In this guide, we will discuss Feature Extraction in Convents in PyTorch. Convolutional neural networks include a primary feature, extraction. Following steps are used to implement the feature extraction of convolutional neural network.
Step 1
Import the respective models to create the feature extraction model with “PyTorch”.
import torch import torch.nn as nn from torchvision import models
Step 2
Create a class of feature extractor which can be called as and when needed.
class Feature_extractor(nn.module):
def forward(self, input):
self.feature = input.clone()
return input
new_net = nn.Sequential().cuda() # the new network
target_layers = [conv_1, conv_2, conv_4] # layers you want to extract`
i = 1
for layer in list(cnn):
if isinstance(layer,nn.Conv2d):
name = "conv_"+str(i)
art_net.add_module(name,layer)
if name in target_layers:
new_net.add_module("extractor_"+str(i),Feature_extractor())
i+=1
if isinstance(layer,nn.ReLU):
name = "relu_"+str(i)
new_net.add_module(name,layer)
if isinstance(layer,nn.MaxPool2d):
name = "pool_"+str(i)
new_net.add_module(name,layer)
new_net.forward(your_image)
print (new_net.extractor_3.feature)
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