code working with Torch=1.13 now

gp/robotgp.py

@@ -18,6 +18,7 @@ torch.set_printoptions(precision=4, threshold=5)
 import pyro
 import pdb
 
+import inspect
 
 class RobotGP(torch.nn.Module):
     def __init__(self, mc_handle, planner, meas_std=1e0, lsZ=5e0, _stime=None, parameters = {}):
@@ -35,7 +36,7 @@ class RobotGP(torch.nn.Module):
         self.update_searcher_paths = True
 
         self.grad_clamp_value = 1e50
-        self.grad_clamp = lambda grad: torch.clamp(grad, -self.grad_clamp_value, self.grad_clamp_value)
+        #self.grad_clamp = lambda grad: torch.clamp(grad, -self.grad_clamp_value, self.grad_clamp_value)
         # self.X_batch.tregister_hook(lambda grad: torch.clamp(grad, -self.grad_clamp, self.grad_clamp))
 
         self.init_Xstar()
@@ -83,13 +84,15 @@ class RobotGP(torch.nn.Module):
 
         self._stime = _stime if _stime is not None else time.time()
 
-    #!!Memory problem is here @_@. Hook is triggered by backward(forward too?)
+    #!!Memory problem is here @_@. This registers backwards hooks
     #What is this hook doing though. Why do we need it
     def _hook(self, tensor, _min=-1e10, _max=1e10):
-        print(tensor)
+        curframe = inspect.currentframe()
+        calframe = inspect.getouterframes(curframe,2)
+        print(calframe[1][3])
         tensor.requires_grad_(True)
         #tensor.retain_grad()
-        tensor.register_hook(lambda grad: grad.clamp_(min=_min, max=_max))
+        tensor.register_hook(lambda grad: grad.clamp(min=_min, max=_max))
         # tensor.register_hook(lambda grad: grad.clamp_(-self.grad_clamp_value, self.grad_clamp_value))
         return tensor
 
@@ -161,7 +164,6 @@ class RobotGP(torch.nn.Module):
         # self.robot_values = self._hook(self.robot_values)
 
         self.num_train = self.fixed_points.shape[0] + self.robot_points.shape[0]
-
         self.update_trainXY()
 
     def update_Xstar(self):
@@ -361,7 +363,8 @@ class RobotGP(torch.nn.Module):
 
         self.scaled_risk_cost = self.omega.sum() * self.riskcost_scaling
         self.scaled_path_length_cost = self.lengthcost_scaling * self.path_len_cost
-        self.risk_cost = self.scaled_risk_cost + self.scaled_path_length_cost
+        #self.risk_cost = self.scaled_risk_cost + self.scaled_path_length_cost
+        self.risk_cost = torch.add(self.scaled_risk_cost, self.scaled_path_length_cost)
         self.risk_cost = self._hook(self.risk_cost)
         print("!!Ran hook on Risk cost, why are we doing it again...\n\n")
         print(self.risk_cost)