X

exps/GeMOSA/baselines/maml-ft.py

@@ -1,30 +1,33 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
-# python exps/LFNA/basic-maml.py --env_version v1 --inner_step 5
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v1 --hidden_dim 16 --inner_step 5
-# python exps/LFNA/basic-maml.py --env_version v2
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v2 --hidden_dim 16
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v3 --hidden_dim 32
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v4 --hidden_dim 32
 #####################################################
 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()
+lib_dir = (Path(__file__).parent / ".." / ".." / "..").resolve()
+print(lib_dir)
 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 xautodl.procedures import (
-from log_utils import time_string
+    prepare_seed,
-from log_utils import AverageMeter, convert_secs2time
+    prepare_logger,
+    save_checkpoint,
+    copy_checkpoint,
+)
+from xautodl.log_utils import time_string
+from xautodl.log_utils import AverageMeter, convert_secs2time
 
-from utils import split_str2indexes
+from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
-
+from xautodl.datasets.synthetic_core import get_synthetic_env
-from procedures.advanced_main import basic_train_fn, basic_eval_fn
+from xautodl.models.xcore import get_model
-from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from xautodl.xlayers import super_core
-from datasets.synthetic_core import get_synthetic_env, EnvSampler
-from models.xcore import get_model
-from xlayers import super_core
-
-from lfna_utils import lfna_setup, TimeData
 
 
 class MAML:
@@ -34,31 +37,22 @@ class MAML:
         self, network, criterion, epochs, meta_lr, inner_lr=0.01, inner_step=1
     ):
         self.criterion = criterion
-        # self.container = container
         self.network = network
         self.meta_optimizer = torch.optim.Adam(
             self.network.parameters(), lr=meta_lr, amsgrad=True
         )
-        self.meta_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
-            self.meta_optimizer,
-            milestones=[
-                int(epochs * 0.8),
-                int(epochs * 0.9),
-            ],
-            gamma=0.1,
-        )
         self.inner_lr = inner_lr
         self.inner_step = inner_step
         self._best_info = dict(state_dict=None, iepoch=None, score=None)
         print("There are {:} weights.".format(self.network.get_w_container().numel()))
 
-    def adapt(self, dataset):
+    def adapt(self, x, y):
         # create a container for the future timestamp
         container = self.network.get_w_container()
 
         for k in range(0, self.inner_step):
-            y_hat = self.network.forward_with_container(dataset.x, container)
+            y_hat = self.network.forward_with_container(x, container)
-            loss = self.criterion(y_hat, dataset.y)
+            loss = self.criterion(y_hat, y)
             grads = torch.autograd.grad(loss, container.parameters())
             container = container.additive([-self.inner_lr * grad for grad in grads])
         return container
@@ -73,7 +67,6 @@ class MAML:
     def step(self):
         torch.nn.utils.clip_grad_norm_(self.network.parameters(), 1.0)
         self.meta_optimizer.step()
-        self.meta_lr_scheduler.step()
 
     def zero_grad(self):
         self.meta_optimizer.zero_grad()
@@ -82,14 +75,12 @@ class MAML:
         self.criterion.load_state_dict(state_dict["criterion"])
         self.network.load_state_dict(state_dict["network"])
         self.meta_optimizer.load_state_dict(state_dict["meta_optimizer"])
-        self.meta_lr_scheduler.load_state_dict(state_dict["meta_lr_scheduler"])
 
     def state_dict(self):
         state_dict = dict()
         state_dict["criterion"] = self.criterion.state_dict()
         state_dict["network"] = self.network.state_dict()
         state_dict["meta_optimizer"] = self.meta_optimizer.state_dict()
-        state_dict["meta_lr_scheduler"] = self.meta_lr_scheduler.state_dict()
         return state_dict
 
     def save_best(self, score):
@@ -101,12 +92,39 @@ class MAML:
 
 
 def main(args):
-    logger, env_info, model_kwargs = lfna_setup(args)
+    prepare_seed(args.rand_seed)
+    logger = prepare_logger(args)
+    train_env = get_synthetic_env(mode="train", version=args.env_version)
+    valid_env = get_synthetic_env(mode="valid", version=args.env_version)
+    trainval_env = get_synthetic_env(mode="trainval", version=args.env_version)
+    test_env = get_synthetic_env(mode="test", version=args.env_version)
+    all_env = get_synthetic_env(mode=None, version=args.env_version)
+    logger.log("The training enviornment: {:}".format(train_env))
+    logger.log("The validation enviornment: {:}".format(valid_env))
+    logger.log("The trainval enviornment: {:}".format(trainval_env))
+    logger.log("The total enviornment: {:}".format(all_env))
+    logger.log("The test enviornment: {:}".format(test_env))
+    model_kwargs = dict(
+        config=dict(model_type="norm_mlp"),
+        input_dim=all_env.meta_info["input_dim"],
+        output_dim=all_env.meta_info["output_dim"],
+        hidden_dims=[args.hidden_dim] * 2,
+        act_cls="relu",
+        norm_cls="layer_norm_1d",
+    )
+
     model = get_model(**model_kwargs)
-
+    model = model.to(args.device)
-    dynamic_env = get_synthetic_env(mode="train", version=args.env_version)
+    if all_env.meta_info["task"] == "regression":
-
+        criterion = torch.nn.MSELoss()
-    criterion = torch.nn.MSELoss()
+        metric_cls = MSEMetric
+    elif all_env.meta_info["task"] == "classification":
+        criterion = torch.nn.CrossEntropyLoss()
+        metric_cls = Top1AccMetric
+    else:
+        raise ValueError(
+            "This task ({:}) is not supported.".format(all_env.meta_info["task"])
+        )
 
     maml = MAML(
         model, criterion, args.epochs, args.meta_lr, args.inner_lr, args.inner_step
@@ -127,14 +145,16 @@ def main(args):
         maml.zero_grad()
         meta_losses = []
         for ibatch in range(args.meta_batch):
-            future_timestamp = dynamic_env.random_timestamp()
+            future_idx = random.randint(0, len(trainval_env) - 1)
-            _, (future_x, future_y) = dynamic_env(future_timestamp)
+            future_t, (future_x, future_y) = trainval_env[future_idx]
-            past_timestamp = (
+            # -->>
-                future_timestamp - args.prev_time * dynamic_env.timestamp_interval
+            seq_times = trainval_env.get_seq_times(future_idx, args.seq_length)
-            )
+            _, (allxs, allys) = trainval_env.seq_call(seq_times)
-            _, (past_x, past_y) = dynamic_env(past_timestamp)
+            allxs, allys = allxs.view(-1, allxs.shape[-1]), allys.view(-1, 1)
-
+            if trainval_env.meta_info["task"] == "classification":
-            future_container = maml.adapt(TimeData(past_timestamp, past_x, past_y))
+                allys = allys.view(-1)
+            historical_x, historical_y = allxs.to(args.device), allys.to(args.device)
+            future_container = maml.adapt(historical_x, historical_y)
             future_y_hat = maml.predict(future_x, future_container)
             future_loss = maml.criterion(future_y_hat, future_y)
             meta_losses.append(future_loss)
@@ -157,37 +177,67 @@ def main(args):
 
     # meta-test
     maml.load_best()
-    eval_env = env_info["dynamic_env"]
+
-    assert eval_env.timestamp_interval == dynamic_env.timestamp_interval
+    def finetune(index):
-    w_container_per_epoch = dict()
+        seq_times = test_env.get_seq_times(index, args.seq_length)
-    for idx in range(args.prev_time, len(eval_env)):
+        _, (allxs, allys) = test_env.seq_call(seq_times)
-        future_timestamp, (future_x, future_y) = eval_env[idx]
+        allxs, allys = allxs.view(-1, allxs.shape[-1]), allys.view(-1, 1)
-        past_timestamp = (
+        if test_env.meta_info["task"] == "classification":
-            future_timestamp.item() - args.prev_time * eval_env.timestamp_interval
+            allys = allys.view(-1)
-        )
+        historical_x, historical_y = allxs.to(args.device), allys.to(args.device)
-        _, (past_x, past_y) = eval_env(past_timestamp)
+        future_container = maml.adapt(historical_x, historical_y)
-        future_container = maml.adapt(TimeData(past_timestamp, past_x, past_y))
+
-        w_container_per_epoch[idx] = future_container.no_grad_clone()
+        historical_y_hat = maml.predict(historical_x, future_container)
+        train_metric = metric_cls(True)
+        # model.analyze_weights()
         with torch.no_grad():
-            future_y_hat = maml.predict(future_x, w_container_per_epoch[idx])
+            train_metric(historical_y_hat, historical_y)
-            future_loss = maml.criterion(future_y_hat, future_y)
+        train_results = train_metric.get_info()
-        logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
+        return train_results, future_container
-    save_checkpoint(
+
-        {"w_container_per_epoch": w_container_per_epoch},
+    train_results, future_container = finetune(0)
-        logger.path(None) / "final-ckp.pth",
+
-        logger,
+    metric = metric_cls(True)
-    )
+    per_timestamp_time, start_time = AverageMeter(), time.time()
+    for idx, (future_time, (future_x, future_y)) in enumerate(test_env):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(per_timestamp_time.avg * (len(test_env) - idx), True)
+        )
+        logger.log(
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, len(test_env))
+            + " "
+            + need_time
+        )
+
+        # build optimizer
+        future_x.to(args.device), future_y.to(args.device)
+        future_y_hat = maml.predict(future_x, future_container)
+        future_loss = criterion(future_y_hat, future_y)
+        metric(future_y_hat, future_y)
+        log_str = (
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, len(test_env))
+            + " train-score: {:.5f}, eval-score: {:.5f}".format(
+                train_results["score"], metric.get_info()["score"]
+            )
+        )
+        logger.log(log_str)
+        logger.log("")
+        per_timestamp_time.update(time.time() - start_time)
+        start_time = time.time()
 
     logger.log("-" * 200 + "\n")
     logger.close()
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Use the data in the past.")
+    parser = argparse.ArgumentParser("Use the maml.")
     parser.add_argument(
         "--save_dir",
         type=str,
-        default="./outputs/lfna-synthetic/use-maml",
+        default="./outputs/lfna-synthetic/use-maml-nft",
         help="The checkpoint directory.",
     )
     parser.add_argument(
@@ -205,15 +255,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--meta_lr",
         type=float,
-        default=0.01,
+        default=0.02,
         help="The learning rate for the MAML optimizer (default is Adam)",
     )
-    parser.add_argument(
-        "--fail_thresh",
-        type=float,
-        default=1000,
-        help="The threshold for the failure, which we reuse the previous best model",
-    )
     parser.add_argument(
         "--inner_lr",
         type=float,
@@ -224,15 +268,12 @@ if __name__ == "__main__":
         "--inner_step", type=int, default=1, help="The inner loop steps for MAML."
     )
     parser.add_argument(
-        "--prev_time",
+        "--seq_length", type=int, default=20, help="The sequence length."
-        type=int,
-        default=5,
-        help="The gap between prev_time and current_timestamp",
     )
     parser.add_argument(
         "--meta_batch",
         type=int,
-        default=64,
+        default=256,
         help="The batch size for the meta-model",
     )
     parser.add_argument(
@@ -247,6 +288,12 @@ if __name__ == "__main__":
         default=50,
         help="The maximum epochs for early stop.",
     )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="",
+    )
     parser.add_argument(
         "--workers",
         type=int,
@@ -259,12 +306,11 @@ if __name__ == "__main__":
     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 = "{:}-s{:}-mlr{:}-d{:}-prev{:}-e{:}-env{:}".format(
+    args.save_dir = "{:}-s{:}-mlr{:}-d{:}-e{:}-env{:}".format(
         args.save_dir,
         args.inner_step,
         args.meta_lr,
         args.hidden_dim,
-        args.prev_time,
         args.epochs,
         args.env_version,
     )

exps/GeMOSA/baselines/maml-nof.py

@@ -0,0 +1,317 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v1 --hidden_dim 16 --inner_step 5
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v2 --hidden_dim 16
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v3 --hidden_dim 32
+# python exps/GeMOSA/baselines/maml-nof.py --env_version v4 --hidden_dim 32
+#####################################################
+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()
+print(lib_dir)
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+from xautodl.procedures import (
+    prepare_seed,
+    prepare_logger,
+    save_checkpoint,
+    copy_checkpoint,
+)
+from xautodl.log_utils import time_string
+from xautodl.log_utils import AverageMeter, convert_secs2time
+
+from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from xautodl.datasets.synthetic_core import get_synthetic_env
+from xautodl.models.xcore import get_model
+from xautodl.xlayers import super_core
+
+
+class MAML:
+    """A LFNA meta-model that uses the MLP as delta-net."""
+
+    def __init__(
+        self, network, criterion, epochs, meta_lr, inner_lr=0.01, inner_step=1
+    ):
+        self.criterion = criterion
+        self.network = network
+        self.meta_optimizer = torch.optim.Adam(
+            self.network.parameters(), lr=meta_lr, amsgrad=True
+        )
+        self.inner_lr = inner_lr
+        self.inner_step = inner_step
+        self._best_info = dict(state_dict=None, iepoch=None, score=None)
+        print("There are {:} weights.".format(self.network.get_w_container().numel()))
+
+    def adapt(self, x, y):
+        # create a container for the future timestamp
+        container = self.network.get_w_container()
+
+        for k in range(0, self.inner_step):
+            y_hat = self.network.forward_with_container(x, container)
+            loss = self.criterion(y_hat, y)
+            grads = torch.autograd.grad(loss, container.parameters())
+            container = container.additive([-self.inner_lr * grad for grad in grads])
+        return container
+
+    def predict(self, x, container=None):
+        if container is not None:
+            y_hat = self.network.forward_with_container(x, container)
+        else:
+            y_hat = self.network(x)
+        return y_hat
+
+    def step(self):
+        torch.nn.utils.clip_grad_norm_(self.network.parameters(), 1.0)
+        self.meta_optimizer.step()
+
+    def zero_grad(self):
+        self.meta_optimizer.zero_grad()
+
+    def load_state_dict(self, state_dict):
+        self.criterion.load_state_dict(state_dict["criterion"])
+        self.network.load_state_dict(state_dict["network"])
+        self.meta_optimizer.load_state_dict(state_dict["meta_optimizer"])
+
+    def state_dict(self):
+        state_dict = dict()
+        state_dict["criterion"] = self.criterion.state_dict()
+        state_dict["network"] = self.network.state_dict()
+        state_dict["meta_optimizer"] = self.meta_optimizer.state_dict()
+        return state_dict
+
+    def save_best(self, score):
+        success, best_score = self.network.save_best(score)
+        return success, best_score
+
+    def load_best(self):
+        self.network.load_best()
+
+
+def main(args):
+    prepare_seed(args.rand_seed)
+    logger = prepare_logger(args)
+    train_env = get_synthetic_env(mode="train", version=args.env_version)
+    valid_env = get_synthetic_env(mode="valid", version=args.env_version)
+    trainval_env = get_synthetic_env(mode="trainval", version=args.env_version)
+    test_env = get_synthetic_env(mode="test", version=args.env_version)
+    all_env = get_synthetic_env(mode=None, version=args.env_version)
+    logger.log("The training enviornment: {:}".format(train_env))
+    logger.log("The validation enviornment: {:}".format(valid_env))
+    logger.log("The trainval enviornment: {:}".format(trainval_env))
+    logger.log("The total enviornment: {:}".format(all_env))
+    logger.log("The test enviornment: {:}".format(test_env))
+    model_kwargs = dict(
+        config=dict(model_type="norm_mlp"),
+        input_dim=all_env.meta_info["input_dim"],
+        output_dim=all_env.meta_info["output_dim"],
+        hidden_dims=[args.hidden_dim] * 2,
+        act_cls="relu",
+        norm_cls="layer_norm_1d",
+    )
+
+    model = get_model(**model_kwargs)
+    model = model.to(args.device)
+    if all_env.meta_info["task"] == "regression":
+        criterion = torch.nn.MSELoss()
+        metric_cls = MSEMetric
+    elif all_env.meta_info["task"] == "classification":
+        criterion = torch.nn.CrossEntropyLoss()
+        metric_cls = Top1AccMetric
+    else:
+        raise ValueError(
+            "This task ({:}) is not supported.".format(all_env.meta_info["task"])
+        )
+
+    maml = MAML(
+        model, criterion, args.epochs, args.meta_lr, args.inner_lr, args.inner_step
+    )
+
+    # meta-training
+    last_success_epoch = 0
+    per_epoch_time, start_time = AverageMeter(), time.time()
+    for iepoch in range(args.epochs):
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+        head_str = (
+            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+            + need_time
+        )
+
+        maml.zero_grad()
+        meta_losses = []
+        for ibatch in range(args.meta_batch):
+            future_idx = random.randint(0, len(trainval_env) - 1)
+            future_t, (future_x, future_y) = trainval_env[future_idx]
+            # -->>
+            seq_times = trainval_env.get_seq_times(future_idx, args.seq_length)
+            _, (allxs, allys) = trainval_env.seq_call(seq_times)
+            allxs, allys = allxs.view(-1, allxs.shape[-1]), allys.view(-1, 1)
+            if trainval_env.meta_info["task"] == "classification":
+                allys = allys.view(-1)
+            historical_x, historical_y = allxs.to(args.device), allys.to(args.device)
+            future_container = maml.adapt(historical_x, historical_y)
+            future_y_hat = maml.predict(future_x, future_container)
+            future_loss = maml.criterion(future_y_hat, future_y)
+            meta_losses.append(future_loss)
+        meta_loss = torch.stack(meta_losses).mean()
+        meta_loss.backward()
+        maml.step()
+
+        logger.log(head_str + " meta-loss: {:.4f}".format(meta_loss.item()))
+        success, best_score = maml.save_best(-meta_loss.item())
+        if success:
+            logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
+            save_checkpoint(maml.state_dict(), logger.path("model"), logger)
+            last_success_epoch = iepoch
+        if iepoch - last_success_epoch >= args.early_stop_thresh:
+            logger.log("Early stop at {:}".format(iepoch))
+            break
+
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    # meta-test
+    maml.load_best()
+
+    def finetune(index):
+        seq_times = test_env.get_seq_times(index, args.seq_length)
+        _, (allxs, allys) = test_env.seq_call(seq_times)
+        allxs, allys = allxs.view(-1, allxs.shape[-1]), allys.view(-1, 1)
+        if test_env.meta_info["task"] == "classification":
+            allys = allys.view(-1)
+        historical_x, historical_y = allxs.to(args.device), allys.to(args.device)
+        future_container = maml.adapt(historical_x, historical_y)
+
+        historical_y_hat = maml.predict(historical_x, future_container)
+        train_metric = metric_cls(True)
+        # model.analyze_weights()
+        with torch.no_grad():
+            train_metric(historical_y_hat, historical_y)
+        train_results = train_metric.get_info()
+        return train_results, future_container
+
+    train_results, future_container = finetune(0)
+
+    metric = metric_cls(True)
+    per_timestamp_time, start_time = AverageMeter(), time.time()
+    for idx, (future_time, (future_x, future_y)) in enumerate(test_env):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(per_timestamp_time.avg * (len(test_env) - idx), True)
+        )
+        logger.log(
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, len(test_env))
+            + " "
+            + need_time
+        )
+
+        # build optimizer
+        future_x.to(args.device), future_y.to(args.device)
+        future_y_hat = maml.predict(future_x, future_container)
+        future_loss = criterion(future_y_hat, future_y)
+        metric(future_y_hat, future_y)
+        log_str = (
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, len(test_env))
+            + " train-score: {:.5f}, eval-score: {:.5f}".format(
+                train_results["score"], metric.get_info()["score"]
+            )
+        )
+        logger.log(log_str)
+        logger.log("")
+        per_timestamp_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Use the maml.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/use-maml-nft",
+        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,
+        default=16,
+        help="The hidden dimension.",
+    )
+    parser.add_argument(
+        "--meta_lr",
+        type=float,
+        default=0.02,
+        help="The learning rate for the MAML optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--inner_lr",
+        type=float,
+        default=0.005,
+        help="The learning rate for the inner optimization",
+    )
+    parser.add_argument(
+        "--inner_step", type=int, default=1, help="The inner loop steps for MAML."
+    )
+    parser.add_argument(
+        "--seq_length", type=int, default=20, help="The sequence length."
+    )
+    parser.add_argument(
+        "--meta_batch",
+        type=int,
+        default=256,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
+    )
+    parser.add_argument(
+        "--early_stop_thresh",
+        type=int,
+        default=50,
+        help="The maximum epochs for early stop.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=4,
+        help="The number of data loading workers (default: 4)",
+    )
+    # 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 = "{:}-s{:}-mlr{:}-d{:}-e{:}-env{:}".format(
+        args.save_dir,
+        args.inner_step,
+        args.meta_lr,
+        args.hidden_dim,
+        args.epochs,
+        args.env_version,
+    )
+    main(args)

exps/GeMOSA/basic-same.py

@@ -28,7 +28,6 @@ from xautodl.log_utils import AverageMeter, convert_secs2time
 
 from xautodl.utils import split_str2indexes
 
-from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
 from xautodl.procedures.metric_utils import (
     SaveMetric,
     MSEMetric,

exps/GeMOSA/side_utils.py

@@ -1,50 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
-#####################################################
-import copy
-import torch
-from tqdm import tqdm
-from xautodl.procedures import prepare_seed, prepare_logger
-from xautodl.datasets.synthetic_core import get_synthetic_env
-
-
-def train_model(model, dataset, lr, epochs):
-    criterion = torch.nn.MSELoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
-    best_loss, best_param = None, None
-    for _iepoch in range(epochs):
-        preds = model(dataset.x)
-        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())
-    model.load_state_dict(best_param)
-    return best_loss
-
-
-class TimeData:
-    def __init__(self, timestamp, xs, ys):
-        self._timestamp = timestamp
-        self._xs = xs
-        self._ys = ys
-
-    @property
-    def x(self):
-        return self._xs
-
-    @property
-    def y(self):
-        return self._ys
-
-    @property
-    def timestamp(self):
-        return self._timestamp
-
-    def __repr__(self):
-        return "{name}(timestamp={timestamp}, with {num} samples)".format(
-            name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
-        )