Remove axial encoder

1 files changed, 0 insertions, 79 deletions

diff --git a/text_recognizer/networks/transformer/axial_attention/utils.py b/text_recognizer/networks/transformer/axial_attention/utils.py
deleted file mode 100644
index 2f5bf7e..0000000
--- a/text_recognizer/networks/transformer/axial_attention/utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""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