Revert "fixed bug with only one score reported (#155)"

This reverts commit be0b9db.

deckard/base/model/art_pipeline.py

@@ -69,14 +69,14 @@ def __call__(self):
             device_type = "gpu" if torch.cuda.is_available() else "cpu"
             if device_type == "gpu":
                 logger.info("Using GPU")
+                logger.info("Model moved to GPU")
                 number_of_devices = torch.cuda.device_count()
                 num = randint(0, number_of_devices - 1)
                 device = torch.device(f"cuda:{num}")
                 if isinstance(data[0][0], np.ndarray):
                     data = [torch.from_numpy(d).to(device) for d in data]
                 data = [d.to(device) for d in data]
                 model.to(device)
-                logger.info(f"Model moved to GPU: {device}")
             else:
                 device = torch.device("cpu")
             model = TorchInitializer(

deckard/layers/optimise.py

@@ -316,8 +316,6 @@ def optimise(cfg: DictConfig) -> None:
         if raise_exception:
             raise e
         scores = fake_scores
-    if len(scores) == 1:
-        scores = scores[0]
     return scores
 
 

examples/power/conf/torch_cifar10.yaml

@@ -12,17 +12,17 @@ stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
-  - maximize
   - minimize
-  # - minimize
+  - minimize
+  - minimize
+  - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
+   - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
+   - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
@@ -51,6 +51,6 @@ hydra:
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
       ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
+      ++attack.init.batch_size: int(range(10,10000))
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}

examples/power/conf/torch_cifar100.yaml

@@ -13,17 +13,17 @@ stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
-  - maximize
   - minimize
-  # - minimize
+  - minimize
+  - minimize
+  - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
+   - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
+   - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
@@ -52,6 +52,6 @@ hydra:
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
       ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
+      ++attack.init.batch_size: int(range(10,10000))
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}

examples/power/conf/torch_mnist.yaml

@@ -13,17 +13,17 @@ stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
-  - maximize
   - minimize
-  # - minimize
+  - minimize
+  - minimize
+  - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
+   - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
+   - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
@@ -52,9 +52,10 @@ hydra:
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
       ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
+      ++attack.init.batch_size: int(range(10,10000))
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}
+
 #   launcher: REDIS QUEUE LAUNCHER
 #     # @package hydra.launcher
 #     _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher