new sanity methode

experiments/meta_task_exp.py

@@ -45,7 +45,7 @@ from functionalities_test import test_for_fixpoints
 WORKER = 10 if not debug else 2
 debug = False
 BATCHSIZE = 500 if not debug else 50
-EPOCH = 100
+EPOCH = 50
 VALIDATION_FRQ = 3 if not debug else 1
 SELF_TRAIN_FRQ = 1 if not debug else 1
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -279,9 +279,9 @@ if __name__ == '__main__':
 
     self_train = True
     training = True
-    train_to_id_first = True
+    train_to_id_first = False
     train_to_task_first = False
-    train_to_task_first_sequential = False
+    train_to_task_first_sequential = True
     force_st_for_n_from_last_epochs = 5
 
     use_sparse_network = False
@@ -303,10 +303,12 @@ if __name__ == '__main__':
     # dr_str = f'{f"_dr_{dropout}" if dropout != 0 else ""}'
     id_str = f'{f"_StToId" if train_to_id_first else ""}'
     tsk_str = f'{f"_Tsk_{tsk_threshold}" if train_to_task_first and tsk_threshold != 1 else ""}'
+    sprs_str = '_sprs' if use_sparse_network else ''
     f_str = f'_f_{force_st_for_n_from_last_epochs}' if \
         force_st_for_n_from_last_epochs and train_to_task_first_sequential and train_to_task_first \
         else ""
-    exp_path = Path('output') / f'mn_{st_str}_{EPOCH}_{weight_hidden_size}{a_str}{res_str}{id_str}{tsk_str}{f_str}'
+    config_str = f'{a_str}{res_str}{id_str}{tsk_str}{f_str}{sprs_str}'
+    exp_path = Path('output') / f'mn_{st_str}_{EPOCH}_{weight_hidden_size}{config_str}'
 
     for seed in range(n_seeds):
         seed_path = exp_path / str(seed)
@@ -358,8 +360,8 @@ if __name__ == '__main__':
                 force_st = (force_st_for_n_from_last_epochs >= (EPOCH - epoch)
                             ) and train_to_task_first_sequential and force_st_for_n_from_last_epochs
                 for batch, (batch_x, batch_y) in tqdm(enumerate(d), total=len(d), desc='MetaNet Train - Batch'):
-                    # Self Train
 
+                    # Self Train
                     if self_train and ((not init_tsk and (is_self_train_epoch or init_st)) or force_st):
                         # Transfer weights
                         if use_sparse_network:
@@ -376,6 +378,8 @@ if __name__ == '__main__':
                         # Transfer weights
                         if use_sparse_network:
                             dense_metanet = dense_metanet.replace_particles(sparse_metanet.particle_weights)
+
+                    # Task Train
                     if not init_st:
                         # Zero your gradients for every batch!
                         dense_optimizer.zero_grad()