Extract model

src/editor/models/create_model.py

@@ -0,0 +1,5 @@
+from .v1 import HistogramRestorationNet as v1
+
+
+def create_model(type: str, bin_count: int):
+    return {"v1": v1}[type](bin_count)

src/editor/models/v1.py

@@ -0,0 +1,69 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+
+class HistogramRestorationNet(nn.Module):
+    def __init__(self, bin_count: int):
+        super(HistogramRestorationNet, self).__init__()
+
+        self.conv1 = nn.Conv3d(in_channels=1, out_channels=16, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm3d(16)
+        self.conv2 = nn.Conv3d(16, 32, 3, padding=1)
+        self.bn2 = nn.BatchNorm3d(32)
+        self.conv3 = nn.Conv3d(32, 64, 3, padding=1)
+        self.bn3 = nn.BatchNorm3d(64)
+
+        # Adjusted residual connections with proper downsampling and channel matching
+        self.res1 = nn.Sequential(
+            nn.Conv3d(16, 32, 1, stride=1, padding=0),  # Match channels
+            nn.BatchNorm3d(32),
+            nn.MaxPool3d(2),  # Downsample to match size
+        )
+        self.res2 = nn.Sequential(
+            nn.Conv3d(32, 64, 1, stride=1, padding=0),  # Match channels
+            nn.BatchNorm3d(64),
+            nn.MaxPool3d(2),  # Downsample to match size
+        )
+
+        self.fc1 = nn.Linear(64 * 4 * 4 * 4, 512)
+        self.fc_bn1 = nn.BatchNorm1d(512)
+        self.fc2 = nn.Linear(512, 32 * 32 * 32)
+        self.apply(HistogramRestorationNet._init_weights_he)
+
+    @staticmethod
+    def _init_weights_he(m):
+        if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d):
+            torch.nn.init.kaiming_uniform_(m.weight, nonlinearity="relu")
+            if m.bias is not None:
+                torch.nn.init.zeros_(m.bias)
+
+    def forward(self, x):
+        # Input dimensions: (batch_size, channels(1), 32, 32, 32)
+
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.max_pool3d(x, 2)
+
+        # Apply first adjusted residual connection
+        res = self.res1(x)
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.max_pool3d(x, 2)
+        x += res  # Add adjusted residual
+
+        # Apply second adjusted residual connection
+        res = self.res2(x)
+        x = F.relu(self.bn3(self.conv3(x)))
+        x = F.max_pool3d(x, 2)
+        x += res  # Add adjusted residual
+
+        # Flatten for fully connected layers
+        x = x.view(x.size(0), -1)
+
+        x = F.relu(self.fc_bn1(self.fc1(x)))
+        x = self.fc2(x)
+
+        # Reshape back to the histogram shape
+        x = x.view(-1, 32, 32, 32)
+        x /= torch.sum(x, (1, 2, 3)).view(x.size()[0], 1, 1, 1)
+
+        return x