dataset stretch now optional

_paramters.py

@@ -22,19 +22,19 @@ main_arg_parser.add_argument("--data_worker", type=int, default=11, help="")
 main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
 main_arg_parser.add_argument("--data_class_name", type=str, default='BinaryMasksDataset', help="")
 main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=False, help="")
 main_arg_parser.add_argument("--data_n_mels", type=int, default=64, help="")
 main_arg_parser.add_argument("--data_sr", type=int, default=16000, help="")
 main_arg_parser.add_argument("--data_hop_length", type=int, default=256, help="")
 main_arg_parser.add_argument("--data_n_fft", type=int, default=512, help="")
 main_arg_parser.add_argument("--data_mixup", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--data_stretch", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, help="")
 
 # Transformation Parameters
-main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="")  # 0.4
+main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0.4, help="")  # 0.4
-main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0, help="")  # 0.3
+main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="")  # 0.3
-main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="")  # 0.4
+main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0.4, help="")  # 0.4
-main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="")  # 0.2
+main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0.2, help="")  # 0.2
 main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0.3, help="")  # 0.3
 main_arg_parser.add_argument("--data_speed_factor", type=float, default=0.7, help="")  # 0.7
 

datasets/binar_masks.py

@@ -19,7 +19,9 @@ class BinaryMasksDataset(Dataset):
     def sample_shape(self):
         return self[0][0].shape
 
-    def __init__(self, data_root, setting, mel_transforms, transforms=None, mixup=False, stretch_dataset=False):
+    def __init__(self, data_root, setting, mel_transforms, transforms=None, mixup=False, stretch_dataset=False,
+                 use_preprocessed=True):
+        self.use_preprocessed = use_preprocessed
         self.stretch = stretch_dataset
         assert isinstance(setting, str), f'Setting has to be a string, but was: {type(setting)}.'
         assert setting in V.DATA_OPTIONS, f'Setting must match one of: {V.DATA_OPTIONS}.'
@@ -28,12 +30,13 @@ class BinaryMasksDataset(Dataset):
         self.data_root = Path(data_root)
         self.setting = setting
         self.mixup = mixup
+        self._wav_folder = self.data_root / 'wav'
+        self._mel_folder = self.data_root / 'mel'
         self.container_ext = '.pik'
         self._mel_transform = mel_transforms
+
         self._labels = self._build_labels()
-        self._wav_folder = self.data_root / 'wav'
         self._wav_files = list(sorted(self._labels.keys()))
-        self._mel_folder = self.data_root / 'mel'
         self._transforms = transforms or F_x(in_shape=None)
 
     def _build_labels(self):
@@ -51,18 +54,25 @@ class BinaryMasksDataset(Dataset):
             additional_dict.update({f'X_X_{key}': val for key, val in labeldict.items()})
             additional_dict.update({f'X_X_X_{key}': val for key, val in labeldict.items()})
             labeldict.update(additional_dict)
+
+        # Delete File if one exists.
+        if not self.use_preprocessed:
+            for key in labeldict.keys():
+                (self._mel_folder / (key.replace('.wav', '') + self.container_ext)).unlink(missing_ok=True)
         return labeldict
 
     def __len__(self):
         return len(self._labels) * 2 if self.mixup else len(self._labels)
 
     def _compute_or_retrieve(self, filename):
+
         if not (self._mel_folder / (filename + self.container_ext)).exists():
             raw_sample, sr = librosa.core.load(self._wav_folder / (filename.replace('X_', '') + '.wav'))
             mel_sample = self._mel_transform(raw_sample)
             self._mel_folder.mkdir(exist_ok=True, parents=True)
             with (self._mel_folder / (filename + self.container_ext)).open(mode='wb') as f:
                 pickle.dump(mel_sample, f, protocol=pickle.HIGHEST_PROTOCOL)
+
         with (self._mel_folder / (filename + self.container_ext)).open(mode='rb') as f:
             mel_sample = pickle.load(f, fix_imports=True)
         return mel_sample

models/residual_conv_classifier.py

@@ -41,13 +41,11 @@ class ResidualConvClassifier(BinaryMaskDatasetMixin,
         last_shape = self.conv_list[-1].shape
         for idx in range(len(self.conv_filters)):
             conv_module_params.update(conv_filters=self.conv_filters[idx])
-            self.conv_list.append(ResidualModule(last_shape, ConvModule, 3, **conv_module_params))
+            self.conv_list.append(ResidualModule(last_shape, ConvModule, 2, **conv_module_params))
             last_shape = self.conv_list[-1].shape
             try:
                 self.conv_list.append(ConvModule(last_shape, self.conv_filters[idx+1], (k, k), conv_stride=(2, 2), conv_padding=2,
                                                  **self.params.module_kwargs))
-                for param in self.conv_list[-1].parameters():
-                    param.requires_grad = False
                 last_shape = self.conv_list[-1].shape
             except IndexError:
                 pass

util/module_mixins.py

@@ -142,6 +142,7 @@ class BinaryMaskDatasetMixin:
             **dict(
                 # TRAIN DATASET
                 train_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.train,
+                                                 use_preprocessed=self.params.use_preprocessed,
                                                  mixup=self.params.mixup, stretch_dataset=self.params.stretch,
                                                  mel_transforms=mel_transforms_train, transforms=aug_transforms),
                 # VALIDATION DATASET