Add models

src/editor/models/__init__.py

@@ -1 +1,19 @@
-from .create_model import create_model
+from .v1 import HistogramRestorationNet as v1
+from .simple_cnn import SimpleCNN
+from .residual import Residual
+from .normalised_cnn import NormalisedCNN
+from .smart_res import SmartRes
+from .attention_net import AttentionNet
+from .res2 import Res2
+
+
+def create_model(type: str, bin_count: int):
+    return {
+        # "v1": v1,
+        "SimpleCNN": SimpleCNN,
+        "Residual": Residual,
+        "NormalisedCNN": NormalisedCNN,
+        "SmartRes": SmartRes,
+        "AttentionNet": AttentionNet,
+        "Res2": Res2,
+    }[type](bin_count)

src/editor/models/attention_net.py

@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+
+
+# Define the self-attention module
+class SelfAttention(nn.Module):
+    def __init__(self, channels):
+        super(SelfAttention, self).__init__()
+        self.query_conv = nn.Conv3d(channels, channels // 8, kernel_size=1)
+        self.key_conv = nn.Conv3d(channels, channels // 8, kernel_size=1)
+        self.value_conv = nn.Conv3d(channels, channels, kernel_size=1)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        batch_size, channels, depth, height, width = x.size()
+        query = (
+            self.query_conv(x)
+            .view(batch_size, -1, depth * height * width)
+            .permute(0, 2, 1)
+        )
+        key = self.key_conv(x).view(batch_size, -1, depth * height * width)
+        value = self.value_conv(x).view(batch_size, -1, depth * height * width)
+
+        attention = self.softmax(torch.bmm(query, key))  # Batch matrix multiplication
+        out = torch.bmm(value, attention.permute(0, 2, 1))
+        out = out.view(batch_size, channels, depth, height, width)
+
+        return x + out
+
+
+# Define the residual block
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super(ResidualBlock, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv3d(channels, channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(channels),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(channels, channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(channels),
+        )
+        self.attention = SelfAttention(channels)
+
+    def forward(self, x):
+        return self.attention(self.conv(x)) + x
+
+
+# Define the network
+class AttentionNet(nn.Module):
+    def __init__(self, bin_count):
+        super(AttentionNet, self).__init__()
+        self.input_layer = nn.Sequential(
+            nn.Conv3d(1, 16, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm3d(16),
+        )
+        self.res_blocks = nn.Sequential(ResidualBlock(16), ResidualBlock(16))
+        self.output_layer = nn.Conv3d(16, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.res_blocks(x)
+        x = self.output_layer(x)
+        return x

src/editor/models/create_model.py

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

src/editor/models/normalised_cnn.py

@@ -0,0 +1,34 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class NormalisedCNN(nn.Module):
+    def __init__(self, bin_count):
+        super(NormalisedCNN, self).__init__()
+        self.bin_count = bin_count
+
+        # Define the layers of the neural network
+        self.conv1 = nn.Conv3d(1, 16, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm3d(16)
+        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm3d(32)
+        self.conv3 = nn.Conv3d(32, 64, kernel_size=3, padding=1)
+        self.bn3 = nn.BatchNorm3d(64)
+        self.conv4 = nn.Conv3d(64, 32, kernel_size=3, padding=1)
+        self.bn4 = nn.BatchNorm3d(32)
+        self.conv5 = nn.Conv3d(32, 16, kernel_size=3, padding=1)
+        self.bn5 = nn.BatchNorm3d(16)
+        self.conv6 = nn.Conv3d(16, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = x.view(
+            -1, 1, self.bin_count, self.bin_count, self.bin_count
+        )  # Reshape input to (N, C, D, H, W)
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        x = F.relu(self.bn4(self.conv4(x)))
+        x = F.relu(self.bn5(self.conv5(x)))
+        x = self.conv6(x)
+
+        return x

src/editor/models/res2.py

@@ -0,0 +1,37 @@
+import torch.nn as nn
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super(ResidualBlock, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv3d(channels, channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(channels),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(channels, channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(channels),
+        )
+
+    def forward(self, x):
+        return self.conv(x) + x
+
+
+# Define the network
+class Res2(nn.Module):
+    def __init__(self, bin_count):
+        super(Res2, self).__init__()
+        self.input_layer = nn.Sequential(
+            nn.Conv3d(1, 16, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm3d(16),
+        )
+        self.res_blocks = nn.Sequential(
+            ResidualBlock(16), ResidualBlock(16), ResidualBlock(16), ResidualBlock(16)
+        )
+        self.output_layer = nn.Conv3d(16, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.res_blocks(x)
+        x = self.output_layer(x)
+        return x

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

src/editor/models/simple_cnn.py

@@ -0,0 +1,37 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SimpleCNN(nn.Module):
+    def __init__(self, bin_count):
+        super(SimpleCNN, self).__init__()
+        self.bin_count = bin_count
+
+        # Define the convolutional layers
+        self.conv1 = nn.Conv3d(
+            1, 16, kernel_size=3, padding=1
+        )  # input channels = 1, output channels = 16
+        self.conv2 = nn.Conv3d(
+            16, 32, kernel_size=3, padding=1
+        )  # input channels = 16, output channels = 32
+        self.conv3 = nn.Conv3d(
+            32, 64, kernel_size=3, padding=1
+        )  # input channels = 32, output channels = 64
+        self.conv4 = nn.Conv3d(
+            64, 32, kernel_size=3, padding=1
+        )  # input channels = 64, output channels = 32
+        self.conv5 = nn.Conv3d(
+            32, 16, kernel_size=3, padding=1
+        )  # input channels = 32, output channels = 16
+        self.conv6 = nn.Conv3d(
+            16, 1, kernel_size=3, padding=1
+        )  # input channels = 16, output channels = 1
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = F.relu(self.conv4(x))
+        x = F.relu(self.conv5(x))
+        x = self.conv6(x)
+        return x

src/editor/models/smart_res.py

@@ -0,0 +1,42 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = nn.Conv3d(channels, channels, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm3d(channels)
+        self.conv2 = nn.Conv3d(channels, channels, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm3d(channels)
+
+    def forward(self, x):
+        identity = x
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += identity
+        return F.relu(out)
+
+
+class SmartRes(nn.Module):
+    def __init__(self, bin_count):
+        super(SmartRes, self).__init__()
+        self.bin_count = bin_count
+        self.initial_conv = nn.Conv3d(1, 16, kernel_size=3, padding=1)
+        self.bn0 = nn.BatchNorm3d(16)
+        self.resblock1 = ResidualBlock(16)
+        self.resblock2 = ResidualBlock(16)
+        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm3d(32)
+        self.dilated_conv = nn.Conv3d(32, 32, kernel_size=3, padding=2, dilation=2)
+        self.bn_dilated = nn.BatchNorm3d(32)
+        self.final_conv = nn.Conv3d(32, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = F.relu(self.bn0(self.initial_conv(x)))
+        x = self.resblock1(x)
+        x = self.resblock2(x)
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn_dilated(self.dilated_conv(x)))
+        x = self.final_conv(x)
+        return x