from a pre trained network

1

Can someone help me to get cnn features from 3 ,6 and 8 from VGG Net as shown.

import torchvision.models as models
import torch

model = models.vgg11(pretrained=True)

model
Out[2]:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): ReLU(inplace)
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace)
(8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace)
(10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(12): ReLU(inplace)
(13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(14): ReLU(inplace)
(15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU(inplace)
(18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(19): ReLU(inplace)
(20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(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)
)
)

2 
feat_net = model.features
feat_net3 = feat_net[:5] # don't forget the nonlinear func
feat_net6 = feat_net[:8]
feat_net8 = feat_net[:10]

At inference, just do

x = get_input()
features = feat_net3(x)