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|
"""Simple convolutional network."""
from typing import Tuple
from einops import rearrange
from torch import nn, Tensor
from text_recognizer.networks.transformer import AxialPositionalEmbedding
class Subsampler(nn.Module):
def __init__(
self,
channels: int,
dim: int,
depth: int,
height: int,
pixel_pos_embedding: AxialPositionalEmbedding,
dropout: float = 0.1,
) -> None:
super().__init__()
self.pixel_pos_embedding = pixel_pos_embedding
self.subsampler, self.projector = self._build(
channels, height, dim, depth, dropout
)
def _build(
self, channels: int, height: int, dim: int, depth: int, dropout: float
) -> Tuple[nn.Sequential, nn.Sequential]:
subsampler = []
for i in range(depth):
subsampler.append(
nn.Conv2d(
in_channels=1 if i == 0 else channels,
out_channels=channels,
kernel_size=3,
stride=2,
)
)
subsampler.append(nn.Mish(inplace=True))
projector = nn.Sequential(
nn.Linear(channels * height, dim), nn.Dropout(dropout)
)
return nn.Sequential(*subsampler), projector
def forward(self, x: Tensor) -> Tensor:
x = self.subsampler(x)
x = self.pixel_pos_embedding(x)
x = rearrange(x, "b c h w -> b w (c h)")
x = self.projector(x)
return x
|
