Automatically formatted with black

lightorch/nn/criterions.py

@@ -32,8 +32,8 @@ def __init__(self, *loss) -> None:
             loss
         ), "Not valid input classes, each should be different."
         super().__init__(
-            labels = list(set([*chain.from_iterable([i.labels for i in loss])])),
-            factors = _merge_dicts([i.factors for i in loss]),
+            labels=list(set([*chain.from_iterable([i.labels for i in loss])])),
+            factors=_merge_dicts([i.factors for i in loss]),
         )
         self.loss = loss
 
@@ -48,8 +48,11 @@ def forward(self, **kwargs) -> Tuple[Tensor, ...]:
         out_list = tuple(out_list)
         return out_list
 
+
 class MSELoss(nn.MSELoss):
-    def __init__(self, size_average=None, reduce=None, reduction: str = "mean", factor: float = 1) -> None:
+    def __init__(
+        self, size_average=None, reduce=None, reduction: str = "mean", factor: float = 1
+    ) -> None:
         super(MSELoss, self).__init__(size_average, reduce, reduction)
         self.factors = {self.__class__.__name__: factor}
         self.labels = [self.__class__.__name__]
@@ -80,8 +83,15 @@ def forward(self, **kwargs) -> Tuple[Tensor, Tensor]:
         out = super().forward(kwargs["input"], kwargs["target"])
         return out, out * self.factors[self.__class__.__name__]
 
+
 class BinaryCrossEntropy(nn.BCELoss):
-    def __init__(self, weight: Optional[Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean') -> None:
+    def __init__(
+        self,
+        weight: Optional[Tensor] = None,
+        size_average=None,
+        reduce=None,
+        reduction: str = "mean",
+    ) -> None:
         super().__init__(weight, size_average, reduce, reduction)
         self.factors = {self.__class__.__name__: factor}
         self.labels = [self.__class__.__name__]
@@ -91,7 +101,6 @@ def forward(self, **kwargs) -> Tuple[Tensor, Tensor]:
         return out, out * self.factors[self.__class__.__name__]
 
 
-
 class ELBO(LighTorchLoss):
     """
     # Variational Autoencoder Loss:
@@ -103,8 +112,7 @@ def __init__(self, beta: float, reconstruction_criterion: LighTorchLoss) -> None
         factors = {"KL Divergence": beta}
         factors.update(reconstruction_criterion.factors)
         super().__init__(
-            labels = ["KL Divergence"] + reconstruction_criterion.labels,
-            factors = factors
+            labels=["KL Divergence"] + reconstruction_criterion.labels, factors=factors
         )
 
         self.L_recons = reconstruction_criterion
@@ -179,6 +187,7 @@ def forward(self, **kwargs) -> Tensor:
         )
         return out, self.factors[self.__class__.__name__] * out
 
+
 class PeakSignalNoiseRatio(LighTorchLoss):
     """
     forward (input, target)
@@ -192,6 +201,7 @@ def forward(self, **kwargs) -> Tensor:
         out = F.psnr(kwargs["input"], kwargs["target"], self.max)
         return out, out * self.factors[self.__class__.__name__]
 
+
 class TV(LighTorchLoss):
     """
     # Total Variance (TV)
@@ -205,6 +215,7 @@ def forward(self, **kwargs) -> Tensor:
         out = F.total_variance(kwargs["input"])
         return out, out * self.factors[self.__class__.__name__]
 
+
 class LagrangianFunctional(LighTorchLoss):
     """
     Creates a lagrangian function of the form:

lightorch/training/supervised.py

@@ -170,7 +170,7 @@ def _configure_optimizer(self) -> Optimizer:
         params = self.get_param_groups()
         if params is not None:
             if isinstance(self.optimizer, str):
-                if valid:=VALID_OPTIMIZERS.get(self.optimizer, None):
+                if valid := VALID_OPTIMIZERS.get(self.optimizer, None):
                     return valid(params)
                 raise TypeError("Not valid optimizer")
             elif isinstance(self.optimizer, Optimizer):
@@ -197,9 +197,13 @@ def _configure_scheduler(self, optimizer: Optimizer) -> LRScheduler:
                             self.trainer.estimated_stepping_batches
                         )
                     else:
-                        raise ValueError(f'Scheduler kwargs not defined for {self.scheduler}')
+                        raise ValueError(
+                            f"Scheduler kwargs not defined for {self.scheduler}"
+                        )
                 if self.scheduler_kwargs is not None:
-                    return VALID_SCHEDULERS[self.scheduler](optimizer, **self.scheduler_kwargs)
+                    return VALID_SCHEDULERS[self.scheduler](
+                        optimizer, **self.scheduler_kwargs
+                    )
             else:
                 return self.scheduler(optimizer)