Add models

src/editor/models/residual.py

@@ -0,0 +1,52 @@
+import torch.nn as nn
+
+
+class Residual(nn.Module):
+    def __init__(self, bin_count: int):
+        super(Residual, self).__init__()
+        self.bin_count = bin_count
+
+        # Assuming the input histograms are 3D tensors of shape (bin_count, bin_count, bin_count)
+        # Convolutional layers to extract features from the histograms
+        self.conv1 = nn.Conv3d(1, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv3d(32, 64, kernel_size=3, padding=1)
+
+        # Batch normalization layers for better convergence
+        self.bn1 = nn.BatchNorm3d(16)
+        self.bn2 = nn.BatchNorm3d(32)
+        self.bn3 = nn.BatchNorm3d(64)
+
+        # Residual block to help the network learn identity functions effectively
+        self.resblock1 = nn.Sequential(
+            nn.Conv3d(64, 64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm3d(64),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(64, 64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm3d(64),
+        )
+
+        # ReLU activation
+        self.relu = nn.ReLU(inplace=True)
+
+        self.deconv1 = nn.ConvTranspose3d(64, 32, kernel_size=3, stride=1, padding=1)
+        self.deconv2 = nn.ConvTranspose3d(32, 16, kernel_size=3, stride=1, padding=1)
+        self.deconv3 = nn.ConvTranspose3d(16, 1, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        x = self.relu(self.bn1(self.conv1(x)))
+        x = self.relu(self.bn2(self.conv2(x)))
+        x = self.relu(self.bn3(self.conv3(x)))
+
+        # Apply residual blocks
+        residual = x
+        out = self.resblock1(x)
+        out += residual
+        out = self.relu(out)
+
+        # Upsample to original size
+        out = self.relu(self.deconv1(out))
+        out = self.relu(self.deconv2(out))
+        out = self.relu(self.deconv3(out))
+
+        return out