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"""UNet for segmentation."""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
class ConvBlock(nn.Module):
"""Basic UNet convolutional block."""
def __init__(self, channels: List[int], activation: str) -> None:
super().__init__()
self.channels = channels
self.activation = activation_function(activation)
self.block = self._configure_block()
def _configure_block(self) -> nn.Sequential:
block = []
for i in range(len(self.channels) - 1):
block += [
nn.Conv2d(
self.channels[i], self.channels[i + 1], kernel_size=3, padding=1
),
nn.BatchNorm2d(self.channels[i + 1]),
self.activation,
]
return nn.Sequential(*block)
def forward(self, x: Tensor) -> Tensor:
"""Apply the convolutional block."""
return self.block(x)
class DownSamplingBlock(nn.Module):
"""Basic down sampling block."""
def __init__(
self,
channels: List[int],
activation: str,
pooling_kernel: Union[int, bool] = 2,
) -> None:
super().__init__()
self.conv_block = ConvBlock(channels, activation)
self.down_sampling = nn.MaxPool2d(pooling_kernel) if pooling_kernel else None
def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Return the convolutional block output and a down sampled tensor."""
x = self.conv_block(x)
if self.down_sampling is not None:
x_down = self.down_sampling(x)
else:
x_down = None
return x_down, x
class UpSamplingBlock(nn.Module):
"""The upsampling block of the UNet."""
def __init__(
self, channels: List[int], activation: str, scale_factor: int = 2
) -> None:
super().__init__()
self.conv_block = ConvBlock(channels, activation)
self.up_sampling = nn.Upsample(
scale_factor=scale_factor, mode="bilinear", align_corners=True
)
def forward(self, x: Tensor, x_skip: Optional[Tensor] = None) -> Tensor:
"""Apply the up sampling and convolutional block."""
x = self.up_sampling(x)
if x_skip is not None:
x = torch.cat((x, x_skip), dim=1)
return self.conv_block(x)
class UNet(nn.Module):
"""UNet architecture."""
def __init__(
self,
in_channels: int = 1,
base_channels: int = 64,
num_classes: int = 3,
depth: int = 4,
out_channels: int = 3,
activation: str = "relu",
pooling_kernel: int = 2,
scale_factor: int = 2,
) -> None:
super().__init__()
self.depth = depth
channels = [1] + [base_channels * 2 ** i for i in range(depth)]
self.encoder_blocks = self._configure_down_sampling_blocks(
channels, activation, pooling_kernel
)
self.decoder_blocks = self._configure_up_sampling_blocks(
channels, activation, scale_factor
)
self.head = nn.Conv2d(base_channels, num_classes, kernel_size=1)
def _configure_down_sampling_blocks(
self, channels: List[int], activation: str, pooling_kernel: int
) -> nn.ModuleList:
blocks = nn.ModuleList([])
for i in range(len(channels) - 1):
pooling_kernel = pooling_kernel if i < self.depth - 1 else False
blocks += [
DownSamplingBlock(
[channels[i], channels[i + 1], channels[i + 1]],
activation,
pooling_kernel,
)
]
return blocks
def _configure_up_sampling_blocks(
self,
channels: List[int],
activation: str,
scale_factor: int,
) -> nn.ModuleList:
channels.reverse()
return nn.ModuleList(
[
UpSamplingBlock(
[channels[i] + channels[i + 1], channels[i + 1], channels[i + 1]],
activation,
scale_factor,
)
for i in range(len(channels) - 2)
]
)
def encode(self, x: Tensor) -> Tuple[Tensor, List[Tensor]]:
x_skips = []
for block in self.encoder_blocks:
x, x_skip = block(x)
if x_skip is not None:
x_skips.append(x_skip)
return x, x_skips
def decode(self, x: Tensor, x_skips: List[Tensor]) -> Tensor:
x = x_skips[-1]
for i, block in enumerate(self.decoder_blocks):
x = block(x, x_skips[-(i + 2)])
return x
def forward(self, x: Tensor) -> Tensor:
x, x_skips = self.encode(x)
x = self.decode(x, x_skips)
return self.head(x)
|
