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PyTorch – Feature Extraction in Convents

PyTorch Feature Extraction in Convents

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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