Collapse progressive zoom candidates into the batch dimension

This contributes a significant speedup to training this type of network
since losses can operate on the entire prediction spectrum at once.

codes/models/steps/progressive_zoom.py

@@ -103,8 +103,10 @@ class ProgressiveGeneratorInjector(Injector):
                 self.produce_progressive_visual_debugs(chain_input, chain_output, debug_index)
                 debug_index += 1
         results[self.hq_output_key] = results_hq
+
+        # Results are concatenated into the batch dimension, to allow normal losses to be used against the output.
         for k, v in results.items():
-            results[k] = torch.stack(v, dim=1)
+            results[k] = torch.cat(v, dim=0)
         return results