1 files changed, 0 insertions, 44 deletions
diff --git a/text_recognizer/networks/stn.py b/text_recognizer/networks/stn.py
deleted file mode 100644
index e9d216f..0000000
--- a/text_recognizer/networks/stn.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""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 arguments 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)