diff --git a/codes/data/full_image_dataset.py b/codes/data/full_image_dataset.py
index 9e890dc8..e4f297ab 100644
--- a/codes/data/full_image_dataset.py
+++ b/codes/data/full_image_dataset.py
@@ -102,7 +102,7 @@ class FullImageDataset(data.Dataset):
         left = self.pick_along_range(w, square_size, .3)
         top = self.pick_along_range(w, square_size, .3)
 
-        mask = np.zeros((h, w, 1), dtype=np.float)
+        mask = np.zeros((h, w, 1), dtype=image.dtype)
         mask[top:top+square_size, left:left+square_size] = 1
         patch = image[top:top+square_size, left:left+square_size, :]
         center = torch.tensor([top + square_size // 2, left + square_size // 2], dtype=torch.long)
diff --git a/codes/models/ExtensibleTrainer.py b/codes/models/ExtensibleTrainer.py
index 985d017f..20130840 100644
--- a/codes/models/ExtensibleTrainer.py
+++ b/codes/models/ExtensibleTrainer.py
@@ -82,8 +82,14 @@ class ExtensibleTrainer(BaseModel):
 
         # Initialize amp.
         total_nets = [g for g in self.netsG.values()] + [d for d in self.netsD.values()]
-        amp_nets, amp_opts = amp.initialize(total_nets + [self.netF] + self.steps,
-                                            self.optimizers, opt_level=opt['amp_opt_level'], num_losses=len(opt['steps']))
+        if 'amp_opt_level' in opt.keys():
+            self.env['amp'] = True
+            amp_nets, amp_opts = amp.initialize(total_nets + [self.netF] + self.steps,
+                                                self.optimizers, opt_level=opt['amp_opt_level'], num_losses=len(opt['steps']))
+        else:
+            amp_nets = total_nets + [self.netF] + self.steps
+            amp_opts = self.optimizers
+            self.env['amp'] = False
 
         # Unwrap steps & netF
         self.netF = amp_nets[len(total_nets)]
diff --git a/codes/models/steps/steps.py b/codes/models/steps/steps.py
index 9e8080be..e7de2e8d 100644
--- a/codes/models/steps/steps.py
+++ b/codes/models/steps/steps.py
@@ -115,8 +115,11 @@ class ConfigurableStep(Module):
             total_loss = total_loss / self.env['mega_batch_factor']
 
             # Get dem grads!
-            with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
-                scaled_loss.backward()
+            if self.env['amp']:
+                with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
+                    scaled_loss.backward()
+            else:
+                total_loss.backward()
 
         # Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
         # we must release the gradients.