Implement batch splitter to split sampled data

Disentanglement cannot be processed on different subjects at the same time, we need to load `pr` subjects one by one. The batch splitter will return a pr-length list of tuples (with 2 dicts containing k-length lists of labels, conditions, view and k-length tensor of clip data, representing condition 1 and condition 2 respectively).

1 files changed, 32 insertions, 0 deletions

diff --git a/models/model.py b/models/model.py
new file mode 100644
index 0000000..369d6c2
--- /dev/null
+++ b/models/model.py
@@ -0,0 +1,32 @@
+from typing import List, Dict, Union, Tuple
+
+import torch
+from torch.utils.data.dataloader import default_collate
+
+
+class Model:
+    def __init__(
+            self,
+            batch_size: Tuple[int, int]
+    ):
+        (self.pr, self.k) = batch_size
+
+    def _batch_splitter(
+            self,
+            batch: List[Dict[str, Union[str, torch.Tensor]]]
+    ) -> List[Tuple[Dict[str, List[Union[str, torch.Tensor]]],
+                    Dict[str, List[Union[str, torch.Tensor]]]]]:
+        """
+        Disentanglement cannot be processed on different subjects at the
+        same time, we need to load `pr` subjects one by one. The batch
+        splitter will return a pr-length list of tuples (with 2 dicts
+        containing k-length lists of labels, conditions, view and
+        k-length tensor of clip data, representing condition 1 and
+        condition 2 respectively).
+        """
+        _batch = []
+        for i in range(0, self.pr * self.k * 2, self.k * 2):
+            _batch.append((default_collate(batch[i:i + self.k]),
+                           default_collate(batch[i + self.k:i + self.k * 2])))
+
+        return _batch