Remove nystromer

1 files changed, 0 insertions, 64 deletions

diff --git a/text_recognizer/networks/transformer/nystromer/nystromer.py b/text_recognizer/networks/transformer/nystromer/nystromer.py
deleted file mode 100644
index 2113f1f..0000000
--- a/text_recognizer/networks/transformer/nystromer/nystromer.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""Nyströmer encoder.
-
-Stolen from:
-    https://github.com/lucidrains/nystrom-attention/blob/main/nystrom_attention/nystrom_attention.py
-
-"""
-from typing import Optional
-
-from torch import nn, Tensor
-
-from text_recognizer.networks.transformer.mlp import FeedForward
-from text_recognizer.networks.transformer.norm import PreNorm
-from text_recognizer.networks.transformer.nystromer.attention import NystromAttention
-
-
-class Nystromer(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim: int,
-        depth: int,
-        dim_head: int = 64,
-        num_heads: int = 8,
-        num_landmarks: int = 256,
-        inverse_iter: int = 6,
-        residual: bool = True,
-        residual_conv_kernel: int = 33,
-        dropout_rate: float = 0.0,
-        glu: bool = True,
-    ) -> None:
-        super().__init__()
-        self.dim = dim
-        self.layers = nn.ModuleList(
-            [
-                nn.ModuleList(
-                    [
-                        PreNorm(
-                            dim,
-                            NystromAttention(
-                                dim=dim,
-                                dim_head=dim_head,
-                                num_heads=num_heads,
-                                num_landmarks=num_landmarks,
-                                inverse_iter=inverse_iter,
-                                residual=residual,
-                                residual_conv_kernel=residual_conv_kernel,
-                                dropout_rate=dropout_rate,
-                            ),
-                        ),
-                        PreNorm(
-                            dim,
-                            FeedForward(dim=dim, glu=glu, dropout_rate=dropout_rate),
-                        ),
-                    ]
-                )
-                for _ in range(depth)
-            ]
-        )
-
-    def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        for attn, ff in self.layers:
-            x = attn(x, mask=mask) + x
-            x = ff(x) + x
-        return x