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| import torch | |
| import torchvision | |
| import torch.nn as nn | |
| from torchvision import transforms | |
| ## Add more imports if required | |
| #################################################################################################################### | |
| # Define your model and transform and all necessary helper functions here # | |
| # They will be imported to the exp_recognition.py file # | |
| #################################################################################################################### | |
| # Definition of classes as dictionary | |
| classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'} | |
| # Example Network | |
| class facExpRec(torch.nn.Module): | |
| def __init__(self): | |
| pass # remove 'pass' once you have written your code | |
| #YOUR CODE HERE | |
| def forward(self, x): | |
| pass # remove 'pass' once you have written your code | |
| #YOUR CODE HERE | |
| class ExpressionRecognitionCNN(nn.Module): | |
| def __init__(self, num_classes = 7): | |
| super(ExpressionRecognitionCNN, self).__init__() | |
| self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) | |
| self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.fc1 = nn.Linear(64 * 25 * 25, 128) | |
| self.fc2 = nn.Linear(128, num_classes) | |
| def forward(self, x): | |
| x = self.pool(torch.relu(self.conv1(x))) | |
| x = self.pool(torch.relu(self.conv2(x))) | |
| x = x.view(-1, 64 * 25 * 25) | |
| x = torch.relu(self.fc1(x)) | |
| x = self.fc2(x) | |
| return x | |
| # Sample Helper function | |
| def rgb2gray(image): | |
| return image.convert('L') | |
| # 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(), | |
| transforms.Normalize((0.5,), (0.5,)),]) | |