From 143d37636c4533a74c558ca5afb8a579af38de97 Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Tue, 13 Sep 2022 18:46:09 +0200 Subject: Remove axial encoder --- .../networks/transformer/axial_attention/utils.py | 79 ---------------------- 1 file changed, 79 deletions(-) delete mode 100644 text_recognizer/networks/transformer/axial_attention/utils.py (limited to 'text_recognizer/networks/transformer/axial_attention/utils.py') 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 -- cgit v1.2.3-70-g09d2