Upload random_torch.py

ant/pwm/random_torch.py

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+"""
+PyTorch WM models covering ALL CallModes
+"""
+
+import os
+import torch
+import torch.nn as nn
+
+OBS_DIM = 105
+ACTION_DIM = 8
+LATENT_DIM = 32
+HIDDEN_DIM = 256
+
+OUT_DIR = "weights_torch_signatures"
+os.makedirs(OUT_DIR, exist_ok=True)
+
+# ---------------- Encoder ----------------
+class Encoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(OBS_DIM, HIDDEN_DIM),
+            nn.ReLU(),
+            nn.Linear(HIDDEN_DIM, LATENT_DIM),
+        )
+
+    def forward(self, obs):
+        return self.net(obs)
+
+# ---------------- Positional / Kwargs ----------------
+class TransitionPositional(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(LATENT_DIM + ACTION_DIM, HIDDEN_DIM),
+            nn.ReLU(),
+            nn.Linear(HIDDEN_DIM, LATENT_DIM),
+        )
+
+    def forward(self, z, a):
+        return self.net(torch.cat([z, a], dim=-1))
+
+# ---------------- Tuple ----------------
+class TransitionTuple(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(LATENT_DIM + ACTION_DIM, HIDDEN_DIM),
+            nn.ReLU(),
+            nn.Linear(HIDDEN_DIM, LATENT_DIM),
+        )
+
+    def forward(self, inputs):
+        z, a = inputs
+        return self.net(torch.cat([z, a], dim=-1))
+
+# ---------------- Concat ----------------
+class TransitionConcat(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(LATENT_DIM + ACTION_DIM, HIDDEN_DIM),
+            nn.ReLU(),
+            nn.Linear(HIDDEN_DIM, LATENT_DIM),
+        )
+
+    def forward(self, za):
+        return self.net(za)
+
+def save(model, name):
+    torch.save(model.state_dict(), f"{OUT_DIR}/{name}.pth")
+
+save(Encoder(), "encoder")
+save(TransitionPositional(), "transition_positional")
+save(TransitionTuple(), "transition_tuple")
+save(TransitionConcat(), "transition_concat")
+
+print("✅ PyTorch models saved")