Commentaires

higher/smart_aug/higher_patch.py

@@ -1,21 +1,31 @@
+""" Patch for Higher package
+    
+    Recommended use ::
+    
+    import higher
+    import higher_patch
 
+    Might become unnecessary with future update of the Higher package.
+"""
 import higher
 import torch as _torch
 
 def detach_(self):
-        """Removes all params from their compute graph in place."""
-        # detach param groups
-        for group in self.param_groups:
-            for k, v in group.items():
-                if isinstance(v,_torch.Tensor):
-                    v.detach_().requires_grad_()
+    """Removes all params from their compute graph in place.
 
-        # detach state
-        for state_dict in self.state:
-            for k,v_dict in state_dict.items():
-                if isinstance(k,_torch.Tensor): k.detach_().requires_grad_()
-                for k2,v2 in v_dict.items():
-                    if isinstance(v2,_torch.Tensor):
-                        v2.detach_().requires_grad_()
+    """
+    # detach param groups
+    for group in self.param_groups:
+        for k, v in group.items():
+            if isinstance(v,_torch.Tensor):
+                v.detach_().requires_grad_()
+
+    # detach state
+    for state_dict in self.state:
+        for k,v_dict in state_dict.items():
+            if isinstance(k,_torch.Tensor): k.detach_().requires_grad_()
+            for k2,v2 in v_dict.items():
+                if isinstance(v2,_torch.Tensor):
+                    v2.detach_().requires_grad_()
 
 higher.optim.DifferentiableOptimizer.detach_ = detach_

higher/smart_aug/train_utils.py

@@ -254,6 +254,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
     for epoch in range(1, epochs+1):
         t0 = time.perf_counter()
        
+        #Cross-Validation
         #dl_train, dl_val = cvs.next_split()
         #dl_val_it = iter(dl_val)