def _after_epoch(self) -> None:
for i in range(self.num_classes):
self.total_seen[i] = dist.allreduce(self.total_seen[i],
reduction='sum')
self.total_correct[i] = dist.allreduce(self.total_correct[i],
reduction='sum')
self.total_positive[i] = dist.allreduce(self.total_positive[i],
reduction='sum')
ious = []
for i in range(self.num_classes):
if self.total_seen[i] == 0:
ious.append(1)
else:
cur_iou = self.total_correct[i] / (self.total_seen[i] +
self.total_positive[i] -
self.total_correct[i])
ious.append(cur_iou)
miou = np.mean(ious)
if hasattr(self, 'trainer') and hasattr(self.trainer, 'summary'):
self.trainer.summary.add_scalar(self.name, miou * 100)
else:
print(ious)
print(miou)
def _after_epoch(self) -> None:
self.size = dist.allreduce(self.size, reduction='sum')
self.errors = dist.allreduce(self.errors, reduction='sum')
self.trainer.summary.add_scalar(self.name, self.errors / self.size)