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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 Siamese(torch.nn.Module): | |
| def __init__(self): | |
| super(Siamese, self).__init__() | |
| #YOUR CODE HERE | |
| 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 | |
| # def forward(self, x): | |
| # pass # remove 'pass' once you have written your code | |
| #YOUR CODE HERE | |
| ########################################################################################################## | |
| ## 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 | |
| classes = ['person1','person2','person6','person7'] | |
| num_of_classes = len(classes) | |
| classifier = nn.Sequential(nn.Linear(256, 64), nn.BatchNorm1d(64), nn.ReLU(), | |
| nn.Linear(64, 32), nn.BatchNorm1d(32), nn.ReLU(), | |
| nn.Linear(32, num_of_classes)) | |
| #classifier = classifier.to(device) -error thrown | |
| print(classifier) | |
| class MLPClassifier(nn.Module): | |
| def __init__(self, input_size, hidden_size, num_classes): | |
| super(MLPClassifier, self).__init__() | |
| self.fc1 = nn.Linear(input_size, hidden_size) | |
| self.relu1 = nn.ReLU() | |
| self.fc2 = nn.Linear(hidden_size, 2048) | |
| self.relu2 = nn.ReLU() | |
| self.fc3 = nn.Linear(2048, 1024) | |
| self.relu3 = nn.ReLU() | |
| self.fc4 = nn.Linear(1024, num_classes) | |
| def forward(self, x): | |
| x = self.fc1(x) | |
| x = self.relu1(x) | |
| x = self.fc2(x) | |
| x = self.relu2(x) | |
| x = self.fc3(x) | |
| x = self.relu2(x) | |
| x = self.fc4(x) | |
| return x | |