UPDATE save parameters

src/itwinai/components.py

@@ -134,7 +134,7 @@ class BaseComponent(ABC, Serializable):
             name (Optional[str], optional): unique identifier for a step.
                 Defaults to None.
         """
-    _name: str = 'unnamed'
+    _name: str = None
     parameters: Dict[Any, Any] = None
 
     def __init__(
@@ -144,6 +144,7 @@ def __init__(
         # debug: bool = False,
     ) -> None:
         self.save_parameters(name=name)
+        self.name = name
 
     @property
     def name(self) -> str:
@@ -329,6 +330,8 @@ class Adapter(BaseComponent):
     def __init__(self, policy: List[Any], name: Optional[str] = None) -> None:
         super().__init__(name=name)
         self.save_parameters(policy=policy, name=name)
+        self.name = name
+        self.policy = policy
 
     @monitor_exec
     def execute(self, *args) -> Tuple:
@@ -370,15 +373,15 @@ def execute(self, *args) -> Tuple:
 
 class DataSplitter(BaseComponent):
     """Splits a dataset into train, validation, and test splits."""
-    train_proportion: float
-    validation_proportion: float
-    test_proportion: float
+    _train_proportion: Union[int, float]
+    _validation_proportion: Union[int, float]
+    _test_proportion: Union[int, float]
 
     def __init__(
         self,
-        train_proportion: float,
-        validation_proportion: float,
-        test_proportion: float,
+        train_proportion: Union[int, float],
+        validation_proportion: Union[int, float],
+        test_proportion: Union[int, float],
         name: Optional[str] = None
     ) -> None:
         super().__init__(name)
@@ -388,6 +391,51 @@ def __init__(
             test_proportion=test_proportion,
             name=name
         )
+        self.train_proportion = train_proportion
+        self.validation_proportion = validation_proportion
+        self.test_proportion = test_proportion
+
+    @property
+    def train_proportion(self) -> Union[int, float]:
+        """Training set proportion."""
+        return self._train_proportion
+
+    @train_proportion.setter
+    def train_proportion(self, prop: Union[int, float]) -> None:
+        if isinstance(prop, float) and not 0.0 <= prop <= 1.0:
+            raise ValueError(
+                "Train proportion should be in the interval [0.0, 1.0] "
+                f"if given as float. Received {prop}"
+            )
+        self._train_proportion = prop
+
+    @property
+    def validation_proportion(self) -> Union[int, float]:
+        """Validation set proportion."""
+        return self._validation_proportion
+
+    @validation_proportion.setter
+    def validation_proportion(self, prop: Union[int, float]) -> None:
+        if isinstance(prop, float) and not 0.0 <= prop <= 1.0:
+            raise ValueError(
+                "Validation proportion should be in the interval [0.0, 1.0] "
+                f"if given as float. Received {prop}"
+            )
+        self._validation_proportion = prop
+
+    @property
+    def test_proportion(self) -> Union[int, float]:
+        """Test set proportion."""
+        return self._ttest_proportion
+
+    @test_proportion.setter
+    def test_proportion(self, prop: Union[int, float]) -> None:
+        if isinstance(prop, float) and not 0.0 <= prop <= 1.0:
+            raise ValueError(
+                "Test proportion should be in the interval [0.0, 1.0] "
+                f"if given as float. Received {prop}"
+            )
+        self._test_proportion = prop
 
     @abstractmethod
     @monitor_exec

src/itwinai/serialization.py

@@ -44,6 +44,23 @@ def save_parameters(self, **kwargs) -> None:
         # for k, v in kwargs.items():
         #     self.__setattr__(k, v)
 
+    @staticmethod
+    def locals2params(locals: Dict, pop_self: bool = True) -> Dict:
+        """Remove ``self`` from the output of ``locals()``.
+
+        Args:
+            locals (Dict): output of ``locals()`` called in the constructor
+            of a class.
+            pop_self (bool, optional): whether to remove ``self``.
+            Defaults to True.
+
+        Returns:
+            Dict: cleaned ``locals()``.
+        """
+        if pop_self:
+            locals.pop('self', None)
+        return locals
+
     def update_parameters(self, **kwargs) -> None:
         """Updates stored parameters."""
         self.save_parameters(**kwargs)

src/itwinai/tensorflow/trainer.py

@@ -38,11 +38,7 @@ def __init__(
             strategy
     ):
         super().__init__()
-        self.save_parameters(
-            strategy=strategy, epochs=epochs, batch_size=batch_size,
-            callbacks=callbacks, model_dict=model_dict,
-            compile_conf=compile_conf, strategy=strategy
-        )
+        self.save_parameters(**self.locals2params(locals()))
         self.strategy = strategy
         self.epochs = epochs
         self.batch_size = batch_size

src/itwinai/tests/dummy_components.py

@@ -7,6 +7,7 @@ def __init__(self, data_uri: str, name: Optional[str] = None
                  ) -> None:
         super().__init__(name)
         self.save_parameters(data_uri=data_uri, name=name)
+        self.data_uri = data_uri
 
     def execute(self):
         ...
@@ -25,6 +26,7 @@ def __init__(self, train_prop: float, name: Optional[str] = None
                  ) -> None:
         super().__init__(name)
         self.save_parameters(train_prop=train_prop, name=name)
+        self.train_prop = train_prop
 
     def execute(self):
         ...
@@ -43,6 +45,7 @@ def __init__(self, max_items: int, name: Optional[str] = None
                  ) -> None:
         super().__init__(name)
         self.save_parameters(max_items=max_items, name=name)
+        self.max_items = max_items
 
     def execute(self):
         ...
@@ -59,6 +62,8 @@ def __init__(self, lr: float, batch_size: int, name: Optional[str] = None
                  ) -> None:
         super().__init__(name)
         self.save_parameters(lr=lr, batch_size=batch_size, name=name)
+        self.lr = lr
+        self.batch_size = batch_size
 
     def execute(self):
         ...
@@ -76,6 +81,7 @@ class FakeSaver(BaseComponent):
     def __init__(self, save_path: str, name: Optional[str] = None) -> None:
         super().__init__(name)
         self.save_parameters(save_path=save_path, name=name)
+        self.save_path = save_path
 
     def execute(self):
         ...

src/itwinai/torch/inference.py

@@ -93,7 +93,7 @@ def __init__(
         name: str = None
     ) -> None:
         super().__init__(model=model, name=name)
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.model = self.model.eval()
         # self.seed = seed
         # self.strategy = strategy

src/itwinai/torch/trainer.py

@@ -205,7 +205,7 @@ def __init__(
         Makes the model a DDP model.
         """
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.model = model
         self.loss = loss
         self.epochs = epochs

use-cases/3dgan/dataloader.py

@@ -20,7 +20,7 @@ def __init__(
         data_url: Optional[str] = None,
         name: Optional[str] = None,
     ) -> None:
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         super().__init__(name)
         self.data_path = data_path
         self.data_url = data_url

use-cases/3dgan/saver.py

@@ -17,7 +17,7 @@ def __init__(
         self,
         save_dir: str = '3dgan-generated'
     ) -> None:
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         super().__init__()
         self.save_dir = save_dir
 

use-cases/3dgan/trainer.py

@@ -19,7 +19,7 @@
 
 class Lightning3DGANTrainer(Trainer):
     def __init__(self, config: Union[Dict, str]):
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         super().__init__()
         if isinstance(config, str) and os.path.isfile(config):
             # Load from YAML
@@ -88,7 +88,7 @@ def __init__(
         config: Union[Dict, str],
         name: Optional[str] = None
     ):
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         super().__init__(model, name)
         if isinstance(config, str) and os.path.isfile(config):
             # Load from YAML

use-cases/cyclones/dataloader.py

@@ -43,6 +43,7 @@ def __init__(
         data_path: str = "tmp_data"
     ):
         super().__init__()
+        self.save_parameters(**self.locals2params(locals()))
         self.batch_size = batch_size
         self.split_ratio = split_ratio
         self.epochs = epochs

use-cases/cyclones/trainer.py

@@ -29,6 +29,7 @@ def __init__(
         cores: int = None,
     ):
         super().__init__()
+        self.save_parameters(**self.locals2params(locals()))
         # Configurable
         self.cores = cores
         self.model_backup = model_backup
@@ -43,7 +44,7 @@ def __init__(
         # Optimizers, Losses
         self.optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
 
-    def train(self, train_data, validation_data):
+    def execute(self, train_data, validation_data):
         train_dataset, n_train = train_data
         valid_dataset, n_valid = validation_data
 
@@ -103,16 +104,6 @@ def train(self, train_data, validation_data):
         model.save(self.last_model_name)
         logging.debug("Saved training history")
 
-    def execute(
-        self,
-        train_dataset,
-        validation_dataset,
-        config: Optional[Dict] = None,
-    ) -> Tuple[Optional[Tuple], Optional[Dict]]:
-        config = self.setup_config(config)
-        train_result = self.train(train_dataset, validation_dataset)
-        return (train_result,), config
-
     def setup_config(self, config: Optional[Dict] = None) -> Dict:
         config = config if config is not None else {}
         self.experiment_dir = config["experiment_dir"]

use-cases/mnist/tensorflow/dataloader.py

@@ -8,7 +8,7 @@
 class MNISTDataGetter(DataGetter):
     def __init__(self):
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
 
     @monitor_exec
     def execute(self) -> Tuple:
@@ -19,7 +19,7 @@ def execute(self) -> Tuple:
 class MNISTDataPreproc(DataPreproc):
     def __init__(self, classes: int):
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.classes = classes
 
     @monitor_exec

use-cases/mnist/tensorflow/trainer.py

@@ -28,7 +28,7 @@ def __init__(
             compile_conf=dict(loss=loss, optimizer=optimizer),
             strategy=strategy
         )
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         print(f'STRATEGY: {strategy}')
         self.logger = logger if logger is not None else []
 

use-cases/mnist/torch-lightning/dataloader.py

@@ -15,7 +15,7 @@ def __init__(
         name: Optional[str] = None
     ) -> None:
         super().__init__(name)
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.data_path = data_path
         self._downloader = MNISTDataModule(
             data_path=self.data_path, download=True,

use-cases/mnist/torch-lightning/trainer.py

@@ -11,7 +11,7 @@
 class LightningMNISTTrainer(Trainer):
     def __init__(self, config: Union[Dict, str]):
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         if isinstance(config, str) and os.path.isfile(config):
             # Load from YAML
             config = load_yaml(config)

use-cases/mnist/torch/dataloader.py

@@ -16,7 +16,7 @@ class MNISTDataModuleTorch(DataGetter):
 
     def __init__(self, save_path: str = '.tmp/',) -> None:
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.save_path = save_path
 
     @monitor_exec
@@ -107,7 +107,7 @@ def generate_jpg_sample(
 class MNISTPredictLoader(DataGetter):
     def __init__(self, test_data_path: str) -> None:
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.test_data_path = test_data_path
 
     @monitor_exec

use-cases/mnist/torch/saver.py

@@ -20,7 +20,7 @@ def __init__(
         class_labels: Optional[List] = None
     ) -> None:
         super().__init__()
-        self.save_parameters(**locals())
+        self.save_parameters(**self.locals2params(locals()))
         self.save_dir = save_dir
         self.predictions_file = predictions_file
         self.class_labels = (