models.py

## TODO: define the convolutional neural network architecture

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
# can use the below import should you choose to initialize the weights of your Net
import torch.nn.init as I

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        ## TODO: Define all the layers of this CNN, the only requirements are:
        ## 1. This network takes in a square (same width and height), grayscale image as input
        ## 2. It ends with a linear layer that represents the keypoints
        ## it's suggested that you make this last layer output 136 values, 2 for each of the 68 keypoint (x, y) pairs
        
        # As an example, you've been given a convolutional layer, which you may (but don't have to) change:
        # 1 input image channel (grayscale), 32 output channels/feature maps, 5x5 square convolution kernel
        # output_dim = (W-F)/S + 1 
        self.conv1 = nn.Conv2d(1, 32, 5) #(224-5)/1 + 1 = 220
        self.conv2 = nn.Conv2d(32, 64, 3) #(110-3)/1 + 1 = 108
        self.conv3 = nn.Conv2d(64, 128, 3) #(54-3)/1 + 1 = 52
        self.conv4 = nn.Conv2d(128, 256, 3) #(26-3)/1 + 1 = 24
        self.conv5 = nn.Conv2d(256, 512, 1) #(12-1)/1 + 1 = 12

        self.pool = nn.MaxPool2d(2, 2)

        self.fc1 = nn.Linear(512 * 6 * 6, 1024)
        self.fc2 = nn.Linear(1024, 512)
        self.fc3 = nn.Linear(512, 68 * 2)
        
        self.drop1 = nn.Dropout(p=0.2)
      
    def forward(self, x):
        ## TODO: Define the feedforward behavior of this model
        ## x is the input image and, as an example, here you may choose to include a pool/conv step:
        
        # two conv/relu + pool layers
        x = self.pool(F.relu(self.conv1(x)))
        x = self.drop1(x)
        x = self.pool(F.relu(self.conv2(x)))
        x = self.drop1(x)
        x = self.pool(F.relu(self.conv3(x)))
        x = self.drop1(x)
        x = self.pool(F.relu(self.conv4(x)))
        x = self.drop1(x)
        x = self.pool(F.relu(self.conv5(x)))
        x = self.drop1(x)

        # prep for linear layer
        # flatten the inputs into a vector
        x = x.view(x.size(0), -1)
        
        # linear layers with dropout in between
        x = F.relu(self.fc1(x))
        x = self.drop1(x)
        x = F.relu(self.fc2(x))
        x = self.drop1(x)
        x = self.fc3(x)

        # a modified x, having gone through all the layers of your model, should be returned
        return x