IAM datasets implemented.

1 files changed, 44 insertions, 0 deletions

diff --git a/src/text_recognizer/networks/stn.py b/src/text_recognizer/networks/stn.py
new file mode 100644
index 0000000..b031128
--- /dev/null
+++ b/src/text_recognizer/networks/stn.py
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+"""Spatial Transformer Network."""
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+class SpatialTransformerNetwork(nn.Module):
+    """A network with differentiable attention.
+
+    Network that learns how to perform spatial transformations on the input image in order to enhance the
+    geometric invariance of the model.
+
+    # TODO: add arguements to make it more general.
+
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        # Initialize the identity transformation and its weights and biases.
+        linear = nn.Linear(32, 3 * 2)
+        linear.weight.data.zero_()
+        linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
+
+        self.theta = nn.Sequential(
+            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=7),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=8, out_channels=10, kernel_size=5),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.ReLU(inplace=True),
+            Rearrange("b c h w -> b (c h w)", h=3, w=3),
+            nn.Linear(in_features=10 * 3 * 3, out_features=32),
+            nn.ReLU(inplace=True),
+            linear,
+            Rearrange("b (row col) -> b row col", row=2, col=3),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """The spatial transformation."""
+        grid = F.affine_grid(self.theta(x), x.shape)
+        return F.grid_sample(x, grid, align_corners=False)