added model definitions

model/src/models/angle.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class AngleTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/contact.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class ContactTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=2)

model/src/models/distance.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class DistanceTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/framing.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class FramingTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=4)

model/src/models/fully_connected.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+import torch.nn as nn
+
+
+class FullyConnectedModel(nn.Module):
+    def __init__(self, num_out_features: int):
+        super().__init__()
+        # define layers
+        self.fc1 = nn.Linear(in_features=16*16*512, out_features=512)
+        self.af1 = nn.ReLU()
+        self.fc2 = nn.Linear(in_features=512, out_features=128)
+        self.af2 = nn.ReLU()
+        self.fc3 = nn.Linear(in_features=128, out_features=32)
+        self.af3 = nn.ReLU()
+        self.fc4 = nn.Linear(in_features=32, out_features=num_out_features)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.af1(x)
+        x = self.fc2(x)
+        x = self.af2(x)
+        x = self.fc3(x)
+        x = self.af3(x)
+        x = self.fc4(x)
+        return x

model/src/models/information_value.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class InformationValueTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/modality_color.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class ModalityColorTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/modality_depth.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class ModalityDepthTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/modality_lighting.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class ModalityLightingTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=3)

model/src/models/point_of_view.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class PointOfViewTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=2)

model/src/models/resnet18_head.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import torch.nn as nn
+import torchvision
+
+
+class ResNet18Head(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # copy out parts from ResNet18 with weights
+        resnet18 = torchvision.models.resnet18(
+            weights=torchvision.models.ResNet18_Weights.IMAGENET1K_V1,
+        )
+        # save relevant layers
+        self.conv1 = resnet18.conv1
+        self.bn1 = resnet18.bn1
+        self.relu = resnet18.relu
+        self.maxpool = resnet18.maxpool
+        self.layer1 = resnet18.layer1
+        self.layer2 = resnet18.layer2
+        self.layer3 = resnet18.layer3
+        self.layer4 = resnet18.layer4
+        self.avgpool = resnet18.avgpool
+        self.flat = nn.Flatten()    # size 512
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = self.flat(x)
+        return x

model/src/models/salience.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class SalienceTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=5)

model/src/models/visual_syntax.py

@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .fully_connected import FullyConnectedModel
+
+
+class VisualSyntaxTail(FullyConnectedModel):
+    def __init__(self):
+        super().__init__(num_out_features=18)