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from typing import List, Tuple, Union
import torch
import torch.nn as nn
class ConvBlockRes(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
momentum: float = 0.01,
):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
# self.shortcut:Optional[nn.Module] = None
if in_channels != out_channels:
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
def forward(self, x: torch.Tensor):
if not hasattr(self, "shortcut"):
return self.conv(x) + x
else:
return self.conv(x) + self.shortcut(x)
class Encoder(nn.Module):
def __init__(
self,
in_channels: int,
in_size: int,
n_encoders: int,
kernel_size: Tuple[int, int],
n_blocks: int,
out_channels=16,
momentum=0.01,
):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
for _ in range(self.n_encoders):
self.layers.append(
ResEncoderBlock(
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
)
)
in_channels = out_channels
out_channels *= 2
in_size //= 2
self.out_size = in_size
self.out_channel = out_channels
def __call__(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
return super().__call__(x)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
concat_tensors: List[torch.Tensor] = []
x = self.bn(x)
for layer in self.layers:
t, x = layer(x)
concat_tensors.append(t)
return x, concat_tensors
class ResEncoderBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, int],
n_blocks=1,
momentum=0.01,
):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.kernel_size = kernel_size
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
for _ in range(n_blocks - 1):
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
if self.kernel_size is not None:
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
def forward(
self,
x: torch.Tensor,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
for conv in self.conv:
x = conv(x)
if self.kernel_size is not None:
return x, self.pool(x)
return x
class Intermediate(nn.Module):
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.layers = nn.ModuleList()
self.layers.append(
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
)
for _ in range(n_inters - 1):
self.layers.append(
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
)
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
self.conv2 = nn.ModuleList()
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
for _ in range(n_blocks - 1):
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
def forward(self, x, concat_tensor):
x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for conv2 in self.conv2:
x = conv2(x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for _ in range(self.n_decoders):
out_channels = in_channels // 2
self.layers.append(
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
)
in_channels = out_channels
def forward(self, x: torch.Tensor, concat_tensors: List[torch.Tensor]):
for i, layer in enumerate(self.layers):
x = layer(x, concat_tensors[-1 - i])
return x
class DeepUnet(nn.Module):
def __init__(
self,
kernel_size: Tuple[int, int],
n_blocks: int,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(DeepUnet, self).__init__()
self.encoder = Encoder(
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
)
self.intermediate = Intermediate(
self.encoder.out_channel // 2,
self.encoder.out_channel,
inter_layers,
n_blocks,
)
self.decoder = Decoder(
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
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