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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for iou metric."""

import tensorflow as tf, tf_keras

from official.vision.evaluation import iou


class IoUTest(tf.test.TestCase):

  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_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_weighted(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_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)

  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_update_state_and_result(self):
    y_pred = [0, 1, 0, 1]
    y_true = [0, 0, 1, 1]

    m_obj = iou.PerClassIoU(num_classes=2)

    m_obj.update_state(y_true, y_pred)
    result = m_obj.result()

    # 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_per_class_iou_v2(self):
    metrics = iou.PerClassIoUV2(num_classes=3)
    y_true = tf.constant([[
        [
            [0, 0, 1],
            [0, 1, 1],
        ],
        [
            [0, 1, 0],
            [0, 0, 1],
        ],
    ]])
    y_pred = tf.constant([[
        [
            [1, 0, 0],
            [1, 1, 1],
        ],
        [
            [1, 1, 1],
            [1, 0, 1],
        ],
    ]])
    metrics.update_state(y_true, y_pred)
    self.assertAllClose([0.0, 1.0, 0.5], metrics.result(), atol=1e-3)

  def test_per_class_iou_v2_sparse_input(self):
    metrics = iou.PerClassIoUV2(
        num_classes=3, sparse_y_true=True, sparse_y_pred=True)
    y_true = [[
        [1, 2, 1],
        [2, 2, 1],
    ]]
    y_pred = [[
        [2, 0, 1],
        [2, 0, 1],
    ]]
    metrics.update_state(y_true, y_pred)
    self.assertAllClose([0., 2. / 3., 1. / 4.], metrics.result(), atol=1e-3)

  def test_per_class_iou_v2_keep_tailing_dims(self):
    num_classes = 3
    num_channels = 2
    metrics = iou.PerClassIoUV2(
        num_classes=num_classes,
        shape=(num_classes, num_channels),
        sparse_y_true=True,
        sparse_y_pred=True,
        axis=0)
    y_pred = tf.constant([2, 1])
    y_true = tf.constant([2, 0])
    metrics.update_state(y_true, y_pred)
    self.assertAllClose([[0., 0.], [0., 0.], [1., 0.]],
                        metrics.result(),
                        atol=1e-3)


if __name__ == '__main__':
  tf.test.main()
