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architecture.py

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+
+import torch
+import torch.nn as nn
+
+class ResidualBlock(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.ln = nn.LayerNorm(dim)
+        self.fc = nn.Sequential(
+            nn.Linear(dim, dim),
+            nn.GELU(),
+            nn.Linear(dim, dim)
+        )
+    def forward(self, x):
+        return x + self.fc(self.ln(x))
+
+class SADIM_V2_Expert(nn.Module):
+    def __init__(self, input_dim=12, latent_dim=64):
+        super().__init__()
+        h = 3584 
+        self.encoder = nn.Sequential(
+            nn.Linear(input_dim, h),
+            nn.LayerNorm(h),
+            nn.GELU(),
+            nn.Linear(h, h),
+            nn.LayerNorm(h),
+            nn.GELU(),
+            ResidualBlock(h)
+        )
+        self.fc_mu = nn.Linear(h, latent_dim)
+        self.fc_logvar = nn.Linear(h, latent_dim)
+        self.decoder = nn.Sequential(
+            nn.Linear(latent_dim, h),
+            nn.GELU(),
+            nn.Linear(h, h),
+            nn.LayerNorm(h),
+            nn.GELU(),
+            ResidualBlock(h),
+            nn.Linear(h, input_dim)
+        )
+
+    def forward(self, x):
+        h = self.encoder(x)
+        mu, logvar = self.fc_mu(h), self.fc_logvar(h)
+        logvar = torch.clamp(logvar, -10, 10)
+        std = torch.exp(0.5 * logvar)
+        z = mu + torch.randn_like(mu) * std
+        return self.decoder(z), mu, logvar
+
+# --- مذكرة المعالجة المسبقة (Preprocessing Guide) ---
+# للحصول على نتائج صحيحة غداً، استخدم هذه القسمة:
+# ra, l: /360.0 | dec, b: /90.0 | d_pc: /10000.0 
+# pmra, pmdec: /500.0 | x, y, z: /15000.0