Inital commit for refactoring to lightning

1 files changed, 255 insertions, 0 deletions

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