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deeplab2 / model /layers /positional_encodings_test.py
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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 positional_encodings."""
import tensorflow as tf
from deeplab2.model.layers import positional_encodings
class PositionalEncodingsTest(tf.test.TestCase):
def test_compute_relative_distance_matrix_output_shape(self):
output = positional_encodings._compute_relative_distance_matrix(33, 97)
self.assertListEqual(output.get_shape().as_list(), [33, 97])
def test_relative_positional_encoding_output_shape(self):
layer = positional_encodings.RelativePositionalEncoding(
33, 97, 32, 8, 'rpe')
output = layer(None)
self.assertListEqual(output.get_shape().as_list(), [8, 33, 97, 32])
def test_add_absolute_positional_encoding_1d_output_shape(self):
layer = positional_encodings.AddAbsolutePositionalEncoding(
'ape1d', positional_encoding_type='1d')
shape = [2, 5, 5, 3]
output = layer(tf.zeros(shape))
self.assertEqual(len(layer.get_weights()), 10)
self.assertListEqual(output.get_shape().as_list(), shape)
def test_add_absolute_positional_encoding_2d_output_shape(self):
layer = positional_encodings.AddAbsolutePositionalEncoding(
'ape2d', positional_encoding_type='2d')
shape = [2, 5, 5, 3]
output = layer(tf.zeros(shape))
self.assertEqual(len(layer.get_weights()), 5)
self.assertListEqual(output.get_shape().as_list(), shape)
def test_add_absolute_positional_encoding_none_output_shape(self):
layer = positional_encodings.AddAbsolutePositionalEncoding(
'none', positional_encoding_type='none')
shape = [2, 5, 5, 3]
output = layer(tf.zeros(shape))
self.assertEqual(len(layer.get_weights()), 0)
self.assertListEqual(output.get_shape().as_list(), shape)
if __name__ == '__main__':
tf.test.main()