Add axial encoder

1 files changed, 79 insertions, 0 deletions

diff --git a/text_recognizer/networks/transformer/axial_attention/utils.py b/text_recognizer/networks/transformer/axial_attention/utils.py
new file mode 100644
index 0000000..2f5bf7e
--- /dev/null
+++ b/text_recognizer/networks/transformer/axial_attention/utils.py
@@ -0,0 +1,79 @@
+"""Helper functions for axial attention."""
+from operator import itemgetter
+from typing import Callable, List, Tuple
+
+from torch import nn, Tensor
+
+
+def _map_el_ind(arr: Tensor, ind: int) -> List:
+    return list(map(itemgetter(ind), arr))
+
+
+def _sort_indices(arr: Tensor) -> Tuple[List[int], List[int]]:
+    indices = [i for i in range(len(arr))]
+    arr = zip(arr, indices)
+    arr = sorted(arr)
+    return _map_el_ind(arr, 0), _map_el_ind(arr, 1)
+
+
+def calculate_permutations(num_dims: int, emb_dim: int) -> List[List[int]]:
+    """Returns permutations of tensor."""
+    total_dims = num_dims + 2
+    axial_dims = [i for i in range(1, total_dims) if i != emb_dim]
+
+    permutations = []
+
+    for axial_dim in axial_dims:
+        last_two_dims = [axial_dim, emb_dim]
+        dims_rest = set(range(0, total_dims)) - set(last_two_dims)
+        permutation = [*dims_rest, *last_two_dims]
+        permutations.append(permutation)
+
+    return permutations
+
+
+class PermuteToForm(nn.Module):
+    """Helper class for applying axial attention."""
+
+    def __init__(
+        self,
+        fn: Callable,
+        permutation: List[List[int]],
+    ) -> None:
+        super().__init__()
+
+        self.fn = fn
+        self.permutation = permutation
+        _, self.inv_permutation = _sort_indices(self.permutation)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Permutes tensor, applies axial attention, permutes tensor back."""
+        x = x.permute(*self.permutation).contiguous()
+        shape = x.shape
+        *_, t, d = shape
+
+        # Merge all but axial dimension
+        x = x.reshape(-1, t, d)
+
+        # Apply attention
+        x = self.fn(x)
+
+        # Restore original shape and permutation
+        x = x.reshape(*shape)
+        x = x.permute(*self.inv_permutation).contiguous()
+        return x
+
+
+class Sequential(nn.Module):
+    """Applies a list of paired functions to input."""
+
+    def __init__(self, fns: nn.ModuleList) -> None:
+        super().__init__()
+        self.fns = fns
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Applies blocks to input."""
+        for f, g in self.fns:
+            x = x + f(x)
+            x = x + g(x)
+        return x