From dc28cbe2b4ed77be92ee8b2b69a20689c3bf02a4 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Sun, 8 Nov 2020 14:54:44 +0100
Subject: new updates

---
 src/text_recognizer/networks/util.py | 83 ++++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 src/text_recognizer/networks/util.py

(limited to 'src/text_recognizer/networks/util.py')

diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py
new file mode 100644
index 0000000..b31e640
--- /dev/null
+++ b/src/text_recognizer/networks/util.py
@@ -0,0 +1,83 @@
+"""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 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()]
+
+
+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"]
+
+        # Initializes the network with trained weights.
+        backbone = backbone_(**network_args)
+        backbone.load_state_dict(weights)
+        if "freeze" in backbone_args and backbone_args["freeze"] is True:
+            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
-- 
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