Merge pull request #3521 from YknZhu/master

Add deeplab model in tensorflow models

research/deeplab/utils/get_dataset_colormap_test.py

+# Copyright 2018 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 get_dataset_colormap.py."""
+
+import numpy as np
+import tensorflow as tf
+
+from deeplab.utils import get_dataset_colormap
+
+
+class VisualizationUtilTest(tf.test.TestCase):
+
+  def testBitGet(self):
+    """Test that if the returned bit value is correct."""
+    self.assertEqual(1, get_dataset_colormap.bit_get(9, 0))
+    self.assertEqual(0, get_dataset_colormap.bit_get(9, 1))
+    self.assertEqual(0, get_dataset_colormap.bit_get(9, 2))
+    self.assertEqual(1, get_dataset_colormap.bit_get(9, 3))
+
+  def testPASCALLabelColorMapValue(self):
+    """Test the getd color map value."""
+    colormap = get_dataset_colormap.create_pascal_label_colormap()
+
+    # Only test a few sampled entries in the color map.
+    self.assertTrue(np.array_equal([128., 0., 128.], colormap[5, :]))
+    self.assertTrue(np.array_equal([128., 192., 128.], colormap[23, :]))
+    self.assertTrue(np.array_equal([128., 0., 192.], colormap[37, :]))
+    self.assertTrue(np.array_equal([224., 192., 192.], colormap[127, :]))
+    self.assertTrue(np.array_equal([192., 160., 192.], colormap[175, :]))
+
+  def testLabelToPASCALColorImage(self):
+    """Test the value of the converted label value."""
+    label = np.array([[0, 16, 16], [52, 7, 52]])
+    expected_result = np.array([
+        [[0, 0, 0], [0, 64, 0], [0, 64, 0]],
+        [[0, 64, 192], [128, 128, 128], [0, 64, 192]]
+    ])
+    colored_label = get_dataset_colormap.label_to_color_image(
+        label, get_dataset_colormap.get_pascal_name())
+    self.assertTrue(np.array_equal(expected_result, colored_label))
+
+  def testUnExpectedLabelValueForLabelToPASCALColorImage(self):
+    """Raise ValueError when input value exceeds range."""
+    label = np.array([[120], [300]])
+    with self.assertRaises(ValueError):
+      get_dataset_colormap.label_to_color_image(
+          label, get_dataset_colormap.get_pascal_name())
+
+  def testUnExpectedLabelDimensionForLabelToPASCALColorImage(self):
+    """Raise ValueError if input dimension is not correct."""
+    label = np.array([120])
+    with self.assertRaises(ValueError):
+      get_dataset_colormap.label_to_color_image(
+          label, get_dataset_colormap.get_pascal_name())
+
+  def testGetColormapForUnsupportedDataset(self):
+    with self.assertRaises(ValueError):
+      get_dataset_colormap.create_label_colormap('unsupported_dataset')
+
+
+if __name__ == '__main__':
+  tf.test.main()