feat: metric callback defaults on epoch

prototorch/models/architectures/base.py

@@ -46,15 +46,15 @@ class BaseYArchitecture(pl.LightningModule):
     components_layer: torch.nn.Module
 
     def __init__(self, hparams) -> None:
-        if type(hparams) is dict:
+        if isinstance(hparams, dict):
             self.save_hyperparameters(hparams)
             # TODO: => Move into Component Child
             del hparams["initialized_proto_shape"]
             hparams = self.HyperParameters(**hparams)
         else:
-            hparam_dict = asdict(hparams)
-            hparam_dict["component_initializer"] = None
-            self.save_hyperparameters(hparam_dict, )
+            hparams_dict = asdict(hparams)
+            hparams_dict["component_initializer"] = None
+            self.save_hyperparameters(hparams_dict, )
 
         super().__init__()
 
@@ -72,6 +72,9 @@ class BaseYArchitecture(pl.LightningModule):
 
     # external API
     def get_competition(self, batch, components):
+        '''
+        Returns the output of the competition layer.
+        '''
         latent_batch, latent_components = self.backbone(batch, components)
         # TODO: => Latent Hook
         comparison_tensor = self.comparison(latent_batch, latent_components)
@@ -79,6 +82,9 @@ class BaseYArchitecture(pl.LightningModule):
         return comparison_tensor
 
     def forward(self, batch):
+        '''
+        Returns the prediction.
+        '''
         if isinstance(batch, torch.Tensor):
             batch = (batch, None)
         # TODO: manage different datatypes?
@@ -95,6 +101,9 @@ class BaseYArchitecture(pl.LightningModule):
         return self.forward(batch)
 
     def forward_comparison(self, batch):
+        '''
+        Returns the Output of the comparison layer.
+        '''
         if isinstance(batch, torch.Tensor):
             batch = (batch, None)
         # TODO: manage different datatypes?
@@ -103,6 +112,9 @@ class BaseYArchitecture(pl.LightningModule):
         return self.get_competition(batch, components)
 
     def loss_forward(self, batch):
+        '''
+        Returns the output of the loss layer.
+        '''
         # TODO: manage different datatypes?
         components = self.components_layer()
         # TODO: => Component Hook
@@ -115,37 +127,31 @@ class BaseYArchitecture(pl.LightningModule):
         """
         All initialization necessary for the components step.
         """
-        ...
 
     def init_backbone(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the backbone step.
         """
-        ...
 
     def init_comparison(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the comparison step.
         """
-        ...
 
     def init_competition(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the competition step.
         """
-        ...
 
     def init_loss(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the loss step.
         """
-        ...
 
     def init_inference(self, hparams: HyperParameters) -> None:
         """
         All initialization necessary for the inference step.
         """
-        ...
 
     # Empty Steps
     def components(self):
@@ -162,7 +168,8 @@ class BaseYArchitecture(pl.LightningModule):
         The backbone step receives the data batch and the components.
         It can transform both by an arbitrary function.
 
-        It returns the transformed batch and components, each of the same length as the original input.
+        It returns the transformed batch and components,
+        each of the same length as the original input.
         """
         return batch, components
 
@@ -211,6 +218,9 @@ class BaseYArchitecture(pl.LightningModule):
         step: str = Steps.TRAINING,
         **metric_kwargs,
     ):
+        '''
+        Register a callback for evaluating a torchmetric.
+        '''
         if step == Steps.PREDICT:
             raise ValueError("Prediction metrics are not supported.")
 
@@ -224,7 +234,7 @@ class BaseYArchitecture(pl.LightningModule):
         # Prediction Metrics
         preds = self(batch)
 
-        x, y = batch
+        _, y = batch
         for metric in self.registered_metrics[step]:
             instance = self.registered_metrics[step][metric].to(self.device)
             instance(y, preds)
@@ -247,7 +257,7 @@ class BaseYArchitecture(pl.LightningModule):
 
         return self.loss_forward(batch)
 
-    def training_epoch_end(self, outs) -> None:
+    def training_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.TRAINING)
 
     # >>>> Validation
@@ -256,7 +266,7 @@ class BaseYArchitecture(pl.LightningModule):
 
         return self.loss_forward(batch)
 
-    def validation_epoch_end(self, outs) -> None:
+    def validation_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.VALIDATION)
 
     # >>>> Test
@@ -264,7 +274,7 @@ class BaseYArchitecture(pl.LightningModule):
         self.update_metrics_step(batch, Steps.TEST)
         return self.loss_forward(batch)
 
-    def test_epoch_end(self, outs) -> None:
+    def test_epoch_end(self, outputs) -> None:
         self.update_metrics_epoch(Steps.TEST)
 
     # >>>> Prediction

prototorch/models/callbacks.py

@@ -1,4 +1,3 @@
-import logging
 import warnings
 from typing import Optional, Type
 
@@ -36,12 +35,14 @@ class LogTorchmetricCallback(pl.Callback):
         name,
         metric: Type[torchmetrics.Metric],
         step: str = Steps.TRAINING,
+        on_epoch=True,
         **metric_kwargs,
     ) -> None:
         self.name = name
         self.metric = metric
         self.metric_kwargs = metric_kwargs
         self.step = step
+        self.on_epoch = on_epoch
 
     def setup(
         self,
@@ -57,7 +58,12 @@ class LogTorchmetricCallback(pl.Callback):
         )
 
     def __call__(self, value, pl_module: BaseYArchitecture):
-        pl_module.log(self.name, value)
+        pl_module.log(
+            self.name,
+            value,
+            on_epoch=self.on_epoch,
+            on_step=(not self.on_epoch),
+        )
 
 
 class LogConfusionMatrix(LogTorchmetricCallback):