Update LFNA ablation codes

exps/LFNA/lfna-ttss-hpnet.py

@@ -0,0 +1,134 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/lfna-ttss-hpnet.py --env_version v1 --hidden_dim 16
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from log_utils import time_string
+from log_utils import AverageMeter, convert_secs2time
+
+from utils import split_str2indexes
+
+from procedures.advanced_main import basic_train_fn, basic_eval_fn
+from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from datasets.synthetic_core import get_synthetic_env
+from models.xcore import get_model
+from xlayers import super_core
+
+
+from lfna_utils import lfna_setup, train_model, TimeData
+from lfna_models import HyperNet_VX as HyperNet
+
+
+def main(args):
+    logger, env_info, model_kwargs = lfna_setup(args)
+    dynamic_env = env_info["dynamic_env"]
+    model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+
+    total_time = env_info["total"]
+    for i in range(total_time):
+        for xkey in ("timestamp", "x", "y"):
+            nkey = "{:}-{:}".format(i, xkey)
+            assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
+    train_time_bar = total_time // 2
+
+    criterion = torch.nn.MSELoss()
+    logger.log("There are {:} weights.".format(model.get_w_container().numel()))
+
+    # pre-train the model
+    dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
+
+    shape_container = model.get_w_container().to_shape_container()
+    hypernet = HyperNet(shape_container, 16)
+    print(hypernet)
+
+    optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
+
+    best_loss, best_param = None, None
+    for _iepoch in range(args.epochs):
+        container = hypernet(None)
+
+        preds = model.forward_with_container(dataset.x, container)
+        optimizer.zero_grad()
+        loss = criterion(preds, dataset.y)
+        loss.backward()
+        optimizer.step()
+        # save best
+        if best_loss is None or best_loss > loss.item():
+            best_loss = loss.item()
+            best_param = copy.deepcopy(model.state_dict())
+    print("hyper-net : best={:.4f}".format(best_loss))
+
+    init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
+    logger.log("The pre-training loss is {:.4f}".format(init_loss))
+
+    print(model)
+    print(hypernet)
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Use the data in the past.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/lfna-debug",
+        help="The checkpoint directory.",
+    )
+    parser.add_argument(
+        "--env_version",
+        type=str,
+        required=True,
+        help="The synthetic enviornment version.",
+    )
+    parser.add_argument(
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
+    )
+    #####
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--meta_batch",
+        type=int,
+        default=32,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--meta_seq",
+        type=int,
+        default=10,
+        help="The length of the sequence for meta-model.",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+    args = parser.parse_args()
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "The save dir argument can not be None"
+    args.save_dir = "{:}-{:}-d{:}".format(
+        args.save_dir, args.env_version, args.hidden_dim
+    )
+    main(args)