1 files changed, 0 insertions, 45 deletions
diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/misc.py
deleted file mode 100644
index 1f853e9..0000000
--- a/src/text_recognizer/networks/misc.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Miscellaneous neural network functionality."""
-from typing import Tuple, Type
-
-from einops import rearrange
-import torch
-from torch import nn
-
-
-def sliding_window(
-    images: torch.Tensor, patch_size: Tuple[int, int], stride: Tuple[int, int]
-) -> torch.Tensor:
-    """Creates patches of an image.
-
-    Args:
-        images (torch.Tensor): A Torch tensor of a 4D image(s), i.e. (batch, channel, height, width).
-        patch_size (Tuple[int, int]): The size of the patches to generate, e.g. 28x28 for EMNIST.
-        stride (Tuple[int, int]): The stride of the sliding window.
-
-    Returns:
-        torch.Tensor: A tensor with the shape (batch, patches, height, width).
-
-    """
-    unfold = nn.Unfold(kernel_size=patch_size, stride=stride)
-    # Preform the slidning window, unsqueeze as the channel dimesion is lost.
-    c = images.shape[1]
-    patches = unfold(images)
-    patches = rearrange(
-        patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1]
-    )
-    return patches
-
-
-def activation_function(activation: str) -> Type[nn.Module]:
-    """Returns the callable activation function."""
-    activation_fns = nn.ModuleDict(
-        [
-            ["elu", nn.ELU(inplace=True)],
-            ["gelu", nn.GELU()],
-            ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
-            ["none", nn.Identity()],
-            ["relu", nn.ReLU(inplace=True)],
-            ["selu", nn.SELU(inplace=True)],
-        ]
-    )
-    return activation_fns[activation.lower()]