fix logging

enhancer/cli/train.py

@@ -4,7 +4,7 @@ from types import MethodType
 import hydra
 from hydra.utils import instantiate
 from omegaconf import DictConfig, OmegaConf
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import MLFlowLogger
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
@@ -39,21 +39,21 @@ def main(config: DictConfig):
     checkpoint = ModelCheckpoint(
         dirpath="./model",
         filename=f"model_{JOB_ID}",
-        monitor="val_loss",
+        monitor="valid_loss",
         verbose=False,
         mode=direction,
         every_n_epochs=1,
     )
     callbacks.append(checkpoint)
-    early_stopping = EarlyStopping(
+    # early_stopping = EarlyStopping(
-        monitor="val_loss",
+    #     monitor="val_loss",
-        mode=direction,
+    #     mode=direction,
-        min_delta=1e-7,
+    #     min_delta=0.0,
-        patience=parameters.get("EarlyStopping_patience", 10),
+    #     patience=parameters.get("EarlyStopping_patience", 10),
-        strict=True,
+    #     strict=True,
-        verbose=True,
+    #     verbose=False,
-    )
+    # )
-    callbacks.append(early_stopping)
+    # callbacks.append(early_stopping)
 
     def configure_optimizer(self):
         optimizer = instantiate(

enhancer/loss.py

@@ -151,9 +151,11 @@ class LossWrapper(nn.Module):
             )
 
         self.higher_better = direction[0]
+        self.name = ""
         for loss in losses:
             loss = self.validate_loss(loss)
             self.valid_losses.append(loss())
+            self.name += f"{loss().name}_"
 
     def validate_loss(self, loss: str):
         if loss not in LOSS_MAP.keys():

enhancer/models/model.py

@@ -132,45 +132,40 @@ class Model(pl.LightningModule):
         mixed_waveform = batch["noisy"]
         target = batch["clean"]
         prediction = self(mixed_waveform)
-
         loss = self.loss(prediction, target)
 
-        if (
+        self.log(
-            (self.logger)
+            "train_loss",
-            and (self.global_step > 50)
+            loss.item(),
-            and (self.global_step % 50 == 0)
+            on_epoch=True,
-        ):
+            on_step=True,
-            self.logger.experiment.log_metric(
+            logger=True,
-                run_id=self.logger.run_id,
+            prog_bar=True,
-                key="train_loss",
+        )
-                value=loss.item(),
+
-                step=self.global_step,
-            )
-        self.log("train_loss", loss.item())
         return {"loss": loss}
 
     def validation_step(self, batch, batch_idx: int):
 
+        metric_dict = {}
         mixed_waveform = batch["noisy"]
         target = batch["clean"]
         prediction = self(mixed_waveform)
 
-        loss_val = self.loss(prediction, target)
+        metric_dict["valid_loss"] = self.loss(target, prediction).item()
-        self.log("val_loss", loss_val.item())
+        for metric in self.metric:
+            value = metric(target, prediction)
+            metric_dict[f"valid_{metric.name}"] = value.item()
 
-        if (
+        self.log_dict(
-            (self.logger)
+            metric_dict,
-            and (self.global_step > 50)
+            on_step=True,
-            and (self.global_step % 50 == 0)
+            on_epoch=True,
-        ):
+            prog_bar=True,
-            self.logger.experiment.log_metric(
+            logger=True,
-                run_id=self.logger.run_id,
+        )
-                key="val_loss",
-                value=loss_val.item(),
-                step=self.global_step,
-            )
 
-        return {"loss": loss_val}
+        return metric_dict
 
     def test_step(self, batch, batch_idx):
 
@@ -181,46 +176,18 @@ class Model(pl.LightningModule):
 
         for metric in self.metric:
             value = metric(target, prediction)
-            metric_dict[metric.name] = value
+            metric_dict[f"test_{metric.name}"] = value
 
-        for k, v in metric_dict.items():
+        self.log_dict(
-            self.logger.experiment.log_metric(
+            metric_dict,
-                run_id=self.logger.run_id,
+            on_step=True,
-                key=k,
+            on_epoch=True,
-                value=v,
+            prog_bar=True,
-                step=self.global_step,
+            logger=True,
-            )
+        )
 
         return metric_dict
 
-    def training_epoch_end(self, outputs):
-        train_mean_loss = 0.0
-        for output in outputs:
-            train_mean_loss += output["loss"]
-        train_mean_loss /= len(outputs)
-
-        if self.logger:
-            self.logger.experiment.log_metric(
-                run_id=self.logger.run_id,
-                key="train_loss_epoch",
-                value=train_mean_loss,
-                step=self.current_epoch,
-            )
-
-    def validation_epoch_end(self, outputs):
-        valid_mean_loss = 0.0
-        for output in outputs:
-            valid_mean_loss += output["loss"]
-        valid_mean_loss /= len(outputs)
-
-        if self.logger:
-            self.logger.experiment.log_metric(
-                run_id=self.logger.run_id,
-                key="valid_loss_epoch",
-                value=valid_mean_loss,
-                step=self.current_epoch,
-            )
-
     def test_epoch_end(self, outputs):
 
         test_mean_metrics = defaultdict(int)