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-rw-r--r--text_recognizer/networks/transformer/vit.py46
1 files changed, 46 insertions, 0 deletions
diff --git a/text_recognizer/networks/transformer/vit.py b/text_recognizer/networks/transformer/vit.py
index e69de29..ab331f8 100644
--- a/text_recognizer/networks/transformer/vit.py
+++ 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