def log_run(split: str, epoch: int, writer: tf.summary.SummaryWriter,
label_names: Sequence[str], metrics: MutableMapping[str, float],
heaps: Mapping[str,
Mapping[int,
List[HeapItem]]], cm: np.ndarray) -> None:
"""Logs the outputs (metrics, confusion matrix, tp/fp/fn images) from a
single epoch run to Tensorboard.
Args:
metrics: dict, keys already prefixed with {split}/
"""
per_class_recall = recall_from_confusion_matrix(cm, label_names)
metrics.update(prefix_all_keys(per_class_recall, f'{split}/label_recall/'))
# log metrics
for metric, value in metrics.items():
tf.summary.scalar(metric, value, epoch)
# log confusion matrix
cm_fig = plot_utils.plot_confusion_matrix(cm,
classes=label_names,
normalize=True)
cm_fig_img = tf.convert_to_tensor(fig_to_img(cm_fig)[np.newaxis, ...])
tf.summary.image(f'confusion_matrix/{split}', cm_fig_img, step=epoch)
# log tp/fp/fn images
for heap_type, heap_dict in heaps.items():
log_images_with_confidence(heap_dict,
label_names,
epoch=epoch,
tag=f'{split}/{heap_type}')
writer.flush()
def log_run(split: str, epoch: int, writer: tensorboard.SummaryWriter,
label_names: Sequence[str], metrics: MutableMapping[str, float],
heaps: Optional[Mapping[str, Mapping[int, list[HeapItem]]]],
cm: np.ndarray) -> None:
"""Logs the outputs (metrics, confusion matrix, tp/fp/fn images) from a
single epoch run to Tensorboard.
Args:
metrics: dict, keys already prefixed with {split}/
"""
per_label_recall = recall_from_confusion_matrix(cm, label_names)
metrics.update(prefix_all_keys(per_label_recall, f'{split}/label_recall/'))
# log metrics
for metric, value in metrics.items():
writer.add_scalar(metric, value, epoch)
# log confusion matrix
cm_fig = plot_utils.plot_confusion_matrix(cm, classes=label_names,
normalize=True)
cm_fig_img = fig_to_img(cm_fig)
writer.add_image(tag=f'confusion_matrix/{split}', img_tensor=cm_fig_img,
global_step=epoch, dataformats='HWC')
# log tp/fp/fn images
if heaps is not None:
for heap_type, heap_dict in heaps.items():
log_images_with_confidence(writer, heap_dict, label_names,
epoch=epoch, tag=f'{split}/{heap_type}')
writer.flush()