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"""Vision transformer for character recognition."""
from typing import Type
import attr
from torch import nn, Tensor
@attr.s
class CnnTransformer(nn.Module):
def __attrs_pre_init__(self) -> None:
super().__init__()
# Parameters,
input_dims: Tuple[int, int, int] = attr.ib()
hidden_dim: int = attr.ib()
dropout_rate: float = attr.ib()
max_output_len: int = attr.ib()
num_classes: int = attr.ib()
# Modules.
encoder: Type[nn.Module] = attr.ib()
decoder: Type[nn.Module] = attr.ib()
embedding: nn.Embedding = attr.ib(init=False, default=None)
latent_encoder: nn.Sequential = attr.ib(init=False, default=None)
token_embedding: nn.Embedding = attr.ib(init=False, default=None)
token_pos_encoder: PositionalEncoding = attr.ib(init=False, default=None)
head: nn.Linear = attr.ib(init=False, default=None)
def __attrs_post_init__(self) -> None:
# Latent projector for down sampling number of filters and 2d
# positional encoding.
self.latent_encoder = nn.Sequential(
nn.Conv2d(
in_channels=self.encoder.out_channels,
out_channels=self.hidden_dim,
kernel_size=1,
),
PositionalEncoding2D(
hidden_dim=self.hidden_dim,
max_h=self.input_dims[1],
max_w=self.input_dims[2],
),
nn.Flatten(start_dim=2),
)
# Token embedding.
self.token_embedding = nn.Embedding(
num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
)
# Positional encoding for decoder tokens.
self.token_pos_encoder = PositionalEncoding(
hidden_dim=self.hidden_dim, dropout_rate=self.dropout_rate
)
# Head
self.head = nn.Linear(
in_features=self.hidden_dim, out_features=self.num_classes
)
# Initalize weights for encoder.
self.init_weights()
def init_weights(self) -> None:
"""Initalize weights for decoder network and head."""
bound = 0.1
self.token_embedding.weight.data.uniform_(-bound, bound)
self.head.bias.data.zero_()
self.head.weight.data.uniform_(-bound, bound)
# TODO: Initalize encoder?
def encode(self, x: Tensor) -> Tensor:
"""Encodes an image into a latent feature vector.
Args:
x (Tensor): Image tensor.
Shape:
- x: :math: `(B, C, H, W)`
- z: :math: `(B, Sx, E)`
where Sx is the length of the flattened feature maps projected from
the encoder. E latent dimension for each pixel in the projected
feature maps.
Returns:
Tensor: A Latent embedding of the image.
"""
z = self.encoder(x)
z = self.latent_encoder(z)
# Permute tensor from [B, E, Ho * Wo] to [Sx, B, E]
z = z.permute(2, 0, 1)
return z
def decode(self, z: Tensor, trg: Tensor) -> Tensor:
"""Decodes latent images embedding into word pieces.
Args:
z (Tensor): Latent images embedding.
trg (Tensor): Word embeddings.
Shapes:
- z: :math: `(B, Sx, E)`
- out: :math: `(B, Sy, T)`
where Sy is the length of the output and T is the number of tokens.
Returns:
Tensor: Sequence of word piece embeddings.
"""
pass
def forward(self, x: Tensor, trg: Tensor) -> Tensor:
"""Encodes images into word piece logtis.
Args:
x (Tensor): Input image(s).
trg (Tensor): Target word embeddings.
Shapes:
- x: :math: `(B, C, H, W)`
- trg: :math: `(B, Sy, T)`
where B is the batch size, C is the number of input channels, H is
the image height and W is the image width.
"""
z = self.encode(x)
y = self.decode(z, trg)
return y
def predict(self, x: Tensor) -> Tensor:
"""Predicts text in image."""
pass
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