Update sync codes

lib/datasets/__init__.py

@@ -3,3 +3,5 @@
 ##################################################
 from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
 from .SearchDatasetWrap import SearchDataset
+
+from .synthetic_adaptive_environment import SynAdaptiveEnv

lib/datasets/synthetic_adaptive_environment.py

@@ -0,0 +1,84 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import numpy as np
+from typing import Optional
+import torch.utils.data as data
+
+
+class SynAdaptiveEnv(data.Dataset):
+    """The synethtic dataset for adaptive environment."""
+
+    def __init__(
+        self,
+        max_num_phase: int = 100,
+        interval: float = 0.1,
+        max_scale: float = 4,
+        offset_scale: float = 1.5,
+        mode: Optional[str] = None,
+    ):
+
+        self._max_num_phase = max_num_phase
+        self._interval = interval
+
+        self._times = np.arange(0, np.pi * self._max_num_phase, self._interval)
+        xmin, xmax = self._times.min(), self._times.max()
+        self._inputs = []
+        self._total_num = len(self._times)
+        for i in range(self._total_num):
+            scale = (i + 1.0) / self._total_num * max_scale
+            sin_scale = (i + 1.0) / self._total_num * 0.7
+            sin_scale = -4 * (sin_scale - 0.5) ** 2 + 1
+            # scale = -(self._times[i] - (xmin - xmax) / 2) + max_scale
+            self._inputs.append(
+                np.sin(self._times[i] * sin_scale) * (offset_scale - scale)
+            )
+        self._inputs = np.array(self._inputs)
+        # Training Set 60%
+        num_of_train = int(self._total_num * 0.6)
+        # Validation Set 20%
+        num_of_valid = int(self._total_num * 0.2)
+        # Test Set 20%
+        num_of_set = self._total_num - num_of_train - num_of_valid
+        all_indexes = list(range(self._total_num))
+        if mode is None:
+            self._indexes = all_indexes
+        elif mode.lower() in ("train", "training"):
+            self._indexes = all_indexes[:num_of_train]
+        elif mode.lower() in ("valid", "validation"):
+            self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
+        elif mode.lower() in ("test", "testing"):
+            self._indexes = all_indexes[num_of_train + num_of_valid :]
+        else:
+            raise ValueError("Unkonwn mode of {:}".format(mode))
+        # transformation function
+        self._transform = None
+
+    def set_transform(self, fn):
+        self._transform = fn
+
+    def __iter__(self):
+        self._iter_num = 0
+        return self
+
+    def __next__(self):
+        if self._iter_num >= len(self):
+            raise StopIteration
+        self._iter_num += 1
+        return self.__getitem__(self._iter_num - 1)
+
+    def __getitem__(self, index):
+        assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
+        index = self._indexes[index]
+        value = float(self._inputs[index])
+        if self._transform is not None:
+            value = self._transform(value)
+        return index, float(self._times[index]), value
+
+    def __len__(self):
+        return len(self._indexes)
+
+    def __repr__(self):
+        return "{name}({cur_num:}/{total} elements)".format(
+            name=self.__class__.__name__, cur_num=self._total_num, total=len(self)
+        )