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README.md

@@ -6,8 +6,6 @@ colorTo: pink
 sdk: docker
 pinned: false
 license: mit
-app_port: 8001
 ---
 
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/Hackathon_setup/exp_recognition.py

@@ -67,5 +67,9 @@ def get_expression(img):
         face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
     
     # YOUR CODE HERE, return expression using your model
-
-    return "YET TO BE CODED"
+    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'])
+    output = face_det_net(trnscm(face))
+    _, pred = torch.max(output)
+    return classes[pred]

app/Hackathon_setup/exp_recognition_model.py

@@ -14,13 +14,46 @@ 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 __init__(self, out_features=7):
+        super().__init__()
+
+        self.conv1 = self.convlayer(in_channels=1, out_channels=64, kernel_size=3)
+        self.conv2 = self.convlayer(in_channels=64, out_channels=128, kernel_size=5)
+        self.conv3 = self.convlayer(in_channels=128, out_channels=512, kernel_size=3)
+        self.conv4 = self.convlayer(in_channels=512, out_channels=512, kernel_size=3, max_pool=1)
+
+        self.fc1 = self.fclayer(512, 256)
+        self.fc2 = self.fclayer(256, 512)
+        self.fc3 = nn.Linear(512, out_features)
+    
+    def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
+        return nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+            nn.Dropout2d(),
+            nn.MaxPool2d(kernel_size=max_pool),
+        )
+
+    def fclayer(self, in_features, out_features):
+        return nn.Sequential(
+            nn.Linear(in_features, out_features),
+            nn.BatchNorm1d(out_features),
+            nn.Dropout1d(0.4),
+            nn.ReLU(),
+        )
+
+    
     def forward(self, x):
-        pass   # remove 'pass' once you have written your code
-        #YOUR CODE HERE
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = x.view(-1, 512)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        return x
         
 # Sample Helper function
 def rgb2gray(image):
@@ -28,4 +61,4 @@ def rgb2gray(image):
     
 # Sample Transformation function
 #YOUR CODE HERE for changing the Transformation values.
-trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])
+trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor(), transforms.Normalize((0.5), (0.5))])

exp_recognition_net.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eabf0a7bedebe56bb37aecc6908bf665898203a3db6b476579f313df4889cf40
+size 13735490