Prototype MAML

exps/LFNA/lfna_utils.py

@@ -0,0 +1,61 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+import torch
+from tqdm import tqdm
+from procedures import prepare_seed, prepare_logger
+from datasets.synthetic_core import get_synthetic_env
+
+
+def lfna_setup(args):
+    prepare_seed(args.rand_seed)
+    logger = prepare_logger(args)
+
+    cache_path = (
+        logger.path(None) / ".." / "env-{:}-info.pth".format(args.env_version)
+    ).resolve()
+    if cache_path.exists():
+        env_info = torch.load(cache_path)
+    else:
+        env_info = dict()
+        dynamic_env = get_synthetic_env(version=args.env_version)
+        env_info["total"] = len(dynamic_env)
+        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
+            env_info["{:}-timestamp".format(idx)] = timestamp
+            env_info["{:}-x".format(idx)] = _allx
+            env_info["{:}-y".format(idx)] = _ally
+        env_info["dynamic_env"] = dynamic_env
+        torch.save(env_info, cache_path)
+
+    model_kwargs = dict(
+        input_dim=1,
+        output_dim=1,
+        hidden_dim=args.hidden_dim,
+        act_cls="leaky_relu",
+        norm_cls="identity",
+    )
+    return logger, env_info, model_kwargs
+
+
+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={:}, with {num} samples)".format(
+            name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
+        )