diff options
Diffstat (limited to 'text_recognizer/networks/transformer')
4 files changed, 0 insertions, 205 deletions
diff --git a/text_recognizer/networks/transformer/axial_attention/__init__.py b/text_recognizer/networks/transformer/axial_attention/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/text_recognizer/networks/transformer/axial_attention/__init__.py +++ /dev/null diff --git a/text_recognizer/networks/transformer/axial_attention/encoder.py b/text_recognizer/networks/transformer/axial_attention/encoder.py deleted file mode 100644 index 3082bd6..0000000 --- a/text_recognizer/networks/transformer/axial_attention/encoder.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Axial transformer encoder.""" - -from typing import List - -import attr -from torch import nn, Tensor - -from text_recognizer.networks.transformer.axial_attention.self_attention import ( - SelfAttention, -) -from text_recognizer.networks.transformer.axial_attention.utils import ( - calculate_permutations, - PermuteToForm, - Sequential, -) -from text_recognizer.networks.transformer.norm import PreNorm - - -@attr.s(eq=False) -class AxialEncoder(nn.Module): - """Axial transfomer encoder.""" - - def __attrs_pre_init__(self) -> None: - super().__init__() - - shape: List[int] = attr.ib() - dim: int = attr.ib() - depth: int = attr.ib() - heads: int = attr.ib() - dim_head: int = attr.ib() - dim_index: int = attr.ib() - fn: nn.Sequential = attr.ib(init=False) - - def __attrs_post_init__(self) -> None: - self._build() - - def _build(self) -> None: - permutations = calculate_permutations(2, self.dim_index) - get_ff = lambda: nn.Sequential( - nn.LayerNorm([self.dim, *self.shape]), - nn.Conv2d( - in_channels=self.dim, - out_channels=4 * self.dim, - kernel_size=3, - padding=1, - ), - nn.Mish(inplace=True), - nn.Conv2d( - in_channels=4 * self.dim, - out_channels=self.dim, - kernel_size=3, - padding=1, - ), - ) - - layers = nn.ModuleList([]) - for _ in range(self.depth): - attns = nn.ModuleList( - [ - PermuteToForm( - permutation=permutation, - fn=PreNorm( - self.dim, - SelfAttention( - dim=self.dim, heads=self.heads, dim_head=self.dim_head - ), - ), - ) - for permutation in permutations - ] - ) - convs = nn.ModuleList([get_ff(), get_ff()]) - layers.append(attns) - layers.append(convs) - - self.fn = Sequential(layers) - - def forward(self, x: Tensor) -> Tensor: - """Applies fn to input.""" - return self.fn(x) diff --git a/text_recognizer/networks/transformer/axial_attention/self_attention.py b/text_recognizer/networks/transformer/axial_attention/self_attention.py deleted file mode 100644 index ba162be..0000000 --- a/text_recognizer/networks/transformer/axial_attention/self_attention.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Axial self attention module.""" - -import attr -import torch -from torch import nn -from torch import Tensor - - -@attr.s(eq=False) -class SelfAttention(nn.Module): - """Axial self attention module.""" - - def __attrs_pre_init__(self) -> None: - super().__init__() - - dim: int = attr.ib() - dim_head: int = attr.ib() - heads: int = attr.ib() - dim_hidden: int = attr.ib(init=False) - to_q: nn.Linear = attr.ib(init=False) - to_kv: nn.Linear = attr.ib(init=False) - to_out: nn.Linear = attr.ib(init=False) - - def __attrs_post_init__(self) -> None: - self.dim_hidden = self.heads * self.dim_head - self.to_q = nn.Linear(self.dim, self.dim_hidden, bias=False) - self.to_kv = nn.Linear(self.dim, 2 * self.dim_hidden, bias=False) - self.to_out = nn.Linear(self.dim_hidden, self.dim) - - def forward(self, x: Tensor) -> Tensor: - """Applies self attention.""" - q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1)) - b, _, d, h, e = *q.shape, self.heads, self.dim_head - - merge_heads = ( - lambda x: x.reshape(b, -1, h, e).transpose(1, 2).reshape(b * h, -1, e) - ) - q, k, v = map(merge_heads, (q, k, v)) - - energy = torch.einsum("bie,bje->bij", q, k) * (e ** -0.5) - energy = energy.softmax(dim=-1) - attn = torch.einsum("bij,bje->bie", energy, v) - - out = attn.reshape(b, h, -1, e).transpose(1, 2).reshape(b, -1, d) - return self.to_out(out) 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 534ef4e..0000000 --- a/text_recognizer/networks/transformer/axial_attention/utils.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Helper functions for axial attention.""" -from operator import itemgetter -from typing import Callable, List, Tuple - -import attr -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 - - -@attr.s(eq=False) -class PermuteToForm(nn.Module): - """Helper class for applying axial attention.""" - - def __attrs_pre_init__(self) -> None: - super().__init__() - - fn: Callable = attr.ib() - permutation: List[List[int]] = attr.ib() - inv_permutation: List[List[int]] = attr.ib(init=False) - - def __attrs_post_init__(self) -> None: - _, 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 |