File size: 2,381 Bytes
25e57c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # Copyright 2023 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 official.nlp.projects.fffner.fffner_encoder."""
import numpy as np
import tensorflow as tf, tf_keras
from official.projects.fffner import fffner_encoder
class FFFNerEncoderTest(tf.test.TestCase):
def setUp(self):
super().setUp()
np.random.seed(0)
tf.random.set_seed(0)
def test_encoder(self):
sequence_length = 128
batch_size = 2
vocab_size = 1024
hidden_size = 256
network = fffner_encoder.FFFNerEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_layers=1,
num_attention_heads=4,
max_sequence_length=512,
dict_outputs=True)
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length), dtype=np.int32)
mask_data = np.random.randint(
2, size=(batch_size, sequence_length), dtype=np.int32)
type_id_data = np.random.randint(
2, size=(batch_size, sequence_length), dtype=np.int32)
is_entity_token_pos = np.random.randint(
sequence_length, size=(batch_size,), dtype=np.int32)
entity_type_token_pos = np.random.randint(
sequence_length, size=(batch_size,), dtype=np.int32)
inputs = {
'input_word_ids': word_id_data,
'input_mask': mask_data,
'input_type_ids': type_id_data,
'is_entity_token_pos': is_entity_token_pos,
'entity_type_token_pos': entity_type_token_pos
}
outputs = network(inputs)
self.assertEqual(outputs['sequence_output'].shape,
(batch_size, sequence_length, hidden_size))
self.assertEqual(outputs['pooled_output'].shape,
(batch_size, 2 * hidden_size))
if __name__ == '__main__':
tf.test.main()
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