1 files changed, 0 insertions, 89 deletions
diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py
deleted file mode 100644
index 131a6b4..0000000
--- a/src/text_recognizer/networks/util.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""Miscellaneous neural network functionality."""
-import importlib
-from pathlib import Path
-from typing import Dict, Tuple, 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(
-        [
-            ["elu", nn.ELU(inplace=True)],
-            ["gelu", nn.GELU()],
-            ["glu", nn.GLU()],
-            ["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()]
-
-
-def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
-    """Loads a backbone network."""
-    network_module = importlib.import_module("text_recognizer.networks")
-    backbone_ = getattr(network_module, backbone)
-
-    if "pretrained" in backbone_args:
-        logger.info("Loading pretrained backbone.")
-        checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop(
-            "pretrained"
-        )
-
-        # Loading state directory.
-        state_dict = torch.load(checkpoint_file)
-        network_args = state_dict["network_args"]
-        weights = state_dict["model_state"]
-
-        freeze = False
-        if "freeze" in backbone_args and backbone_args["freeze"] is True:
-            backbone_args.pop("freeze")
-            freeze = True
-        network_args = backbone_args
-
-        # Initializes the network with trained weights.
-        backbone = backbone_(**network_args)
-        backbone.load_state_dict(weights)
-        if freeze:
-            for params in backbone.parameters():
-                params.requires_grad = False
-    else:
-        backbone_ = getattr(network_module, backbone)
-        backbone = backbone_(**backbone_args)
-
-    if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
-        backbone = nn.Sequential(
-            *list(backbone.children())[:][: -backbone_args["remove_layers"]]
-        )
-
-    return backbone