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a47c171 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import torch
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.Grayscale(num_output_channels=1),
transforms.Resize((100, 100)),
transforms.ToTensor(),
])
##Example Network
class Siamese(torch.nn.Module):
def __init__(self):
super(Siamese, 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, 25))
# 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
class SiameseV2(nn.Module):
"""Stronger Siamese encoder for retraining.
The original network keeps the full 100x100 spatial map until the first
linear layer. This makes it parameter-heavy, weakly translation tolerant,
and prone to learning dataset-specific pixel positions instead of face
identity. Use this model when you retrain the Siamese checkpoint.
"""
def __init__(self, embedding_dim=128):
super().__init__()
self.cnn1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
)
self.fc1 = nn.Sequential(
nn.Flatten(),
nn.Linear(256, embedding_dim),
)
def forward_once(self, x):
output = self.cnn1(x)
output = self.fc1(output)
return F.normalize(output, p=2, dim=1)
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']
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