Working bash scripts etc.

6 files changed, 55 insertions, 16 deletions

diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
new file mode 100644
index 0000000..00ad47e
--- /dev/null
+++ b/src/text_recognizer/networks/ctc.py
@@ -0,0 +1,10 @@
+"""Decodes the CTC output."""
+#
+# from typing import Tuple
+# import torch
+#
+#
+# def greedy_decoder(
+#     output, labels, label_length, blank_label, collapse_repeated=True
+# ) -> Tuple[torch.Tensor, torch.Tensor]:
+#     pass
diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py
index 2839a0c..cbc58fc 100644
--- a/src/text_recognizer/networks/lenet.py
+++ b/src/text_recognizer/networks/lenet.py
@@ -1,24 +1,16 @@
 """Defines the LeNet network."""
 from typing import Callable, Dict, Optional, Tuple
 
+from einops.layers.torch import Rearrange
 import torch
 from torch import nn
 
 
-class Flatten(nn.Module):
-    """Flattens a tensor."""
-
-    def forward(self, x: int) -> torch.Tensor:
-        """Flattens a tensor for input to a nn.Linear layer."""
-        return torch.flatten(x, start_dim=1)
-
-
 class LeNet(nn.Module):
     """LeNet network."""
 
     def __init__(
         self,
-        input_size: Tuple[int, ...] = (1, 28, 28),
         channels: Tuple[int, ...] = (1, 32, 64),
         kernel_sizes: Tuple[int, ...] = (3, 3, 2),
         hidden_size: Tuple[int, ...] = (9216, 128),
@@ -30,7 +22,6 @@ class LeNet(nn.Module):
         """The LeNet network.
 
         Args:
-            input_size (Tuple[int, ...]): The input shape of the network. Defaults to (1, 28, 28).
             channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
             kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
             hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
@@ -44,10 +35,9 @@ class LeNet(nn.Module):
         """
         super().__init__()
 
-        self._input_size = input_size
-
         if activation_fn is not None:
-            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+            activation_fn_args = activation_fn_args or {}
+            activation_fn = getattr(nn, activation_fn)(**activation_fn_args)
         else:
             activation_fn = nn.ReLU(inplace=True)
 
@@ -66,7 +56,7 @@ class LeNet(nn.Module):
             activation_fn,
             nn.MaxPool2d(kernel_sizes[2]),
             nn.Dropout(p=dropout_rate),
-            Flatten(),
+            Rearrange("b c h w -> b (c h w)"),
             nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
             activation_fn,
             nn.Dropout(p=dropout_rate),
@@ -77,6 +67,7 @@ class LeNet(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """The feedforward."""
+        # If batch dimenstion is missing, it needs to be added.
         if len(x.shape) == 3:
             x = x.unsqueeze(0)
         return self.layers(x)
diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/line_lstm_ctc.py
new file mode 100644
index 0000000..d704139
--- /dev/null
+++ b/src/text_recognizer/networks/line_lstm_ctc.py
@@ -0,0 +1,4 @@
+"""LSTM with CTC for handwritten text recognition within a line."""
+
+import torch
+from torch import nn
diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/misc.py
new file mode 100644
index 0000000..9440f9d
--- /dev/null
+++ b/src/text_recognizer/networks/misc.py
@@ -0,0 +1,28 @@
+"""Miscellaneous neural network functionality."""
+from typing import Tuple
+
+from einops import rearrange
+import torch
+from torch.nn import Unfold
+
+
+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 = Unfold(kernel_size=patch_size, stride=stride)
+    patches = unfold(images)
+    patches = rearrange(
+        patches, "b (h w) c -> b c h w", h=patch_size[0], w=patch_size[1]
+    )
+    return patches
diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py
index d704d99..ac2c825 100644
--- a/src/text_recognizer/networks/mlp.py
+++ b/src/text_recognizer/networks/mlp.py
@@ -1,6 +1,7 @@
 """Defines the MLP network."""
 from typing import Callable, Dict, List, Optional, Union
 
+from einops.layers.torch import Rearrange
 import torch
 from torch import nn
 
@@ -34,7 +35,8 @@ class MLP(nn.Module):
         super().__init__()
 
         if activation_fn is not None:
-            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+            activation_fn_args = activation_fn_args or {}
+            activation_fn = getattr(nn, activation_fn)(**activation_fn_args)
         else:
             activation_fn = nn.ReLU(inplace=True)
 
@@ -42,6 +44,7 @@ class MLP(nn.Module):
             hidden_size = [hidden_size] * num_layers
 
         self.layers = [
+            Rearrange("b c h w -> b (c h w)"),
             nn.Linear(in_features=input_size, out_features=hidden_size[0]),
             activation_fn,
         ]
@@ -63,7 +66,9 @@ class MLP(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """The feedforward."""
-        x = torch.flatten(x, start_dim=1)
+        # If batch dimenstion is missing, it needs to be added.
+        if len(x.shape) == 3:
+            x = x.unsqueeze(0)
         return self.layers(x)
 
     @property
diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py
new file mode 100644
index 0000000..23394b0
--- /dev/null
+++ b/src/text_recognizer/networks/residual_network.py
@@ -0,0 +1 @@
+"""Residual CNN."""