Model Training

_paramters.py

@@ -0,0 +1,56 @@
+from argparse import ArgumentParser, Namespace
+from distutils.util import strtobool
+from pathlib import Path
+
+import os
+
+# Parameter Configuration
+# =============================================================================
+# Argument Parser
+
+_ROOT = Path(__file__).parent
+
+main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
+
+# Main Parameters
+main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
+
+# Data Parameters
+main_arg_parser.add_argument("--data_worker", type=int, default=10, 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="")
+
+# Transformation Parameters
+main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
+
+# Training Parameters
+main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
+main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
+main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
+main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
+main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
+
+# Model Parameters
+main_arg_parser.add_argument("--model_type", type=str, default="BinaryClassifier", help="")
+main_arg_parser.add_argument("--model_weight_init", type=str, default="xavier_normal_", help="")
+main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
+main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
+main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
+main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
+main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--model_norm", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
+
+# Project Parameters
+main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.name, help="")
+main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
+main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
+
+if __name__ == '__main__':
+    # Parse it
+    args: Namespace = main_arg_parser.parse_args()

datasets/__init__.py

@@ -0,0 +1 @@
+from datasets.binar_masks import BinaryMasksDataset

datasets/binar_masks.py

@@ -4,6 +4,7 @@ from pathlib import Path
 
 import librosa as librosa
 from torch.utils.data import Dataset
+import torch
 
 import variables as V
 from ml_lib.modules.utils import F_x
@@ -11,18 +12,16 @@ from ml_lib.modules.utils import F_x
 
 class BinaryMasksDataset(Dataset):
     _to_label = defaultdict(lambda: -1)
-    _to_label['clear'] = V.CLEAR
-    _to_label['mask'] = V.MASK
-    settings = ['test', 'devel', 'train']
+    _to_label.update(dict(clear=V.CLEAR, mask=V.MASK))
 
     @property
     def sample_shape(self):
         return self[0][0].shape
 
     def __init__(self, data_root, setting, transforms=None):
-        assert isinstance(setting, str), f'Setting has to be a string, but was: {self.settings}.'
-        assert setting in self.settings, f'Setting must match one of: {self.settings}.'
-        assert callable(transforms) or None, f'Transforms has to be callable, but was: {transforms}'
+        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}.'
+        assert callable(transforms) or None, f'Transforms has to be callable, but was: {type(transforms)}'
         super(BinaryMasksDataset, self).__init__()
 
         self.data_root = Path(data_root)
@@ -41,7 +40,7 @@ class BinaryMasksDataset(Dataset):
             for row in f:
                 if self.setting not in row:
                     continue
-                filename, label = row.split(',')
+                filename, label = row.strip().split(',')
                 labeldict[filename] = self._to_label[label.lower()]
         return labeldict
 
@@ -60,5 +59,5 @@ class BinaryMasksDataset(Dataset):
                 pickle.dump(transformed_sample, f, protocol=pickle.HIGHEST_PROTOCOL)
         with (self._mel_folder / filename).open(mode='rb') as f:
             sample = pickle.load(f, fix_imports=True)
-        label = self._labels[key]
+        label = torch.as_tensor(self._labels[key], dtype=torch.float)
         return sample, label

main.py

@@ -1,81 +1,28 @@
 # Imports
 # =============================================================================
-import os
-from distutils.util import strtobool
-from pathlib import Path
-from argparse import ArgumentParser, Namespace
 
 import warnings
 
 import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from torch.utils.data import DataLoader
-from torchvision.transforms import Compose, ToTensor
 
-from ml_lib.audio_toolset.audio_io import Melspectogram, NormalizeLocal, AutoPadToShape
 from ml_lib.modules.utils import LightningBaseModule
-from ml_lib.utils.logging import Logger
+
+# Project Specific Config and Logger SubClasses
 from util.config import MConfig
+from util.logging import MLogger
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
 
-_ROOT = Path(__file__).parent
-
-# Parameter Configuration
-# =============================================================================
-# Argument Parser
-main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
-
-# Main Parameters
-main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
-
-# Data Parameters
-main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
-main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
-main_arg_parser.add_argument("--data_root", type=str, default='data', 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="")
-
-# Transformation Parameters
-main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
-
-# Training Parameters
-main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
-main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
-main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
-main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
-main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
-main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
-
-# Model Parameters
-main_arg_parser.add_argument("--model_type", type=str, default="BinaryClassifier", help="")
-main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
-main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
-main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
-main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
-main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--model_norm", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
-
-# Project Parameters
-main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.parent.name, help="")
-main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
-main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
-
-# Parse it
-args: Namespace = main_arg_parser.parse_args()
-
 
 def run_lightning_loop(config_obj):
 
     # Logging
     # ================================================================================
     # Logger
-    with Logger(config_obj) as logger:
+    with MLogger(config_obj) as logger:
         # Callbacks
         # =============================================================================
         # Checkpoint Saving
@@ -93,24 +40,10 @@ def run_lightning_loop(config_obj):
             patience=0,
         )
 
-        # Dataset and Dataloaders
-        # =============================================================================
-        # Transforms
-        transforms = Compose([Melspectogram(), ToTensor(), NormalizeLocal()])
-        # Datasets
-        from datasets.binar_masks import BinaryMasksDataset
-        train_dataset = BinaryMasksDataset(config_obj.data.root, setting='train', transforms=transforms)
-        val_dataset = BinaryMasksDataset(config_obj.data.root, setting='devel', transforms=transforms)
-        # Dataloaders
-        train_dataloader = DataLoader(train_dataset)
-        val_dataloader = DataLoader(val_dataset)
-
         # Model
         # =============================================================================
         # Build and Init its Weights
-        config_obj.set('model', 'in_shape', str(tuple(train_dataset.sample_shape)))
-        model: LightningBaseModule = config_obj.build_and_init_model(weight_init_function=torch.nn.init.xavier_normal_
-                                                                     )
+        model: LightningBaseModule = config_obj.build_and_init_model()
 
         # Trainer
         # =============================================================================
@@ -129,7 +62,7 @@ def run_lightning_loop(config_obj):
                           )
 
         # Train It
-        trainer.fit(model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader)
+        trainer.fit(model)
 
         # Save the last state & all parameters
         trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
@@ -144,5 +77,7 @@ def run_lightning_loop(config_obj):
 
 if __name__ == "__main__":
 
-    config = MConfig.read_namespace(args)
+    from _paramters import main_arg_parser
+
+    config = MConfig.read_argparser(main_arg_parser)
     trained_model = run_lightning_loop(config)

main_inference.py

@@ -0,0 +1,44 @@
+from torch.utils.data import DataLoader, Dataset
+from torchvision.transforms import Compose, ToTensor
+
+from ml_lib.audio_toolset.audio_io import Melspectogram, NormalizeLocal
+
+# Dataset and Dataloaders
+# =============================================================================
+
+# Transforms
+from ml_lib.utils.model_io import SavedLightningModels
+from util.config import MConfig
+from util.logging import MLogger
+
+transforms = Compose([Melspectogram(), ToTensor(), NormalizeLocal()])
+
+# Datasets
+from datasets.binar_masks import BinaryMasksDataset
+
+
+def prepare_dataset(config_obj):
+    dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='test', transforms=transforms)
+    return DataLoader(dataset=dataset,
+                      batch_size=None,
+                      worker=config_obj.data.worker,
+                      shuffle=False)
+
+
+def restore_logger_and_model(config_obj):
+    logger = MLogger(config_obj)
+    model = SavedLightningModels().load_checkpoint(models_root_path=logger.log_dir)
+    model = model.restore()
+
+    return model
+
+
+if __name__ == '__main__':
+    from _paramters import main_arg_parser
+
+    config = MConfig().read_argparser(main_arg_parser)
+    test_dataset = prepare_dataset(config)
+    loaded_model = restore_logger_and_model(config)
+    print("run model here and find a format to store the output")
+
+

models/binary_classifier.py

@@ -1,24 +1,23 @@
+from argparse import Namespace
+
 import torch
 from torch import nn
 from torch.optim import Adam
+from torchvision.transforms import Compose, ToTensor
 
+from ml_lib.audio_toolset.audio_io import Melspectogram, NormalizeLocal
 from ml_lib.modules.blocks import ConvModule
-from ml_lib.modules.utils import LightningBaseModule, Flatten
+from ml_lib.modules.utils import LightningBaseModule, Flatten, BaseModuleMixin_Dataloaders
 
 
-class BinaryClassifier(LightningBaseModule):
-    def test_step(self, *args, **kwargs):
-        pass
-
-    def test_epoch_end(self, outputs):
-        pass
+class BinaryClassifier(BaseModuleMixin_Dataloaders, LightningBaseModule):
 
     @classmethod
     def name(cls):
         return cls.__name__
 
     def configure_optimizers(self):
-        return Adam(params=self.parameters(), lr=self.hparams.lr)
+        return Adam(params=self.parameters(), lr=self.params.lr)
 
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
         batch_x, batch_y = batch_xy
@@ -26,11 +25,11 @@ class BinaryClassifier(LightningBaseModule):
         loss = self.criterion(y, batch_y)
         return dict(loss=loss)
 
-    def validation_step(self, batch_xy, **kwargs):
+    def validation_step(self, batch_xy, batch_idx, *args, **kwargs):
         batch_x, batch_y = batch_xy
-        y = self(batch_y)
+        y = self(batch_x)
         val_loss = self.criterion(y, batch_y)
-        return dict(val_loss=val_loss)
+        return dict(val_loss=val_loss, batch_idx=batch_idx)
 
     def validation_epoch_end(self, outputs):
         overall_val_loss = torch.mean(torch.stack([output['val_loss'] for output in outputs]))
@@ -41,22 +40,36 @@ class BinaryClassifier(LightningBaseModule):
     def __init__(self, hparams):
         super(BinaryClassifier, self).__init__(hparams)
 
-        self.criterion = nn.BCELoss()
+        # Dataset and Dataloaders
+        # =============================================================================
+        # Transforms
+        transforms = Compose([Melspectogram(), ToTensor(), NormalizeLocal()])
+        # Datasets
+        from datasets.binar_masks import BinaryMasksDataset
+        self.dataset = Namespace(
+            **dict(
+                train_dataset=BinaryMasksDataset(self.params.root, setting='train', transforms=transforms),
+                val_dataset=BinaryMasksDataset(self.params.root, setting='devel', transforms=transforms),
+                test_dataset=BinaryMasksDataset(self.params.root, setting='test', transforms=transforms),
+            )
+        )
 
+        # Model Paramters
+        # =============================================================================
         # Additional parameters
-        self.in_shape = self.hparams.in_shape
-
-        # Model Modules
-        self.conv_1 = ConvModule(self.in_shape, 32, 3, conv_stride=2, **self.hparams.module_paramters)
-        self.conv_2 = ConvModule(self.conv_1.shape, 64, 5, conv_stride=2, **self.hparams.module_paramters)
-        self.conv_3 = ConvModule(self.conv_2.shape, 128, 7, conv_stride=2, **self.hparams.module_paramters)
+        self.in_shape = self.dataset.train_dataset.sample_shape
+        self.criterion = nn.BCELoss()
+        # Modules
+        self.conv_1 = ConvModule(self.in_shape, 32, 3, conv_stride=2, **self.params.module_kwargs)
+        self.conv_2 = ConvModule(self.conv_1.shape, 64, 5, conv_stride=2, **self.params.module_kwargs)
+        self.conv_3 = ConvModule(self.conv_2.shape, 128, 7, conv_stride=2, **self.params.module_kwargs)
 
         self.flat = Flatten(self.conv_3.shape)
-        self.full_1 = nn.Linear(self.flat.shape, 32, self.hparams.bias)
-        self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2, self.hparams.bias)
+        self.full_1 = nn.Linear(self.flat.shape, 32, self.params.bias)
+        self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2, self.params.bias)
 
-        self.activation = self.hparams.activation()
-        self.full_out = nn.Linear(self.full_2.out_features, 1, self.hparams.bias)
+        self.activation = self.params.activation()
+        self.full_out = nn.Linear(self.full_2.out_features, 1, self.params.bias)
         self.sigmoid = nn.Sigmoid()
 
     def forward(self, batch, **kwargs):

util/config.py

@@ -5,5 +5,5 @@ from models.binary_classifier import BinaryClassifier
 class MConfig(Config):
 
     @property
-    def model_map(self):
+    def _model_map(self):
         return dict(BinaryClassifier=BinaryClassifier)

util/logging.py

@@ -0,0 +1,11 @@
+from pathlib import Path
+
+from ml_lib.utils.logging import Logger
+
+
+class MLogger(Logger):
+
+    @property
+    def outpath(self):
+        # FIXME: Specify a special path
+        return Path(self.config.train.outpath)

variables.py

@@ -1,3 +1,6 @@
 # Labels
 CLEAR = 0
 MASK = 1
+
+# Dataset Options
+DATA_OPTIONS = ['test', 'devel', 'train']