Update app/Hackathon_setup/exp_recognition_model.py

app/Hackathon_setup/exp_recognition_model.py

@@ -25,10 +25,15 @@ class facExpRec(torch.nn.Module):
         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*3*3, 256)
+        self.fc1 = self.fclayer(512, 256)
         self.fc2 = self.fclayer(256, 512)
-        self.fc3 = nn.Linear(512, out_features)
+        self.last = nn.Sequential(
+            nn.Linear(512, 256), 
+            nn.ReLU(),
+            nn.Linear(256, 7)
+        )
     
     def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
         return nn.Sequential(
@@ -50,20 +55,13 @@ class facExpRec(torch.nn.Module):
     
     def forward(self, x):
         x = self.conv1(x)
-        logger.info(x.shape)
         x = self.conv2(x)
-        logger.info(x.shape)
         x = self.conv3(x)
-        logger.info(x.shape)
-        logger.info(x.shape)
-        x = x.view(-1, 512*3*3)
-        logger.info(x.shape)
+        x = self.conv4(x)
+        x = x.view(-1, 512)
         x = self.fc1(x)
-        logger.info(x.shape)
         x = self.fc2(x)
-        logger.info(x.shape)
-        x = self.fc3(x)
-        logger.info(x.shape)
+        x = self.last(x)
         return x
         
 # Sample Helper function