mirror of https://github.com/tensorflow/models.git
96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
# Copyright 2025 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for segmentation_metrics."""
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from absl.testing import parameterized
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import tensorflow as tf, tf_keras
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from official.vision.evaluation import segmentation_metrics
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class SegmentationMetricsTest(parameterized.TestCase, tf.test.TestCase):
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def _create_test_data(self):
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y_pred_cls0 = tf.constant([[1, 1, 0], [1, 1, 0], [0, 0, 0]],
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dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis]
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y_pred_cls1 = tf.constant([[0, 0, 0], [0, 0, 1], [0, 0, 1]],
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dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis]
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y_pred = tf.concat((y_pred_cls0, y_pred_cls1), axis=-1)
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y_true = {
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'masks':
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tf.constant(
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[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]],
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dtype=tf.uint16)[tf.newaxis, :, :, tf.newaxis],
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'valid_masks':
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tf.ones([1, 6, 6, 1], dtype=tf.bool),
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'image_info':
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tf.constant([[[6, 6], [3, 3], [0.5, 0.5], [0, 0]]],
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dtype=tf.float32)
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}
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return y_pred, y_true
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@parameterized.parameters((True, True), (False, False), (True, False),
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(False, True))
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def test_mean_iou_metric(self, rescale_predictions, use_v2):
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tf.config.experimental_run_functions_eagerly(True)
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if use_v2:
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mean_iou_metric = segmentation_metrics.MeanIoUV2(
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num_classes=2, rescale_predictions=rescale_predictions)
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else:
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mean_iou_metric = segmentation_metrics.MeanIoU(
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num_classes=2, rescale_predictions=rescale_predictions)
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y_pred, y_true = self._create_test_data()
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# Disable autograph for correct coverage statistics.
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update_fn = tf.autograph.experimental.do_not_convert(
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mean_iou_metric.update_state)
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update_fn(y_true=y_true, y_pred=y_pred)
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miou = mean_iou_metric.result()
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self.assertAlmostEqual(miou.numpy(), 0.762, places=3)
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@parameterized.parameters((True, True), (False, False), (True, False),
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(False, True))
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def test_per_class_mean_iou_metric(self, rescale_predictions, use_v2):
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if use_v2:
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per_class_iou_metric = segmentation_metrics.PerClassIoUV2(
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num_classes=2, rescale_predictions=rescale_predictions)
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else:
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per_class_iou_metric = segmentation_metrics.PerClassIoU(
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num_classes=2, rescale_predictions=rescale_predictions)
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y_pred, y_true = self._create_test_data()
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# Disable autograph for correct coverage statistics.
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update_fn = tf.autograph.experimental.do_not_convert(
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per_class_iou_metric.update_state)
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update_fn(y_true=y_true, y_pred=y_pred)
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per_class_miou = per_class_iou_metric.result()
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self.assertAllClose(per_class_miou.numpy(), [0.857, 0.667], atol=1e-3)
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def test_mean_iou_metric_v2_target_class_ids(self):
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tf.config.experimental_run_functions_eagerly(True)
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mean_iou_metric = segmentation_metrics.MeanIoUV2(
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num_classes=2, target_class_ids=[0])
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y_pred, y_true = self._create_test_data()
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# Disable autograph for correct coverage statistics.
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update_fn = tf.autograph.experimental.do_not_convert(
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mean_iou_metric.update_state)
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update_fn(y_true=y_true, y_pred=y_pred)
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miou = mean_iou_metric.result()
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self.assertAlmostEqual(miou.numpy(), 0.857, places=3)
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if __name__ == '__main__':
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tf.test.main()
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