Fix a bug where the normalizers stopped the loss history from being saved as a numpy scalar, and added test coverage

src/cdtools/reconstructors/base.py

@@ -218,12 +218,15 @@ class Reconstructor:
                 return total_loss
 
             # This takes the step for this minibatch
-            loss += self.optimizer.step(closure).detach().cpu().numpy()
+            loss += self.optimizer.step(closure).detach()
 
         if hasattr(self.model, 'loss_normalizer') and \
                 self.model.loss_normalizer is not None:
             loss = self.model.loss_normalizer.normalize_loss(loss)
 
+        # Make sure to return a scalar value which is fully numpy
+        loss = loss.cpu().numpy()[()]
+
         # We step the scheduler after the full epoch
         if self.scheduler is not None:
             self.scheduler.step(loss)

tests/test_reconstructors.py

@@ -1,3 +1,4 @@
+from numbers import Number
 import pytest
 import time
 import cdtools
@@ -114,6 +115,11 @@ def test_Adam_gold_balls(gold_ball_cxi, reconstruction_device, show_plot):
         time.sleep(3)
         plt.close('all')
 
+
+    # Check that the losses returned in loss_history are not torch tensors
+    assert isinstance(model.loss_history[-1], Number) and \
+        not isinstance(model.loss_history[-1], t.Tensor)
+
     # Ensure equivalency between the model reconstructions during the first
     # pass, where they should be identical
     assert np.allclose(model_recon.loss_history[:epoch_tup[0]], model.loss_history[:epoch_tup[0]])