Disable gradients in inference mode

python/mpact/models/train.py

@@ -28,20 +28,21 @@ def training_loop(model, optimizer, loss_function, train, validation, epochs=10)
             tloss += loss.data.item()
 
         # Switch to inference mode.
-        model.eval()
-        vloss = 0.0
-        num_validation = len(validation)  # in batches
-        num_correct = 0
-        num_total = 0
-        for inp, target in validation:  # batch loop (validation)
-            output = model(inp)
-            loss = loss_function(output, target)
-            vloss += loss.data.item()
-            correct = torch.eq(
-                torch.max(F.softmax(output, dim=1), dim=1)[1], target
-            ).view(-1)
-            num_correct += torch.sum(correct).item()
-            num_total += correct.shape[0]
+        model.eval()  # disables e.g. model drop-out
+        with torch.no_grad():  # disables gradient computations
+            vloss = 0.0
+            num_validation = len(validation)  # in batches
+            num_correct = 0
+            num_total = 0
+            for inp, target in validation:  # batch loop (validation)
+                output = model(inp)
+                loss = loss_function(output, target)
+                vloss += loss.data.item()
+                correct = torch.eq(
+                    torch.max(F.softmax(output, dim=1), dim=1)[1], target
+                ).view(-1)
+                num_correct += torch.sum(correct).item()
+                num_total += correct.shape[0]
 
         # Report stats.
         print(