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| """Tests for autoaugment."""
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|
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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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|
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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.legacy.image_classification import augment
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| def get_dtype_test_cases():
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| return [
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| ('uint8', tf.uint8),
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| ('int32', tf.int32),
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| ('float16', tf.float16),
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| ('float32', tf.float32),
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| ]
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| @parameterized.named_parameters(get_dtype_test_cases())
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| class TransformsTest(parameterized.TestCase, tf.test.TestCase):
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| """Basic tests for fundamental transformations."""
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| def test_to_from_4d(self, dtype):
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| for shape in [(10, 10), (10, 10, 10), (10, 10, 10, 10)]:
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| original_ndims = len(shape)
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| image = tf.zeros(shape, dtype=dtype)
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| image_4d = augment.to_4d(image)
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| self.assertEqual(4, tf.rank(image_4d))
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| self.assertAllEqual(image, augment.from_4d(image_4d, original_ndims))
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|
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| def test_transform(self, dtype):
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| image = tf.constant([[1, 2], [3, 4]], dtype=dtype)
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| self.assertAllEqual(
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| augment.transform(image, transforms=[1] * 8), [[4, 4], [4, 4]])
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|
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| def test_translate(self, dtype):
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| image = tf.constant(
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| [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], dtype=dtype)
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| translations = [-1, -1]
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| translated = augment.translate(image=image, translations=translations)
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| expected = [[1, 0, 1, 1], [0, 1, 0, 0], [1, 0, 1, 1], [1, 0, 1, 1]]
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| self.assertAllEqual(translated, expected)
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|
|
| def test_translate_shapes(self, dtype):
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| translation = [0, 0]
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| for shape in [(3, 3), (5, 5), (224, 224, 3)]:
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| image = tf.zeros(shape, dtype=dtype)
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| self.assertAllEqual(image, augment.translate(image, translation))
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|
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| def test_translate_invalid_translation(self, dtype):
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| image = tf.zeros((1, 1), dtype=dtype)
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| invalid_translation = [[[1, 1]]]
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| with self.assertRaisesRegex(TypeError, 'rank 1 or 2'):
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| _ = augment.translate(image, invalid_translation)
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|
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| def test_rotate(self, dtype):
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| image = tf.reshape(tf.cast(tf.range(9), dtype), (3, 3))
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| rotation = 90.
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| transformed = augment.rotate(image=image, degrees=rotation)
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| expected = [[2, 5, 8], [1, 4, 7], [0, 3, 6]]
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| self.assertAllEqual(transformed, expected)
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|
|
| def test_rotate_shapes(self, dtype):
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| degrees = 0.
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| for shape in [(3, 3), (5, 5), (224, 224, 3)]:
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| image = tf.zeros(shape, dtype=dtype)
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| self.assertAllEqual(image, augment.rotate(image, degrees))
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|
|
| class AutoaugmentTest(tf.test.TestCase):
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| def test_autoaugment(self):
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| """Smoke test to be sure there are no syntax errors."""
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| image = tf.zeros((224, 224, 3), dtype=tf.uint8)
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| augmenter = augment.AutoAugment()
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| aug_image = augmenter.distort(image)
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| self.assertEqual((224, 224, 3), aug_image.shape)
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|
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| def test_randaug(self):
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| """Smoke test to be sure there are no syntax errors."""
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| image = tf.zeros((224, 224, 3), dtype=tf.uint8)
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|
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| augmenter = augment.RandAugment()
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| aug_image = augmenter.distort(image)
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| self.assertEqual((224, 224, 3), aug_image.shape)
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|
|
| def test_all_policy_ops(self):
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| """Smoke test to be sure all augmentation functions can execute."""
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|
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| prob = 1
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| magnitude = 10
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| replace_value = [128] * 3
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| cutout_const = 100
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| translate_const = 250
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| image = tf.ones((224, 224, 3), dtype=tf.uint8)
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|
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| for op_name in augment.NAME_TO_FUNC:
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| func, _, args = augment._parse_policy_info(op_name, prob, magnitude,
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| replace_value, cutout_const,
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| translate_const)
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| image = func(image, *args)
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|
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| self.assertEqual((224, 224, 3), image.shape)
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|
|
| if __name__ == '__main__':
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| tf.test.main()
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|
