From f0e006105b68a6e86a8c50f1a365fed0f65da460 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 4 Dec 2021 17:00:58 +0100
Subject: Revert "Remove ViT"

This reverts commit 3de4312d1796b1ee56d6467d36773df29a831e45.
---
 text_recognizer/networks/transformer/vit.py | 46 +++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 text_recognizer/networks/transformer/vit.py

(limited to 'text_recognizer/networks/transformer')

diff --git a/text_recognizer/networks/transformer/vit.py b/text_recognizer/networks/transformer/vit.py
new file mode 100644
index 0000000..ab331f8
--- /dev/null
+++ b/text_recognizer/networks/transformer/vit.py
@@ -0,0 +1,46 @@
+"""Vision Transformer."""
+from typing import Tuple, Type
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn, Tensor
+
+
+class ViT(nn.Module):
+    def __init__(
+        self,
+        image_size: Tuple[int, int],
+        patch_size: Tuple[int, int],
+        dim: int,
+        transformer: Type[nn.Module],
+        channels: int = 1,
+    ) -> None:
+        super().__init__()
+        img_height, img_width = image_size
+        patch_height, patch_width = patch_size
+        assert img_height % patch_height == 0
+        assert img_width % patch_width == 0
+
+        num_patches = (img_height // patch_height) * (img_width // patch_width)
+        patch_dim = channels * patch_height * patch_width
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange(
+                "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
+                p1=patch_height,
+                p2=patch_width,
+                c=channels,
+            ),
+            nn.Linear(patch_dim, dim),
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
+        self.transformer = transformer
+        self.norm = nn.LayerNorm(dim)
+
+    def forward(self, img: Tensor) -> Tensor:
+        x = self.to_patch_embedding(img)
+        _, n, _ = x.shape
+        x += self.pos_embedding[:, :n]
+        x = self.transformer(x)
+        return x
-- 
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