From 3a42081d0f422ea441def27bbf6b9eb29cd3451f Mon Sep 17 00:00:00 2001
From: aktersnurra <grydholm@kth.se>
Date: Sun, 15 Nov 2020 20:46:14 +0100
Subject: Working on UNet.

---
 src/text_recognizer/networks/unet.py | 134 +++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 src/text_recognizer/networks/unet.py

(limited to 'src')

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
-- 
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