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"""Transformer module."""
from typing import Type
from einops.layers.torch import Rearrange
from torch import Tensor, nn
from text_recognizer.network.transformer.embedding.token import TokenEmbedding
from text_recognizer.network.transformer.embedding.sincos import sincos_2d
from text_recognizer.network.transformer.decoder import Decoder
from text_recognizer.network.transformer.encoder import Encoder
class VisionTransformer(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,
pos_embedding: Type[nn.Module],
tie_embeddings: bool,
pad_index: int,
) -> None:
super().__init__()
patch_dim = patch_height * patch_width
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.pos_embedding = pos_embedding
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.to_patch_embedding(img)
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()
# TODO: add mask to decoder
mask = text != self.pad_index
tokens = self.token_embedding(text)
tokens = tokens + self.pos_embedding(tokens)
output = self.decoder(tokens, context=img_features)
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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