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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# 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()
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