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| -rw-r--r-- | src/text_recognizer/networks/unet.py | 134 |
1 files changed, 134 insertions, 0 deletions
diff --git a/src/text_recognizer/networks/unet.py b/src/text_recognizer/networks/unet.py new file mode 100644 index 0000000..eb4188b --- /dev/null +++ b/src/text_recognizer/networks/unet.py @@ -0,0 +1,134 @@ +"""UNet for segmentation.""" +from typing import List, Tuple + +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: int = 2 + ) -> None: + super().__init__() + self.conv_block = ConvBlock(channels, activation) + self.down_sampling = nn.MaxPool2d(pooling_kernel) + + def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: + """Return the convolutional block output and a down sampled tensor.""" + x = self.conv_block(x) + return self.down_sampling(x), 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: Tensor) -> Tensor: + """Apply the up sampling and convolutional block.""" + x = self.up_sampling(x) + 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, + depth: int = 4, + out_channels: int = 3, + activation: str = "relu", + pooling_kernel: int = 2, + scale_factor: int = 2, + ) -> None: + super().__init__() + channels = [base_channels * 2 ** i for i in range(depth)] + self.down_sampling_blocks = self._configure_down_sampling_blocks( + channels, activation, pooling_kernel + ) + self.up_sampling_blocks = self._configure_up_sampling_blocks( + channels, activation, scale_factor + ) + + def _configure_down_sampling_blocks( + self, channels: List[int], activation: str, pooling_kernel: int + ) -> nn.ModuleList: + return nn.ModuleList( + [ + DownSamplingBlock( + [channels[i], channels[i + 1], channels[i + 1]], + activation, + pooling_kernel, + ) + for i in range(len(channels)) + ] + ) + + def _configure_up_sampling_blocks( + self, + channels: List[int], + activation: str, + scale_factor: int, + ) -> nn.ModuleList: + return nn.ModuleList( + [ + UpSamplingBlock( + [channels[i], channels[i + 1], channels[i + 1]], + activation, + scale_factor, + ) + ] + for i in range(len(channels)) + ) + + def down_sampling(self, x: Tensor) -> List[Tensor]: + x_skips = [] + for block in self.down_sampling_blocks: + x, x_skip = block(x) + x_skips.append(x_skip) + return x, x_skips + + def up_sampling(self, x: Tensor, x_skips: List[Tensor]) -> Tensor: + pass |