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fix logging

This commit is contained in:
shahules786 2022-10-12 17:26:51 +05:30
parent e389acefa0
commit 144b9d6128
1 changed files with 28 additions and 62 deletions
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84
enhancer/models/model.py
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@ -132,45 +132,39 @@ class Model(pl.LightningModule):
mixed_waveform = batch["noisy"]
target = batch["clean"]
prediction = self(mixed_waveform)
loss = self.loss(prediction, target)
if (
(self.logger)
and (self.global_step > 50)
and (self.global_step % 50 == 0)
):
self.logger.experiment.log_metric(
run_id=self.logger.run_id,
key="train_loss",
value=loss.item(),
step=self.global_step,
self.log(
"train_loss",
loss.item(),
on_epoch=True,
on_step=True,
logger=True,
prog_bar=True,
)
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)
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.logger)
and (self.global_step > 50)
and (self.global_step % 50 == 0)
):
self.logger.experiment.log_metric(
run_id=self.logger.run_id,
key="val_loss",
value=loss_val.item(),
step=self.global_step,
self.log_dict(
metric_dict,
on_step=True,
on_epoch=True,
prog_bar=True,
logger=True,
)
return {"loss": loss_val}
return metric_dict
def test_step(self, batch, batch_idx):
@ -183,44 +177,16 @@ class Model(pl.LightningModule):
value = metric(target, prediction)
metric_dict[metric.name] = value
for k, v in metric_dict.items():
self.logger.experiment.log_metric(
run_id=self.logger.run_id,
key=k,
value=v,
step=self.global_step,
self.log_dict(
metric_dict,
on_step=True,
on_epoch=True,
prog_bar=True,
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)