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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):
"""Modified UNet convolutional block with dilation."""
def __init__(
self,
channels: List[int],
activation: str,
num_groups: int,
dropout_rate: float = 0.1,
kernel_size: int = 3,
dilation: int = 1,
padding: int = 0,
) -> None:
super().__init__()
self.channels = channels
self.dropout_rate = dropout_rate
self.kernel_size = kernel_size
self.dilation = dilation
self.padding = padding
self.num_groups = num_groups
self.activation = activation_function(activation)
self.block = self._configure_block()
self.residual_conv = nn.Sequential(
nn.Conv2d(
self.channels[0], self.channels[-1], kernel_size=3, stride=1, padding=1
),
self.activation,
)
def _configure_block(self) -> nn.Sequential:
block = []
for i in range(len(self.channels) - 1):
block += [
nn.Dropout(p=self.dropout_rate),
nn.GroupNorm(self.num_groups, self.channels[i]),
self.activation,
nn.Conv2d(
self.channels[i],
self.channels[i + 1],
kernel_size=self.kernel_size,
padding=self.padding,
stride=1,
dilation=self.dilation,
),
]
return nn.Sequential(*block)
def forward(self, x: Tensor) -> Tensor:
"""Apply the convolutional block."""
residual = self.residual_conv(x)
return self.block(x) + residual
class _DownSamplingBlock(nn.Module):
"""Basic down sampling block."""
def __init__(
self,
channels: List[int],
activation: str,
num_groups: int,
pooling_kernel: Union[int, bool] = 2,
dropout_rate: float = 0.1,
kernel_size: int = 3,
dilation: int = 1,
padding: int = 0,
) -> None:
super().__init__()
self.conv_block = _ConvBlock(
channels,
activation,
num_groups,
dropout_rate,
kernel_size,
dilation,
padding,
)
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)
x_down = self.down_sampling(x) if self.down_sampling is not None else x
return x_down, x
class _UpSamplingBlock(nn.Module):
"""The upsampling block of the UNet."""
def __init__(
self,
channels: List[int],
activation: str,
num_groups: int,
scale_factor: int = 2,
dropout_rate: float = 0.1,
kernel_size: int = 3,
dilation: int = 1,
padding: int = 0,
) -> None:
super().__init__()
self.conv_block = _ConvBlock(
channels,
activation,
num_groups,
dropout_rate,
kernel_size,
dilation,
padding,
)
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,
activation: str = "relu",
num_groups: int = 8,
dropout_rate: float = 0.1,
pooling_kernel: int = 2,
scale_factor: int = 2,
kernel_size: Optional[List[int]] = None,
dilation: Optional[List[int]] = None,
padding: Optional[List[int]] = None,
) -> None:
super().__init__()
self.depth = depth
self.num_groups = num_groups
if kernel_size is not None and dilation is not None and padding is not None:
if (
len(kernel_size) != depth
and len(dilation) != depth
and len(padding) != depth
):
raise RuntimeError(
"Length of convolutional parameters does not match the depth."
)
self.kernel_size = kernel_size
self.padding = padding
self.dilation = dilation
else:
self.kernel_size = [3] * depth
self.padding = [1] * depth
self.dilation = [1] * depth
self.dropout_rate = dropout_rate
self.conv = nn.Conv2d(
in_channels, base_channels, kernel_size=3, stride=1, padding=1
)
channels = [base_channels] + [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
dropout_rate = self.dropout_rate if i < 0 else 0
blocks += [
_DownSamplingBlock(
[channels[i], channels[i + 1], channels[i + 1]],
activation,
self.num_groups,
pooling_kernel,
dropout_rate,
self.kernel_size[i],
self.dilation[i],
self.padding[i],
)
]
return blocks
def _configure_up_sampling_blocks(
self, channels: List[int], activation: str, scale_factor: int,
) -> nn.ModuleList:
channels.reverse()
self.kernel_size.reverse()
self.dilation.reverse()
self.padding.reverse()
return nn.ModuleList(
[
_UpSamplingBlock(
[channels[i] + channels[i + 1], channels[i + 1], channels[i + 1]],
activation,
self.num_groups,
scale_factor,
self.dropout_rate,
self.kernel_size[i],
self.dilation[i],
self.padding[i],
)
for i in range(len(channels) - 2)
]
)
def _encode(self, x: Tensor) -> List[Tensor]:
x_skips = []
for block in self.encoder_blocks:
x, x_skip = block(x)
x_skips.append(x_skip)
return x_skips
def _decode(self, 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:
"""Forward pass with the UNet model."""
if len(x.shape) < 4:
x = x[(None,) * (4 - len(x.shape))]
x = self.conv(x)
x_skips = self._encode(x)
x = self._decode(x_skips)
return self.head(x)
|
