Removing legacy code

1 files changed, 0 insertions, 255 deletions

diff --git a/text_recognizer/networks/unet.py b/text_recognizer/networks/unet.py
deleted file mode 100644
index 510910f..0000000
--- a/text_recognizer/networks/unet.py
+++ /dev/null
@@ -1,255 +0,0 @@
-"""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)