def validate_end(self, outputs, num_samples):
conf_mat = sum([o['conf_mat'] for o in outputs])
val_loss = torch.stack([o['val_loss']
for o in outputs]).sum() / num_samples
conf_mat_metrics = compute_conf_mat_metrics(conf_mat,
self.cfg.data.class_names)
metrics = {'val_loss': val_loss.item()}
metrics.update(conf_mat_metrics)
return metrics
def test2(self):
label_names = ['a', 'b']
conf_mat = torch.tensor([[0, 2.], [2, 0]])
metrics = compute_conf_mat_metrics(conf_mat, label_names)
exp_metrics = {
'avg_precision': 0.0,
'avg_recall': 0.0,
'avg_f1': 0.0,
'a_precision': 0.0,
'a_recall': 0.0,
'a_f1': 0.0,
'b_precision': 0.0,
'b_recall': 0.0,
'b_f1': 0.0
}
self.assertDictEqual(metrics, exp_metrics)
def test3(self):
label_names = ['a', 'b']
conf_mat = torch.tensor([[1, 2], [1, 2.]])
metrics = compute_conf_mat_metrics(conf_mat, label_names, eps=0.0)
def f1(prec, rec):
return 2 * (prec * rec) / (prec + rec)
def mean(a, b):
return (a + b) / 2
def round_dict(d):
return dict([(k, round(v, 3)) for k, v in d.items()])
a_prec = 1 / 2
a_rec = 1 / 3
a_f1 = f1(a_prec, a_rec)
b_prec = 2 / 4
b_rec = 2 / 3
b_f1 = f1(b_prec, b_rec)
avg_prec = mean(a_prec, b_prec)
avg_rec = mean(a_rec, b_rec)
avg_f1 = f1(avg_prec, avg_rec)
exp_metrics = {
'avg_precision': avg_prec,
'avg_recall': avg_rec,
'avg_f1': avg_f1,
'a_precision': a_prec,
'a_recall': a_rec,
'a_f1': a_f1,
'b_precision': b_prec,
'b_recall': b_rec,
'b_f1': b_f1
}
self.assertDictEqual(round_dict(metrics), round_dict(exp_metrics))
