Update base models

exps/LFNA/basic-same.py

@@ -1,7 +1,7 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
-# python exps/LFNA/basic-same.py --srange 1-999 --env_version v1 --hidden_dim 16
+# python exps/LFNA/basic-same.py --env_version v1 --hidden_dim 16
 # python exps/LFNA/basic-same.py --srange 1-999 --env_version v2 --hidden_dim
 #####################################################
 import sys, time, copy, torch, random, argparse
@@ -38,27 +38,17 @@ def subsample(historical_x, historical_y, maxn=10000):
 def main(args):
     logger, env_info, model_kwargs = lfna_setup(args)
 
-    # check indexes to be evaluated
-    to_evaluate_indexes = split_str2indexes(args.srange, env_info["total"], None)
-    logger.log(
-        "Evaluate {:}, which has {:} timestamps in total.".format(
-            args.srange, len(to_evaluate_indexes)
-        )
-    )
-
     w_container_per_epoch = dict()
 
     per_timestamp_time, start_time = AverageMeter(), time.time()
-    for i, idx in enumerate(to_evaluate_indexes):
+    for idx in range(env_info["total"]):
 
         need_time = "Time Left: {:}".format(
-            convert_secs2time(
+            convert_secs2time(per_timestamp_time.avg * (env_info["total"] - idx), True)
-                per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
-            )
         )
         logger.log(
             "[{:}]".format(time_string())
-            + " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
+            + " [{:04d}/{:04d}]".format(idx, env_info["total"])
             + " "
             + need_time
         )
@@ -66,7 +56,8 @@ def main(args):
         historical_x = env_info["{:}-x".format(idx)]
         historical_y = env_info["{:}-y".format(idx)]
         # build model
-        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+        model = get_model(**model_kwargs)
+        print(model)
         # build optimizer
         optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
         criterion = torch.nn.MSELoss()
@@ -180,9 +171,6 @@ if __name__ == "__main__":
         default=1000,
         help="The total number of epochs.",
     )
-    parser.add_argument(
-        "--srange", type=str, required=True, help="The range of models to be evaluated"
-    )
     parser.add_argument(
         "--workers",
         type=int,

exps/LFNA/lfna-tall-hpnet.py

@@ -90,6 +90,7 @@ def main(args):
 
         final_loss = torch.stack(losses).mean()
         final_loss.backward()
+        torch.nn.utils.clip_grad_norm_(parameters, 1.0)
         optimizer.step()
         lr_scheduler.step()
 

exps/LFNA/lfna_utils.py

@@ -28,13 +28,24 @@ def lfna_setup(args):
         env_info["dynamic_env"] = dynamic_env
         torch.save(env_info, cache_path)
 
+    """
     model_kwargs = dict(
+        config=dict(model_type="simple_mlp"),
         input_dim=1,
         output_dim=1,
         hidden_dim=args.hidden_dim,
         act_cls="leaky_relu",
         norm_cls="identity",
     )
+    """
+    model_kwargs = dict(
+        config=dict(model_type="norm_mlp"),
+        input_dim=1,
+        output_dim=1,
+        hidden_dims=[args.hidden_dim] * 2,
+        act_cls="gelu",
+        norm_cls="layer_norm_1d",
+    )
     return logger, env_info, model_kwargs