Move mlp to ff

1 files changed, 0 insertions, 46 deletions

diff --git a/text_recognizer/networks/transformer/mlp.py b/text_recognizer/networks/transformer/mlp.py
deleted file mode 100644
index 4028ab3..0000000
--- a/text_recognizer/networks/transformer/mlp.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Feedforward layer in transformer.
-
-Stolen from lucidrains:
-    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-"""
-from typing import Optional
-
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-
-class GEGLU(nn.Module):
-    def __init__(self, dim_in: int, dim_out: int) -> None:
-        super().__init__()
-        self.fc = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x: Tensor) -> Tensor:
-        x, gate = self.fc(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        expansion_factor: int = 4,
-        glu: bool = True,
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-        inner_dim = dim * expansion_factor
-        dim_out = dim_out if dim_out is not None else dim
-        in_projection = (
-            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
-            if not glu
-            else GEGLU(dim, inner_dim)
-        )
-
-        self.mlp = nn.Sequential(
-            in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.mlp(x)