Fix Error in nas SPOS trainer, apply_fixed_architecture

nni/algorithms/nas/pytorch/spos/trainer.py

@@ -63,6 +63,7 @@ def train_one_epoch(self, epoch):
         self.model.train()
         meters = AverageMeterGroup()
         for step, (x, y) in enumerate(self.train_loader):
+            x, y = x.to(self.device), y.to(self.device)
             self.optimizer.zero_grad()
             self.mutator.reset()
             logits = self.model(x)
@@ -82,6 +83,7 @@ def validate_one_epoch(self, epoch):
         meters = AverageMeterGroup()
         with torch.no_grad():
             for step, (x, y) in enumerate(self.valid_loader):
+                x, y = x.to(self.device), y.to(self.device)
                 self.mutator.reset()
                 logits = self.model(x)
                 loss = self.loss(logits, y)

nni/nas/pytorch/fixed.py

@@ -24,11 +24,14 @@ class FixedArchitecture(Mutator):
         Preloaded architecture object.
     strict : bool
         Force everything that appears in ``fixed_arc`` to be used at least once.
+    verbose : bool
+        Print log messages if set to True
     """
 
-    def __init__(self, model, fixed_arc, strict=True):
+    def __init__(self, model, fixed_arc, strict=True, verbose=True):
         super().__init__(model)
         self._fixed_arc = fixed_arc
+        self.verbose = verbose
 
         mutable_keys = set([mutable.key for mutable in self.mutables if not isinstance(mutable, MutableScope)])
         fixed_arc_keys = set(self._fixed_arc.keys())
@@ -99,10 +102,11 @@ def replace_layer_choice(self, module=None, prefix=""):
                 if sum(chosen) == 1 and max(chosen) == 1 and not mutable.return_mask:
                     # sum is one, max is one, there has to be an only one
                     # this is compatible with both integer arrays, boolean arrays and float arrays
-                    _logger.info("Replacing %s with candidate number %d.", global_name, chosen.index(1))
+                    if self.verbose:
+                        _logger.info("Replacing %s with candidate number %d.", global_name, chosen.index(1))
                     setattr(module, name, mutable[chosen.index(1)])
                 else:
-                    if mutable.return_mask:
+                    if mutable.return_mask and self.verbose:
                         _logger.info("`return_mask` flag of %s is true. As it relies on the behavior of LayerChoice, " \
                                      "LayerChoice will not be replaced.")
                     # remove unused parameters
@@ -113,7 +117,7 @@ def replace_layer_choice(self, module=None, prefix=""):
                 self.replace_layer_choice(mutable, global_name)
 
 
-def apply_fixed_architecture(model, fixed_arc):
+def apply_fixed_architecture(model, fixed_arc, verbose=True):
     """
     Load architecture from `fixed_arc` and apply to model.
 
@@ -123,6 +127,8 @@ def apply_fixed_architecture(model, fixed_arc):
         Model with mutables.
     fixed_arc : str or dict
         Path to the JSON that stores the architecture, or dict that stores the exported architecture.
+    verbose : bool
+        Print log messages if set to True
 
     Returns
     -------
@@ -133,7 +139,7 @@ def apply_fixed_architecture(model, fixed_arc):
     if isinstance(fixed_arc, str):
         with open(fixed_arc) as f:
             fixed_arc = json.load(f)
-    architecture = FixedArchitecture(model, fixed_arc)
+    architecture = FixedArchitecture(model, fixed_arc, verbose)
     architecture.reset()
 
     # for the convenience of parameters counting