def test_zero_detection(self):
gt = make_representation(["0 0 0 5 5; 1 10 10 20 20"], is_ground_truth=True)
pred = make_representation(["0 30 30 40 40"], score=1)
metric = _test_metric_wrapper(DetectionMAP, multi_class_dataset())
assert metric(gt, pred) == [0.0]
assert metric.meta.get('names') == ['dog']
def test_no_warnings_in_map_calculation(self):
gt = make_representation(["0 0 0 5 5; 1 10 10 20 20", "1 0 0 5 5"], is_ground_truth=True)
pred = make_representation(["0 0 0 5 5; 1 10 10 20 20", "1 0 0 5 5"], score=1)
with pytest.warns(None) as warnings:
_test_metric_wrapper(DetectionMAP, multi_class_dataset())(gt, pred)
assert len(warnings) == 0
def test_no_detections_warn_user_warning(self):
gt = make_representation(["0 0 0 5 5; 1 10 10 20 20"], is_ground_truth=True)
pred = make_representation("", score=1)
with pytest.warns(UserWarning) as warnings:
map_ = _test_metric_wrapper(DetectionMAP, multi_class_dataset())(gt, pred)[0]
assert len(warnings) == 1
assert map_ == 0
def test_one_false_alarm(self):
gt = make_representation(["0 0 0 5 5", "1 0 0 5 5"], is_ground_truth=True)
pred = make_representation(["1 10 10 20 20; 0 0 0 5 5", "1 0 0 5 5"], score=1)
metric = _test_metric_wrapper(DetectionMAP, multi_class_dataset())
values = metric(gt, pred)
assert values == [1.0, 0.5]
map_ = np.mean(values)
assert 0.75 == map_
assert metric.meta.get('names') == ['dog', 'cat']
