paper preperations and notebooks, optuna callbacks

audio_toolset/audio_io.py

@@ -52,6 +52,10 @@ class NormalizeLocal(object):
         return f'{self.__class__.__name__}({self.__dict__})'
 
     def __call__(self, x: np.ndarray):
+
+        x[np.isnan(x)] = 0
+        x[np.isinf(x)] = 0
+
         mean = x.mean()
         std = x.std() + 0.0001
 

modules/model_parts.py

@@ -190,10 +190,6 @@ class BaseCNNEncoder(ShapeMixin, nn.Module):
         kernels = kernels if not isinstance(kernels, int) else [kernels] * len(filters)
         assert len(kernels) == len(filters), 'Length of "Filters" and "Kernels" has to be same.'
 
-        # Optional Padding for odd image-sizes
-        # Obsolet, cdan be done by autopadding module on incoming tensors
-        # in_shape = [tensor+1 if tensor % 2 != 0 and idx else tensor for idx, tensor in enumerate(in_shape)]
-
         # Parameters
         self.lat_dim = lat_dim
         self.in_shape = in_shape

modules/util.py

@@ -14,6 +14,8 @@ from sklearn.metrics import ConfusionMatrixDisplay
 
 # Utility - Modules
 ###################
+from ..metrics.binary_class_classifictaion import BinaryScores
+from ..metrics.multi_class_classification import MultiClassScores
 from ..utils.model_io import ModelParameters
 from ..utils.tools import add_argparse_args
 
@@ -133,9 +135,6 @@ try:
         def size(self):
             return self.shape
 
-        def additional_scores(self, outputs):
-            raise NotImplementedError
-
         def save_to_disk(self, model_path):
             Path(model_path, exist_ok=True).mkdir(parents=True, exist_ok=True)
             if not (model_path / 'model_class.obj').exists():
@@ -174,6 +173,12 @@ try:
             weight_initializer = WeightInit(in_place_init_function=self._weight_init)
             self.apply(weight_initializer)
 
+        def additional_scores(self, outputs):
+            if self.params.n_classes > 2:
+                return MultiClassScores(self)(outputs)
+            else:
+                return BinaryScores(self)(outputs)
+
     module_types = (LightningBaseModule, nn.Module,)
 
 except ImportError:

utils/callbacks.py

@@ -1,3 +1,4 @@
+import torch
 from pytorch_lightning import Callback, Trainer, LightningModule
 
 
@@ -17,6 +18,10 @@ class BestScoresCallback(Callback):
             current_score = trainer.callback_metrics.get(monitor)
             if current_score is None:
                 pass
+            elif torch.isinf(current_score):
+                pass
+            elif torch.isnan(current_score):
+                pass
             else:
                 self.best_scores[monitor] = max(self.best_scores[monitor], current_score)
                 if self.best_scores[monitor] == current_score:

utils/config.py

@@ -37,12 +37,6 @@ def parse_comandline_args_add_defaults(filepath, overrides=None):
         defaults = config[key]
         new_defaults.update({key: auto_cast(val) for key, val in defaults.items()})
 
-    if new_defaults['debug']:
-        new_defaults.update(
-            max_epochs=2,
-            max_steps=2     # The seems to be the new "fast_dev_run"
-        )
-
     args, _ = parser.parse_known_args()
     overrides = overrides or dict()
     default_data = overrides.get('data_name', None) or new_defaults['data_name']
@@ -71,13 +65,20 @@ def parse_comandline_args_add_defaults(filepath, overrides=None):
     args.update(gpus=[0] if torch.cuda.is_available() and not args['debug'] else None,
                 row_log_interval=1000,  # TODO: Better Value / Setting
                 log_save_interval=10000,  # TODO: Better Value / Setting
-                auto_lr_find=not args['debug'],
                 weights_summary='top',
-                check_val_every_n_epoch=1 if args['debug'] else args.get('check_val_every_n_epoch', 1),
                 )
 
     if overrides is not None and isinstance(overrides, (Mapping, Dict)):
         args.update(**overrides)
+    if args['debug']:
+        args.update(
+            # The seems to be the new "fast_dev_run"
+            val_check_interval=1,
+            max_epochs=2,
+            max_steps=2,
+            auto_lr_find=False,
+            check_val_every_n_epoch=1
+        )
     return args, found_data_class, found_model_class, found_seed