Axial-DeepLab-SWideRNet / data /preprocessing /autoaugment_utils_test.py
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from https://huggingface.co/spaces/akhaliq/deeplab2
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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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 autoaugment_utils.py."""
import numpy as np
import tensorflow as tf
from deeplab2.data.preprocessing import autoaugment_utils
class AutoaugmentUtilsTest(tf.test.TestCase):
def testAugmentWithNamedPolicy(self):
num_classes = 3
np_image = np.random.randint(256, size=(13, 13, 3))
image = tf.constant(np_image, dtype=tf.uint8)
np_label = np.random.randint(num_classes, size=(13, 13, 1))
label = tf.constant(np_label, dtype=tf.int32)
image, label = autoaugment_utils.distort_image_with_autoaugment(
image, label, ignore_label=255,
augmentation_name='simple_classification_policy')
self.assertTrue(image.numpy().any())
self.assertTrue(label.numpy().any())
if __name__ == '__main__':
tf.test.main()