Rename the all metrics to train metrics

golmschenk · Sep 25, 2024 · 5b4c90e · 5b4c90e
1 parent d15a6f9
commit 5b4c90e
Showing 1 changed file with 11 additions and 11 deletions.
diff --git a/src/qusi/internal/module.py b/src/qusi/internal/module.py
@@ -52,10 +52,10 @@ def __init__(
         self.model: Module = model
         self._optimizer: Optimizer = optimizer
         self.loss_metric: Module = loss_metric
-        self.state_based_logging_metrics: ModuleList = state_based_logging_metrics
-        self.functional_logging_metrics: list[Module] = functional_logging_metrics
-        self._functional_logging_metric_cycle_totals: npt.NDArray = np.zeros(len(self.functional_logging_metrics),
-                                                                             dtype=np.float32)
+        self.train_state_based_logging_metrics: ModuleList = state_based_logging_metrics
+        self.train_functional_logging_metrics: list[Module] = functional_logging_metrics
+        self._train_functional_logging_metric_cycle_totals: npt.NDArray = np.zeros(
+            len(self.train_functional_logging_metrics), dtype=np.float32)
         self._loss_cycle_total: int = 0
         self._steps_run_in_phase: int = 0
 
@@ -70,11 +70,11 @@ def compute_loss_and_metrics(self, batch):
         predicted = self(inputs)
         loss = self.loss_metric(predicted, target)
         self._loss_cycle_total += loss
-        for state_based_logging_metric in self.state_based_logging_metrics:
+        for state_based_logging_metric in self.train_state_based_logging_metrics:
             state_based_logging_metric(predicted, target)
-        for functional_logging_metric_index, functional_logging_metric in enumerate(self.functional_logging_metrics):
+        for functional_logging_metric_index, functional_logging_metric in enumerate(self.train_functional_logging_metrics):
             functional_logging_metric_value = functional_logging_metric(predicted, target)
-            self._functional_logging_metric_cycle_totals[
+            self._train_functional_logging_metric_cycle_totals[
                 functional_logging_metric_index] += functional_logging_metric_value
         self._steps_run_in_phase += 1
         return loss
@@ -83,14 +83,14 @@ def on_train_epoch_end(self) -> None:
         self.log_loss_and_metrics()
 
     def log_loss_and_metrics(self, logging_name_prefix: str = ''):
-        for state_based_logging_metric in self.state_based_logging_metrics:
+        for state_based_logging_metric in self.train_state_based_logging_metrics:
             state_based_logging_metric_name = get_metric_name(state_based_logging_metric)
             self.log(name=logging_name_prefix + state_based_logging_metric_name,
                      value=state_based_logging_metric.compute(), sync_dist=True)
             state_based_logging_metric.reset()
-        for functional_logging_metric_index, functional_logging_metric in enumerate(self.functional_logging_metrics):
+        for functional_logging_metric_index, functional_logging_metric in enumerate(self.train_functional_logging_metrics):
             functional_logging_metric_name = get_metric_name(functional_logging_metric)
-            functional_logging_metric_cycle_total = float(self._functional_logging_metric_cycle_totals[
+            functional_logging_metric_cycle_total = float(self._train_functional_logging_metric_cycle_totals[
                                                               functional_logging_metric_index])
 
             functional_logging_metric_cycle_mean = functional_logging_metric_cycle_total / self._steps_run_in_phase
@@ -101,7 +101,7 @@ def log_loss_and_metrics(self, logging_name_prefix: str = ''):
         self.log(name=logging_name_prefix + 'loss',
                  value=mean_cycle_loss, sync_dist=True)
         self._loss_cycle_total = 0
-        self._functional_logging_metric_cycle_totals = np.zeros(len(self.functional_logging_metrics), dtype=np.float32)
+        self._train_functional_logging_metric_cycle_totals = np.zeros(len(self.train_functional_logging_metrics), dtype=np.float32)
         self._steps_run_in_phase = 0
 
     def validation_step(self, batch: tuple[Any, Any], batch_index: int) -> STEP_OUTPUT: