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Support MLflow along with Tensorboard for logging segmentation task visualizations during validation #1209

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merged 1 commit into from Jan 3, 2023
Merged

Support MLflow along with Tensorboard for logging segmentation task visualizations during validation #1209

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16 changes: 12 additions & 4 deletions pyannote/audio/tasks/segmentation/mixins.py
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Expand Up @@ -30,6 +30,7 @@
import numpy as np
import torch
from pyannote.core import Segment, SlidingWindowFeature
from pytorch_lightning.loggers import TensorBoardLogger, MLFlowLogger
from torch.utils.data._utils.collate import default_collate
from torchmetrics import Metric
from torchmetrics.classification import BinaryAUROC, MultilabelAUROC, MulticlassAUROC
Expand Down Expand Up @@ -429,7 +430,7 @@ def validation_step(self, batch, batch_idx: int):
):
return

# visualize first 9 validation samples of first batch in Tensorboard
# visualize first 9 validation samples of first batch in Tensorboard/MLflow
X = X.cpu().numpy()
y = y.float().cpu().numpy()
y_pred = y_pred.cpu().numpy()
Expand Down Expand Up @@ -478,8 +479,15 @@ def validation_step(self, batch, batch_idx: int):

plt.tight_layout()

self.model.logger.experiment.add_figure(
f"{self.logging_prefix}ValSamples", fig, self.model.current_epoch
)
if isinstance(self.model.logger, TensorBoardLogger):
self.model.logger.experiment.add_figure(
f"{self.logging_prefix}ValSamples", fig, self.model.current_epoch
)
elif isinstance(self.model.logger, MLFlowLogger):
self.model.logger.experiment.log_figure(
run_id=self.model.logger.run_id,
figure=fig,
artifact_file=f"{self.logging_prefix}ValSamples_epoch{self.model.current_epoch}.png",
)

plt.close(fig)