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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'} | |
| # Grayscale-compatible Expression CNN from notebook | |
| # CNN model for Expression Recognition - Clean architecture with BatchNorm | |
| class ExpressionCNN(nn.Module): | |
| def __init__(self, num_classes=7): | |
| super(ExpressionCNN, self).__init__() | |
| # Conv Block 1 | |
| self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) | |
| self.bn1 = nn.BatchNorm2d(32) | |
| self.relu1 = nn.ReLU() | |
| self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Conv Block 2 | |
| self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) | |
| self.bn2 = nn.BatchNorm2d(64) | |
| self.relu2 = nn.ReLU() | |
| self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Conv Block 3 | |
| self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) | |
| self.bn3 = nn.BatchNorm2d(128) | |
| self.relu3 = nn.ReLU() | |
| self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Fully Connected Layers | |
| self.fc1 = nn.Linear(128 * 6 * 6, 512) | |
| self.bn4 = nn.BatchNorm1d(512) | |
| self.relu4 = nn.ReLU() | |
| self.fc2 = nn.Linear(512, 256) | |
| self.bn5 = nn.BatchNorm1d(256) | |
| self.relu5 = nn.ReLU() | |
| self.fc3 = nn.Linear(256, num_classes) | |
| def forward(self, x): | |
| # Conv Block 1 | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu1(x) | |
| x = self.pool1(x) | |
| # Conv Block 2 | |
| x = self.conv2(x) | |
| x = self.bn2(x) | |
| x = self.relu2(x) | |
| x = self.pool2(x) | |
| # Conv Block 3 | |
| x = self.conv3(x) | |
| x = self.bn3(x) | |
| x = self.relu3(x) | |
| x = self.pool3(x) | |
| # Flatten | |
| x = x.view(x.size(0), -1) | |
| # FC layers | |
| x = self.fc1(x) | |
| x = self.bn4(x) | |
| x = self.relu4(x) | |
| x = self.fc2(x) | |
| x = self.bn5(x) | |
| x = self.relu5(x) | |
| x = self.fc3(x) | |
| return x | |
| # Backward compatibility alias | |
| facExpRec = ExpressionCNN | |
| # Sample Helper function | |
| def rgb2gray(image): | |
| return image.convert('L') | |
| # Transformation function - normalized grayscale | |
| trnscm = transforms.Compose([ | |
| rgb2gray, # Convert to grayscale | |
| transforms.Resize((48, 48)), # Resize to model input size | |
| transforms.ToTensor(), # Convert to tensor | |
| transforms.Normalize(mean=[0.5], std=[0.5]) # Normalize grayscale image | |
| ]) | |