def test_config(self):
m_obj = iou.PerClassIoU(num_classes=2, name='per_class_iou')
self.assertEqual(m_obj.name, 'per_class_iou')
self.assertEqual(m_obj.num_classes, 2)
m_obj2 = iou.PerClassIoU.from_config(m_obj.get_config())
self.assertEqual(m_obj2.name, 'per_class_iou')
self.assertEqual(m_obj2.num_classes, 2)
def test_zero_and_non_zero_entries(self):
y_pred = tf.constant([1], dtype=tf.float32)
y_true = tf.constant([1])
m_obj = iou.PerClassIoU(num_classes=2)
result = m_obj(y_true, y_pred)
# cm = [[0, 0],
# [0, 1]]
# sum_row = [0, 1], sum_col = [0, 1], true_positives = [0, 1]
# iou = true_positives / (sum_row + sum_col - true_positives))
expected_result = [0, 1 / (1 + 1 - 1)]
self.assertAllClose(expected_result, result, atol=1e-3)
def test_unweighted(self):
y_pred = [0, 1, 0, 1]
y_true = [0, 0, 1, 1]
m_obj = iou.PerClassIoU(num_classes=2)
result = m_obj(y_true, y_pred)
# cm = [[1, 1],
# [1, 1]]
# sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]
# iou = true_positives / (sum_row + sum_col - true_positives))
expected_result = [1 / (2 + 2 - 1), 1 / (2 + 2 - 1)]
self.assertAllClose(expected_result, result, atol=1e-3)
def test_multi_dim_input(self):
y_pred = tf.constant([[0, 1], [0, 1]], dtype=tf.float32)
y_true = tf.constant([[0, 0], [1, 1]])
sample_weight = tf.constant([[0.2, 0.3], [0.4, 0.1]])
m_obj = iou.PerClassIoU(num_classes=2)
result = m_obj(y_true, y_pred, sample_weight=sample_weight)
# cm = [[0.2, 0.3],
# [0.4, 0.1]]
# sum_row = [0.6, 0.4], sum_col = [0.5, 0.5], true_positives = [0.2, 0.1]
# iou = true_positives / (sum_row + sum_col - true_positives))
expected_result = [0.2 / (0.6 + 0.5 - 0.2), 0.1 / (0.4 + 0.5 - 0.1)]
self.assertAllClose(expected_result, result, atol=1e-3)
def test_zero_valid_entries(self): m_obj = iou.PerClassIoU(num_classes=2) self.assertAllClose(m_obj.result(), [0, 0], atol=1e-3)