Ajay85/hackathon

1import torch2import torchvision3import torch.nn as nn4from torchvision import transforms5import torch.nn.functional as F6## Add more imports if required7 8####################################################################################################################9# Define your model and transform and all necessary helper functions here                                          #10# They will be imported to the exp_recognition.py file                                                             #11####################################################################################################################12 13# Definition of classes as dictionary14classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}15 16# Example Network17class facExpRec(torch.nn.Module):18    def __init__(self):19        super(facExpRec, self).__init__()20 21 22        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3)23        self.conv2 = nn.Conv2d(in_channels=16, out_channels=64, kernel_size=3)24        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)25        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1)  26        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)  27        self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) 28        self.fc1 = nn.Linear(1024 * 1 * 1, 256)  29        self.fc2 = nn.Linear(256, 128)30        self.fc3 = nn.Linear(128, 64)31        self.fc4 = nn.Linear(64, 7)32 33        self.pool = nn.MaxPool2d(kernel_size=2)34        #YOUR CODE HERE35        36    def forward(self, x):37        x = self.pool(F.elu(self.conv1(x)))38        x = self.pool(F.elu(self.conv2(x)))39        x = self.pool(F.elu(self.conv3(x)))40        x = self.pool(F.elu(self.conv4(x)))  41        x = self.pool(F.elu(self.conv5(x))) 42        x = self.pool(F.elu(self.conv6(x)))  43        x = x.view(-1, 1024 * 1 * 1)  44        x = F.elu(self.fc1(x))45        x = F.elu(self.fc2(x))46        x = F.elu(self.fc3(x))47        x = self.fc4(x)48        x = F.log_softmax(x, dim=1)49        return x50        51# Sample Helper function52def rgb2gray(image):53    return image.convert('L')54    55# Sample Transformation function56#YOUR CODE HERE for changing the Transformation values.57trnscm = transforms.Compose([rgb2gray, transforms.Resize((100,100)), transforms.ToTensor()])