second commit

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 app/Hackathon_setup/siamese_model.t7 filter=lfs diff=lfs merge=lfs -text
+app/Hackathon_setup/exp_recognition_net.t7 filter=lfs diff=lfs merge=lfs -text

app/Hackathon_setup/exp_recognition.py

@@ -61,11 +61,16 @@ 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))
     
+    output = face_det_net(face)
+    
     # YOUR CODE HERE, return expression using your model
 
-    return "YET TO BE CODED"
+    return output

app/Hackathon_setup/exp_recognition_model.py

@@ -15,12 +15,34 @@ 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
         
+        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)
+        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1)  
+        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)  
+        self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) 
+        self.fc1 = nn.Linear(1024 * 1 * 1, 256)  
+        self.fc2 = nn.Linear(256, 128)
+        self.fc3 = nn.Linear(128, 64)
+        self.fc4 = nn.Linear(64, 7)
+
+        self.pool = nn.MaxPool2d(kernel_size=2)
+
     def forward(self, x):
-        pass   # remove 'pass' once you have written your code
-        #YOUR CODE HERE
+        x = self.pool(F.elu(self.conv1(x)))
+        x = self.pool(F.elu(self.conv2(x)))
+        x = self.pool(F.elu(self.conv3(x)))
+        x = self.pool(F.elu(self.conv4(x)))  
+        x = self.pool(F.elu(self.conv5(x))) 
+        x = self.pool(F.elu(self.conv6(x)))  
+        x = x.view(-1, 1024 * 1 * 1)  
+        x = F.elu(self.fc1(x))
+        x = F.elu(self.fc2(x))
+        x = F.elu(self.fc3(x))
+        x = self.fc4(x)
+        x = F.log_softmax(x, dim=1)
+        return x
         
 # Sample Helper function
 def rgb2gray(image):

app/Hackathon_setup/exp_recognition_net.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03b6c535a608d66cca9a4e794ce5186c51b3b52b148254023b1c631ca71e36b2
+size 4314282

app/Hackathon_setup/face_recognition.py

@@ -99,7 +99,5 @@ def get_face_class(img1):
     det_img1 = detected_face(img1)
     if(det_img1 == 0):
         det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
-    ##YOUR CODE HERE, return face class here
-    ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
-    ##Better Hint: Siamese experiment is covered in one of the labs
+        
     return "YET TO BE CODED"