def test_classification_accuracy_result_for_batch_2_with_not_ordered_ids(
self):
annotations = [
ClassificationAnnotation('identifier', 3),
ClassificationAnnotation('identifier1', 1)
]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0]),
ClassificationPrediction('identifier2', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1}], None)
metric_result, _ = dispatcher.update_metrics_on_batch([42, 17],
annotations,
predictions)
expected_metric_result = [
PerImageMetricResult('accuracy', 'accuracy', 1.0, 'higher-better'),
PerImageMetricResult('accuracy', 'accuracy', 0.0, 'higher-better')
]
assert len(metric_result) == 2
assert 42 in metric_result
assert len(metric_result[42]) == 1
assert metric_result[42][0] == expected_metric_result[0]
assert 17 in metric_result
assert len(metric_result[17]) == 1
assert metric_result[17][0] == expected_metric_result[1]
def test_classification_accuracy_result_for_batch_1_with_2_metrics(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{
'name': 'top1',
'type': 'accuracy',
'top_k': 1
}, {
'name': 'top3',
'type': 'accuracy',
'top_k': 3
}], None)
metric_result, _ = dispatcher.update_metrics_on_batch(
range(len(annotations)), annotations, predictions)
expected_metric_result = [
PerImageMetricResult('top1', 'accuracy', 1.0, 'higher-better'),
PerImageMetricResult('top3', 'accuracy', 1.0, 'higher-better')
]
assert len(metric_result) == 1
assert 0 in metric_result
assert len(metric_result[0]) == 2
assert metric_result[0][0] == expected_metric_result[0]
assert metric_result[0][1] == expected_metric_result[1]