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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.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, 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...) ##
##########################################################################################################
# Load the existing trained DecisionTree classifier
import joblib
import os
# Get the current directory path
current_path = os.path.dirname(os.path.abspath(__file__))
# Load the pre-trained DecisionTree classifier
try:
classifier_path = os.path.join(current_path, 'decision_tree_model.sav')
classifier = joblib.load(classifier_path)
print(f"✓ Loaded DecisionTree classifier with {len(classifier.classes_)} classes")
except Exception as e:
print(f"✗ Error loading DecisionTree classifier: {e}")
classifier = None
# Definition of classes as dictionary
classes = ['Person1','Person2','Person3'] |