Fix bugs

exps/GeMOSA/basic-prev.py

@@ -1,14 +1,18 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
-# python exps/LFNA/basic-prev.py --env_version v1 --prev_time 5 --hidden_dim 16 --epochs 500 --init_lr 0.1
-# python exps/LFNA/basic-prev.py --env_version v2 --hidden_dim 16 --epochs 1000 --init_lr 0.05
+# python exps/GeMOSA/basic-prev.py --env_version v1 --prev_time 5 --hidden_dim 16 --epochs 500 --init_lr 0.1
+# python exps/GeMOSA/basic-prev.py --env_version v2 --hidden_dim 16 --epochs 1000 --init_lr 0.05
 #####################################################
 import sys, time, copy, torch, random, argparse
 from tqdm import tqdm
 from copy import deepcopy
 from pathlib import Path
 
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
 from xautodl.procedures import (
     prepare_seed,
     prepare_logger,
@@ -38,9 +42,9 @@ def subsample(historical_x, historical_y, maxn=10000):
 
 
 def main(args):
-    logger, env_info, model_kwargs = lfna_setup(args)
+    logger, model_kwargs = lfna_setup(args)
 
-    w_container_per_epoch = dict()
+    w_containers = dict()
 
     per_timestamp_time, start_time = AverageMeter(), time.time()
     for idx in range(args.prev_time, env_info["total"]):
@@ -111,7 +115,7 @@ def main(args):
         save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
             idx, env_info["total"]
         )
-        w_container_per_epoch[idx] = model.get_w_container().no_grad_clone()
+        w_containers[idx] = model.get_w_container().no_grad_clone()
         save_checkpoint(
             {
                 "model_state_dict": model.state_dict(),
@@ -127,7 +131,7 @@ def main(args):
         start_time = time.time()
 
     save_checkpoint(
-        {"w_container_per_epoch": w_container_per_epoch},
+        {"w_containers": w_containers},
         logger.path(None) / "final-ckp.pth",
         logger,
     )

exps/GeMOSA/basic-same.py

@@ -68,6 +68,8 @@ def main(args):
         # build model
         model = get_model(**model_kwargs)
         model = model.to(args.device)
+        if idx == 0:
+            print(model)
         # build optimizer
         optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
         criterion = torch.nn.MSELoss()

exps/GeMOSA/lfna_utils.py

@@ -16,7 +16,7 @@ def lfna_setup(args):
         input_dim=1,
         output_dim=1,
         hidden_dims=[args.hidden_dim] * 2,
-        act_cls="gelu",
+        act_cls="relu",
         norm_cls="layer_norm_1d",
     )
     return logger, model_kwargs