Add new single photon model

src/models.py

@@ -102,6 +102,44 @@ class singlePhotonNet_251022(nn.Module):
         x = self.fc(x)
         return x
 
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class singlePhotonNet_260511(nn.Module):
+    def __init__(self):
+        super(singlePhotonNet_260511, self).__init__()
+        
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=3)
+        
+        self.fc = nn.Sequential(
+            nn.Linear(64, 2),
+        )
+        
+        self.weight_init()
+
+    def weight_init(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        c1 = F.relu(self.conv1(x))    # [B, 16, 3, 3]
+        c2 = F.relu(self.conv2(c1))   # [B, 32, 3, 3]
+        c3 = F.relu(self.conv3(c2))   # [B, 64, 1, 1]
+        
+        flat_feat = c3.view(c3.size(0), -1)  # [B, 64]
+        
+        coords = self.fc(flat_feat)          # [B, 2]
+        return coords
+
 class doublePhotonNet_250909(nn.Module):
     def __init__(self):
         super(doublePhotonNet_250909, self).__init__()