馃殌 Feature
When computing per class precision, F1-score, etc... it'd be very useful to have to possibility to put them all inside a list rather than flattened then sent to wandb.
When dealing with 10+ classes, summaries on wandb are extremely hard to read, especially when you keep adding more metrics.
What would be nice is to expose the log call so that it could allow more flexibility in terms of format.
As for now, I'm considering switching temporarily to Lightning.ai until Ignite matures more as a framework.
P.S.: A little off-topic, but despite being able to customize loss functions transforms, I've had a hard time getting automated per-class metrics from ClassificationReport because lists dont have an attach() method. So instead of doing :
class_report = ClassificationReport(...)
prec_per_class = [class_report[str(i)]["precision"] for i in range(C)]
I had to do something rather tricky :
cm = ConfusionMatrix(...)
prec_per_class = (cm.diag() / (cm.sum(dim=1) + 1e-9)).tolist()
But even with tolist(), output is flattened before being sent to wandb.
馃殌 Feature
When computing per class precision, F1-score, etc... it'd be very useful to have to possibility to put them all inside a list rather than flattened then sent to wandb.
When dealing with 10+ classes, summaries on wandb are extremely hard to read, especially when you keep adding more metrics.
What would be nice is to expose the log call so that it could allow more flexibility in terms of format.
As for now, I'm considering switching temporarily to Lightning.ai until Ignite matures more as a framework.
P.S.: A little off-topic, but despite being able to customize loss functions transforms, I've had a hard time getting automated per-class metrics from
ClassificationReportbecause lists dont have anattach()method. So instead of doing :I had to do something rather tricky :
But even with
tolist(), output is flattened before being sent to wandb.