From 5bfb8ed2233ef18b3ef37b0e07888dfa0bb34aad Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 11 Sep 2023 22:14:15 +0200
Subject: Rename vit to convformer

---
 text_recognizer/network/convformer.py | 77 +++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 text_recognizer/network/convformer.py

diff --git a/text_recognizer/network/convformer.py b/text_recognizer/network/convformer.py
new file mode 100644
index 0000000..0ee5487
--- /dev/null
+++ b/text_recognizer/network/convformer.py
@@ -0,0 +1,77 @@
+from typing import Optional
+
+from einops.layers.torch import Rearrange
+from torch import Tensor, nn
+
+from text_recognizer.network.convnext.convnext import ConvNext
+from .transformer.embedding.token import TokenEmbedding
+from .transformer.embedding.sincos import sincos_2d
+from .transformer.decoder import Decoder
+from .transformer.encoder import Encoder
+
+
+class Convformer(nn.Module):
+    def __init__(
+        self,
+        image_height: int,
+        image_width: int,
+        patch_height: int,
+        patch_width: int,
+        dim: int,
+        num_classes: int,
+        encoder: Encoder,
+        decoder: Decoder,
+        token_embedding: TokenEmbedding,
+        tie_embeddings: bool,
+        pad_index: int,
+        stem: Optional[ConvNext] = None,
+        channels: int = 1,
+    ) -> None:
+        super().__init__()
+        patch_dim = patch_height * patch_width * channels
+        self.stem = stem if stem is not None else nn.Identity()
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange(
+                "b c (h ph) (w pw) -> b (h w) (ph pw c)",
+                ph=patch_height,
+                pw=patch_width,
+            ),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+        self.patch_embedding = sincos_2d(
+            h=image_height // patch_height, w=image_width // patch_width, dim=dim
+        )
+        self.token_embedding = token_embedding
+        self.to_logits = (
+            nn.Linear(dim, num_classes)
+            if not tie_embeddings
+            else lambda t: t @ self.token_embedding.to_embedding.weight.t()
+        )
+        self.encoder = encoder
+        self.decoder = decoder
+        self.pad_index = pad_index
+
+    def encode(self, img: Tensor) -> Tensor:
+        x = self.stem(img)
+        x = self.to_patch_embedding(x)
+        x += self.patch_embedding.to(img.device, dtype=img.dtype)
+        return self.encoder(x)
+
+    def decode(self, text: Tensor, img_features: Tensor) -> Tensor:
+        text = text.long()
+        mask = text != self.pad_index
+        tokens = self.token_embedding(text)
+        output = self.decoder(tokens, context=img_features, mask=mask)
+        return self.to_logits(output)
+
+    def forward(
+        self,
+        img: Tensor,
+        text: Tensor,
+    ) -> Tensor:
+        """Applies decoder block on input signals."""
+        img_features = self.encode(img)
+        logits = self.decode(text, img_features)
+        return logits  # [B, N, C]
-- 
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