Move to xautodl

xautodl/datasets/synthetic_utils.py

@@ -0,0 +1,93 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import math
+import abc
+import numpy as np
+from typing import Optional
+import torch
+import torch.utils.data as data
+
+
+class UnifiedSplit:
+    """A class to unify the split strategy."""
+
+    def __init__(self, total_num, mode):
+        # Training Set 60%
+        num_of_train = int(total_num * 0.6)
+        # Validation Set 20%
+        num_of_valid = int(total_num * 0.2)
+        # Test Set 20%
+        num_of_set = total_num - num_of_train - num_of_valid
+        all_indexes = list(range(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))
+        self._all_indexes = all_indexes
+        self._mode = mode
+
+    @property
+    def mode(self):
+        return self._mode
+
+
+class TimeStamp(UnifiedSplit, data.Dataset):
+    """The timestamp dataset."""
+
+    def __init__(
+        self,
+        min_timestamp: float = 0.0,
+        max_timestamp: float = 1.0,
+        num: int = 100,
+        mode: Optional[str] = None,
+    ):
+        self._min_timestamp = min_timestamp
+        self._max_timestamp = max_timestamp
+        self._interval = (max_timestamp - min_timestamp) / (float(num) - 1)
+        self._total_num = num
+        UnifiedSplit.__init__(self, self._total_num, mode)
+
+    @property
+    def min_timestamp(self):
+        return self._min_timestamp + self._interval * min(self._indexes)
+
+    @property
+    def max_timestamp(self):
+        return self._min_timestamp + self._interval * max(self._indexes)
+
+    @property
+    def interval(self):
+        return self._interval
+
+    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]
+        timestamp = self._min_timestamp + self._interval * index
+        return index, timestamp
+
+    def __len__(self):
+        return len(self._indexes)
+
+    def __repr__(self):
+        return "{name}({cur_num:}/{total} elements)".format(
+            name=self.__class__.__name__,
+            cur_num=len(self),
+            total=self._total_num,
+        )