removing some testing output

examples/fl_post/fl/project/src/nnunet_v1.py

@@ -239,13 +239,8 @@ def __init__(self, **kwargs):
     trainer.max_num_epochs = current_epoch + epochs
     trainer.epoch = current_epoch
 
-    print(f"Brandon DEBUG - about to initialize trainer, currently t.max_num:{trainer.max_num_epochs}, t.epo:{trainer.epoch}")
-
-    # TODO: call validation separately
     trainer.initialize(not validation_only)
 
-    print(f"Brandon DEBUG - after initialize trainer, currently t.max_num:{trainer.max_num_epochs}, t.epo:{trainer.epoch}")
-
     # infer total data size and batch size in order to get how many batches to apply so that over many epochs, each data
     # point is expected to be seen epochs number of times
 
@@ -276,8 +271,6 @@ def __init__(self, **kwargs):
             else:
                 # new training without pretraine weights, do nothing
                 pass
-            print(f"Brandon DEBUG - Calling trainer.run_training, trainer epoch: {trainer.epoch}, trainer max_num_epochs:{trainer.max_num_epochs}")
-            print(f"Brandon DEBUG - NOTE: this is where I had just loaded checkpoint.")
 
             batches_applied_train, \
             batches_applied_val, \

examples/fl_post/fl/project/src/runner_nnunetv1.py

@@ -245,8 +245,8 @@ def compare_tensor_dicts(td_1, td_2, tag="", epsilon=0.1, verbose=True):
             metrics = {'val_eval': this_val_eval_metrics}
         else:
             checkpoint_dict = self.load_checkpoint()
-            # double check
-            compare_tensor_dicts(td_1=input_tensor_dict,td_2=checkpoint_dict['state_dict'], tag="checkpoint VS fromOpenFL")
+            # double check uncomment below for testing
+            # compare_tensor_dicts(td_1=input_tensor_dict,td_2=checkpoint_dict['state_dict'], tag="checkpoint VS fromOpenFL")
 
             all_tr_losses, \
                 all_val_losses, \