diff --git a/pl_examples/basic_examples/image_classifier.py b/pl_examples/basic_examples/image_classifier.py
index 04e965c049..0968525e41 100644
--- a/pl_examples/basic_examples/image_classifier.py
+++ b/pl_examples/basic_examples/image_classifier.py
@@ -118,7 +118,8 @@ def cli_main():
     # ------------
     # testing
     # ------------
-    trainer.test(test_dataloaders=test_loader)
+    result = trainer.test(test_dataloaders=test_loader)
+    print(result)
 
 
 if __name__ == '__main__':
diff --git a/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py b/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
index 5cc1b2ff3a..bc494147b9 100644
--- a/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
+++ b/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
@@ -62,10 +62,9 @@ class DDPCPUSpawnBackend(Accelerator):
         # restore main state with best weights
         best_path = self.mp_queue.get()
         results = self.mp_queue.get()
-        last_path = self.mp_queue.get()
 
         # recover the weights of the processes trained in the children
-        self.__recover_child_process_weights(model, best_path, last_path)
+        self.__recover_child_process_weights(model, best_path)
         return results
 
     def ddp_train(self, process_idx, mp_queue, model):
@@ -187,16 +186,10 @@ class DDPCPUSpawnBackend(Accelerator):
         device_ids = None
         return device_ids
 
-    def __recover_child_process_weights(self, model, best_path, last_path):
+    def __recover_child_process_weights(self, model, best_path):
         # transfer back the best path to the trainer
         if self.trainer.checkpoint_callback:
             self.trainer.checkpoint_callback.best_model_path = best_path
-        # todo, pass also best score
-
-        # load last weights
-        if last_path is not None and not self.trainer.testing:
-            ckpt = pl_load(last_path, map_location=lambda storage, loc: storage)
-            model.load_state_dict(ckpt)
 
         self.trainer.model = model
 
@@ -211,10 +204,3 @@ class DDPCPUSpawnBackend(Accelerator):
             # todo, pass complete checkpoint as state dictionary
             mp_queue.put(best_model_path)
             mp_queue.put(results)
-
-            # save the last weights
-            last_path = None
-            if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
-                last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
-                atomic_save(model.state_dict(), last_path)
-            mp_queue.put(last_path)