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# Lint as: python3
# Copyright 2020 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 nlp.nhnet.multi_channel_attention."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from official.nlp.modeling.layers import multi_channel_attention
class MultiChannelAttentionTest(tf.test.TestCase):
def test_doc_attention(self):
num_heads = 2
doc_attention = multi_channel_attention.VotingAttention(
num_heads, head_size=8)
num_docs = 3
inputs = np.zeros((2, num_docs, 10, 16), dtype=np.float32)
doc_mask = np.zeros((2, num_docs), dtype=np.float32)
outputs = doc_attention(inputs, doc_mask)
self.assertEqual(outputs.shape, (2, num_docs))
def test_multi_channel_attention(self):
num_heads = 2
num_docs = 5
attention_layer = multi_channel_attention.MultiChannelAttention(
num_heads, key_size=2)
from_data = 10 * np.random.random_sample((3, 4, 8))
to_data = 10 * np.random.random_sample((3, num_docs, 2, 8))
mask_data = np.random.randint(2, size=(3, num_docs, 4, 2))
doc_probs = np.random.randint(
2, size=(3, num_heads, 4, num_docs)).astype(float)
outputs = attention_layer([from_data, to_data, doc_probs], mask_data)
self.assertEqual(outputs.shape, (3, 4, 8))
if __name__ == "__main__":
tf.test.main()
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