Add new training loop with PyTorch Lightning, remove stale files

1 files changed, 1 insertions, 26 deletions

diff --git a/text_recognizer/networks/util.py b/text_recognizer/networks/util.py
index 131a6b4..d292680 100644
--- a/text_recognizer/networks/util.py
+++ b/text_recognizer/networks/util.py
@@ -1,38 +1,13 @@
 """Miscellaneous neural network functionality."""
 import importlib
 from pathlib import Path
-from typing import Dict, Tuple, Type
+from typing import Dict, Type
 
-from einops import rearrange
 from loguru import logger
 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 sliding 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(