in memory dataset inherits from torch iterable dataset

ml4gw/dataloading/in_memory_dataset.py

@@ -7,7 +7,7 @@
 from ml4gw.utils.slicing import slice_kernels
 
 
-class InMemoryDataset:
+class InMemoryDataset(torch.utils.data.IterableDataset):
     """Dataset for iterating through in-memory multi-channel timeseries
 
     Dataset for arrays of timeseries data which can be stored
@@ -131,7 +131,6 @@ def __init__(
         self.batches_per_epoch = batches_per_epoch
         self.shuffle = shuffle
         self.coincident = coincident
-        self._i = self._idx = None
 
     @property
     def num_kernels(self) -> int:
@@ -156,8 +155,8 @@ def __len__(self) -> int:
             # support it
             num_kernels = self.num_kernels ** len(self.X)
             return (num_kernels - 1) // self.batch_size + 1
-
-    def __iter__(self):
+        
+    def init_indices(self):
         """
         Initialize arrays of indices we'll use to slice
         through X and y at iteration time. This helps by
@@ -204,36 +203,24 @@ def __iter__(self):
             # the simplest case: deteriminstic and coincident
             idx = torch.arange(num_kernels, device=device)
 
-        self._idx = idx
-        self._i = 0
-        return self
+        return idx
 
-    def __next__(
+    def __iter__(
         self,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-        if self._i is None or self._idx is None:
-            raise TypeError(
-                "Must initialize InMemoryDataset iteration "
-                "before calling __next__"
-            )
-
-        # check if we're out of batches, and if so
-        # make sure to reset before stopping
-        if self._i >= len(self):
-            self._i = self._idx = None
-            raise StopIteration
-
-        # slice the array of _indices_ we'll be using to
-        # slice our timeseries, and scale them by the stride
-        slc = slice(self._i * self.batch_size, (self._i + 1) * self.batch_size)
-        idx = self._idx[slc] * self.stride
-
-        # slice our timeseries
-        X = slice_kernels(self.X, idx, self.kernel_size)
-        if self.y is not None:
-            y = slice_kernels(self.y, idx, self.kernel_size)
-
-        self._i += 1
-        if self.y is not None:
-            return X, y
-        return X
+
+        indices = self.init_indices()
+        for i in range(len(self)):
+            # slice the array of _indices_ we'll be using to
+            # slice our timeseries, and scale them by the stride
+            slc = slice(i * self.batch_size, (i + 1) * self.batch_size)
+            idx = indices[slc] * self.stride
+
+            # slice our timeseries
+            X = slice_kernels(self.X, idx, self.kernel_size)
+            if self.y is not None:
+                y = slice_kernels(self.y, idx, self.kernel_size)
+
+            if self.y is not None:
+                yield X, y
+            yield X