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import math
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
# Add more imports if required
# Sample Transformation function
# YOUR CODE HERE for changing the Transformation values.
trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
##Example Network
class SiameseNetwork(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.cnn1 = nn.Sequential(
nn.ReflectionPad2d(1), #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
nn.Conv2d(1, 4, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(4),
nn.ReflectionPad2d(1),
nn.Conv2d(4, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
nn.ReflectionPad2d(1),
nn.Conv2d(8, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
)
self.fc1 = nn.Sequential(
nn.Linear(8*100*100, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 5))
# forward_once is for one image. This can be used while classifying the face images
def forward_once(self, x):
output = self.cnn1(x)
output = output.view(output.size()[0], -1)
output = self.fc1(output)
return output
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
return output1, output2
##########################################################################################################
## Sample classification network (Specify if you are using a pytorch classifier during the training) ##
## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...) ##
##########################################################################################################
# YOUR CODE HERE for pytorch classifier
# Definition of classes as dictionary
classes = ['person1','person2','person3','person4','person5','person6','person7'] |