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from torch import Tensor, nn
from .decoder import Decoder
from .embedding.token import TokenEmbedding
from .vit import Vit
class Transformer(nn.Module):
def __init__(
self,
dim: int,
num_classes: int,
encoder: Vit,
decoder: Decoder,
token_embedding: TokenEmbedding,
tie_embeddings: bool,
pad_index: int,
) -> None:
super().__init__()
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, images: Tensor) -> Tensor:
return self.encoder(images)
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]
|
