expose num_steps_taken helper method on trainer to retrieve number of training steps of each unet

dalle2_pytorch/trainer.py

@@ -527,6 +527,17 @@ class DecoderTrainer(nn.Module):
 
         self.warmup_schedulers = warmup_schedulers
 
+    def validate_and_return_unet_number(self, unet_number = None):
+        if self.num_unets == 1:
+            unet_number = default(unet_number, 1)
+
+        assert exists(unet_number) and 1 <= unet_number <= self.num_unets
+        return unet_number
+
+    def num_steps_taken(self, unet_number = None):
+        unet_number = self.validate_and_return_unet_number(unet_number)
+        return self.steps[unet_number - 1].item()
+
     def save(self, path, overwrite = True, **kwargs):
         path = Path(path)
         assert not (path.exists() and not overwrite)
@@ -595,10 +606,7 @@ class DecoderTrainer(nn.Module):
         self.steps += F.one_hot(unet_index_tensor, num_classes = len(self.steps))
 
     def update(self, unet_number = None):
-        if self.num_unets == 1:
-            unet_number = default(unet_number, 1)
-
-        assert exists(unet_number) and 1 <= unet_number <= self.num_unets
+        unet_number = self.validate_and_return_unet_number(unet_number)
         index = unet_number - 1
 
         optimizer = getattr(self, f'optim{index}')
@@ -664,8 +672,7 @@ class DecoderTrainer(nn.Module):
         max_batch_size = None,
         **kwargs
     ):
-        if self.num_unets == 1:
-            unet_number = default(unet_number, 1)
+        unet_number = self.validate_and_return_unet_number(unet_number)
 
         total_loss = 0.
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.16.18'
+__version__ = '0.16.19'