Remove Barlow Twins

3 files changed, 0 insertions, 55 deletions

diff --git a/text_recognizer/networks/barlow_twins/__init__.py b/text_recognizer/networks/barlow_twins/__init__.py
deleted file mode 100644
index 0b74818..0000000
--- a/text_recognizer/networks/barlow_twins/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Module for projector network in Barlow Twins."""
diff --git a/text_recognizer/networks/barlow_twins/network.py b/text_recognizer/networks/barlow_twins/network.py
deleted file mode 100644
index a3e3750..0000000
--- a/text_recognizer/networks/barlow_twins/network.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Barlow Twins network."""
-from typing import Type
-
-from torch import nn, Tensor
-import torch.nn.functional as F
-
-
-class BarlowTwins(nn.Module):
-    def __init__(self, encoder: Type[nn.Module], projector: Type[nn.Module]) -> None:
-        super().__init__()
-        self.encoder = encoder
-        self.projector = projector
-
-    def forward(self, x: Tensor) -> Tensor:
-        z = self.encoder(x)
-        z_e = F.adaptive_avg_pool2d(z, (1, 1)).flatten(start_dim=1)
-        z_p = self.projector(z_e)
-        return z_p
diff --git a/text_recognizer/networks/barlow_twins/projector.py b/text_recognizer/networks/barlow_twins/projector.py
deleted file mode 100644
index 05d5e2e..0000000
--- a/text_recognizer/networks/barlow_twins/projector.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Projector network in Barlow Twins."""
-
-from typing import List
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class Projector(nn.Module):
-    """MLP network."""
-
-    def __init__(self, dims: List[int]) -> None:
-        super().__init__()
-        self.dims = dims
-        self.network = self._build()
-
-    def _build(self) -> nn.Sequential:
-        """Builds projector network."""
-        layers = [
-            nn.Sequential(
-                nn.Linear(
-                    in_features=self.dims[i], out_features=self.dims[i + 1], bias=False
-                ),
-                nn.BatchNorm1d(self.dims[i + 1]),
-                nn.ReLU(inplace=True),
-            )
-            for i in range(len(self.dims) - 2)
-        ]
-        layers.append(
-            nn.Linear(in_features=self.dims[-2], out_features=self.dims[-1], bias=False)
-        )
-        return nn.Sequential(*layers)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Project latent to higher dimesion."""
-        return self.network(x)