Update exp_recognition_model.t7

app/Hackathon_setup/exp_recognition.py

@@ -61,11 +61,21 @@ def get_expression(img):
     ##the same path as this file, we recommend to put in the same directory                 ##
     ##########################################################################################
     ##########################################################################################
-
+    face_det_net = facExpRec()
+    model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device)
+    face_det_net.load_state_dict(model['net_dict'])
     face = detected_face(img)
     if face==0:
         face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
-    
+
+    with torch.no_grad():
+        face = trnscm(face)
+        output = face_det_net(face)
+        _, predicted = torch.max(output, 1)
+
+    predicted_expression = classes[predicted.item()]
+
     # YOUR CODE HERE, return expression using your model
 
-    return "YET TO BE CODED"
+    return predicted_expression
+    

app/Hackathon_setup/exp_recognition_model.py

@@ -15,11 +15,45 @@ classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5:
 # 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
+        super(facExpRec, self)._init_()
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3)
+
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=64, kernel_size=3)
+
+        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
+
+        # Define the Fully connected layers
+        # The output of the second convolution layer will be input to the first fully connected layer
+        self.fc1 = nn.Linear(128 * 10 * 10, 256)
+        # 256 input features, 128 output features
+        self.fc2 = nn.Linear(256, 128)
+        # 128 input features, 64 output features
+        self.fc3 = nn.Linear(128, 64)
+        # 64 input features, 7 output features for our 7 defined classes
+        self.fc4 = nn.Linear(64, 7)
+
+        # Max pooling
+        self.pool = nn.MaxPool2d(kernel_size=2)  # Max pooling layer with filter size 2x2
+
+
+def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        # Flatten the image
+        x = x.view(-1, 128 * 10 * 10)  # Output shape of convolutional layer is 16*5*5
+
+        # Linear layers with RELU activation
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.relu(self.fc3(x))
+        x = self.fc4(x)
+        x = F.log_softmax(x, dim=1)
+        return x
+
+
+
+# remove 'pass' once you have written your code
         #YOUR CODE HERE
         
 # Sample Helper function

app/Hackathon_setup/exp_recognition_net.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4bdcc922b6a7eb0117e3c5da04e4ea074bb87b1d59f60ff99aeb9c33380a6b7
+size 13612996