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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 segmentation_metrics.""" | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.vision.evaluation import segmentation_metrics | |
| class SegmentationMetricsTest(parameterized.TestCase, tf.test.TestCase): | |
| def _create_test_data(self): | |
| y_pred_cls0 = tf.constant([[1, 1, 0], [1, 1, 0], [0, 0, 0]], | |
| dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis] | |
| y_pred_cls1 = tf.constant([[0, 0, 0], [0, 0, 1], [0, 0, 1]], | |
| dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis] | |
| y_pred = tf.concat((y_pred_cls0, y_pred_cls1), axis=-1) | |
| y_true = { | |
| 'masks': | |
| tf.constant( | |
| [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], | |
| dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis], | |
| 'valid_masks': | |
| tf.ones([1, 6, 6, 1], dtype=tf.bool), | |
| 'image_info': | |
| tf.constant([[[6, 6], [3, 3], [0.5, 0.5], [0, 0]]], | |
| dtype=tf.float32) | |
| } | |
| return y_pred, y_true | |
| def test_mean_iou_metric(self, rescale_predictions, use_v2): | |
| tf.config.experimental_run_functions_eagerly(True) | |
| if use_v2: | |
| mean_iou_metric = segmentation_metrics.MeanIoUV2( | |
| num_classes=2, rescale_predictions=rescale_predictions) | |
| else: | |
| mean_iou_metric = segmentation_metrics.MeanIoU( | |
| num_classes=2, rescale_predictions=rescale_predictions) | |
| y_pred, y_true = self._create_test_data() | |
| # Disable autograph for correct coverage statistics. | |
| update_fn = tf.autograph.experimental.do_not_convert( | |
| mean_iou_metric.update_state) | |
| update_fn(y_true=y_true, y_pred=y_pred) | |
| miou = mean_iou_metric.result() | |
| self.assertAlmostEqual(miou.numpy(), 0.762, places=3) | |
| def test_per_class_mean_iou_metric(self, rescale_predictions, use_v2): | |
| if use_v2: | |
| per_class_iou_metric = segmentation_metrics.PerClassIoUV2( | |
| num_classes=2, rescale_predictions=rescale_predictions) | |
| else: | |
| per_class_iou_metric = segmentation_metrics.PerClassIoU( | |
| num_classes=2, rescale_predictions=rescale_predictions) | |
| y_pred, y_true = self._create_test_data() | |
| # Disable autograph for correct coverage statistics. | |
| update_fn = tf.autograph.experimental.do_not_convert( | |
| per_class_iou_metric.update_state) | |
| update_fn(y_true=y_true, y_pred=y_pred) | |
| per_class_miou = per_class_iou_metric.result() | |
| self.assertAllClose(per_class_miou.numpy(), [0.857, 0.667], atol=1e-3) | |
| def test_mean_iou_metric_v2_target_class_ids(self): | |
| tf.config.experimental_run_functions_eagerly(True) | |
| mean_iou_metric = segmentation_metrics.MeanIoUV2( | |
| num_classes=2, target_class_ids=[0]) | |
| y_pred, y_true = self._create_test_data() | |
| # Disable autograph for correct coverage statistics. | |
| update_fn = tf.autograph.experimental.do_not_convert( | |
| mean_iou_metric.update_state) | |
| update_fn(y_true=y_true, y_pred=y_pred) | |
| miou = mean_iou_metric.result() | |
| self.assertAlmostEqual(miou.numpy(), 0.857, places=3) | |
| if __name__ == '__main__': | |
| tf.test.main() | |