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-rw-r--r--text_recognizer/network/convformer.py77
1 files changed, 77 insertions, 0 deletions
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
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+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]