# Copyright 2022 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 text_searcher."""
import enum
from absl.testing import parameterized
import tensorflow as tf
from tensorflow_lite_support.python.task.core import base_options as base_options_module
from tensorflow_lite_support.python.task.processor.proto import embedding_options_pb2
from tensorflow_lite_support.python.task.processor.proto import search_options_pb2
from tensorflow_lite_support.python.task.processor.proto import search_result_pb2
from tensorflow_lite_support.python.task.text import text_searcher
from tensorflow_lite_support.python.test import test_util
_BaseOptions = base_options_module.BaseOptions
_EmbeddingOptions = embedding_options_pb2.EmbeddingOptions
_SearchOptions = search_options_pb2.SearchOptions
_SearchResult = search_result_pb2.SearchResult
_NearestNeighbor = search_result_pb2.NearestNeighbor
_TextSearcher = text_searcher.TextSearcher
_TextSearcherOptions = text_searcher.TextSearcherOptions
_REGEX_EMBEDDER_MODEL = 'regex_one_embedding_with_metadata.tflite'
_REGEX_SEARCHER_MODEL = 'regex_searcher.tflite'
_REGEX_INDEX = 'regex_index.ldb'
_EXPECTED_REGEX_SEARCH_RESULT = _SearchResult(nearest_neighbors=[
_NearestNeighbor(
metadata=bytearray(b'The weather was excellent.'), distance=0.0),
_NearestNeighbor(
metadata=bytearray(b'The sun was shining on that day.'),
distance=5.7e-5),
_NearestNeighbor(
metadata=bytearray(b'The cat is chasing after the mouse.'),
distance=8.9e-5),
_NearestNeighbor(
metadata=bytearray(b'It was a sunny day.'), distance=0.000113),
_NearestNeighbor(
metadata=bytearray(b'He was very happy with his newly bought car.'),
distance=0.000119)
])
_EXPECTED_REGEX_DEFAULT_OPTIONS_SEARCH_RESULT = _SearchResult(
nearest_neighbors=[
_NearestNeighbor(
metadata=bytearray(b'The weather was excellent.'),
distance=0.889665),
_NearestNeighbor(
metadata=bytearray(b'The sun was shining on that day.'),
distance=0.889668),
_NearestNeighbor(
metadata=bytearray(b'The cat is chasing after the mouse.'),
distance=0.88967),
_NearestNeighbor(
metadata=bytearray(b'It was a sunny day.'), distance=0.889671),
_NearestNeighbor(
metadata=bytearray(b'He was very happy with his newly bought car.'),
distance=0.889672)
])
_BERT_EMBEDDER_MODEL = 'mobilebert_embedding_with_metadata.tflite'
_BERT_SEARCHER_MODEL = 'mobilebert_searcher.tflite'
_BERT_INDEX = 'mobilebert_index.ldb'
_EXPECTED_BERT_SEARCH_RESULT = _SearchResult(nearest_neighbors=[
_NearestNeighbor(
metadata=bytearray(b'The weather was excellent.'), distance=0.0),
_NearestNeighbor(
metadata=bytearray(b'It was a sunny day.'), distance=0.115369),
_NearestNeighbor(
metadata=bytearray(b'The sun was shining on that day.'),
distance=0.230017),
_NearestNeighbor(
metadata=bytearray(b'He was very happy with his newly bought car.'),
distance=0.324563),
_NearestNeighbor(
metadata=bytearray(b'The cat is chasing after the mouse.'),
distance=0.966928)
])
_USE_EMBEDDER_MODEL = 'universal_sentence_encoder_qa_with_metadata.tflite'
_USE_SEARCHER_MODEL = 'universal_sentence_encoder_searcher.tflite'
_USE_INDEX = 'universal_sentence_encoder_index.ldb'
_EXPECTED_USE_SEARCH_RESULT = _SearchResult(nearest_neighbors=[
_NearestNeighbor(
metadata=bytearray(b'The weather was excellent.'), distance=0.0),
_NearestNeighbor(
metadata=bytearray(b'It was a sunny day.'), distance=0.146359),
_NearestNeighbor(
metadata=bytearray(b'The sun was shining on that day.'),
distance=0.152225),
_NearestNeighbor(
metadata=bytearray(b'The cat is chasing after the mouse.'),
distance=0.359965),
_NearestNeighbor(
metadata=bytearray(b'He was very happy with his newly bought car.'),
distance=0.366927)
])
_MAX_RESULTS = 2
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
class IndexFileType(enum.Enum):
NONE = 1
FILE_CONTENT = 2
FILE_NAME = 3
class TextSearcherTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super().setUp()
self.embedder_model_path = test_util.get_test_data_path(
_REGEX_EMBEDDER_MODEL)
self.searcher_model_path = test_util.get_test_data_path(
_REGEX_SEARCHER_MODEL)
self.index_path = test_util.get_test_data_path(_REGEX_INDEX)
def test_create_from_file_succeeds_with_valid_embedder_and_index_paths(self):
# Creates with default option and valid model and index files successfully.
searcher = _TextSearcher.create_from_file(self.embedder_model_path,
self.index_path)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_file_succeeds_with_valid_searcher_path(self):
# Creates with default option and valid model and index files successfully.
searcher = _TextSearcher.create_from_file(self.searcher_model_path)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_succeeds_with_valid_embedder_and_index_paths(
self):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.embedder_model_path),
search_options=_SearchOptions(index_file_name=self.index_path))
searcher = _TextSearcher.create_from_options(options)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_succeeds_with_valid_searcher_path(self):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.searcher_model_path),
search_options=_SearchOptions())
searcher = _TextSearcher.create_from_options(options)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_succeeds_with_valid_embedder_content(self):
# Creates with options containing model content successfully.
with open(self.embedder_model_path, 'rb') as f:
options = _TextSearcherOptions(
base_options=_BaseOptions(file_content=f.read()),
search_options=_SearchOptions(index_file_name=self.index_path))
searcher = _TextSearcher.create_from_options(options)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_succeeds_with_valid_searcher_content(self):
# Creates with options containing model content successfully.
with open(self.searcher_model_path, 'rb') as f:
options = _TextSearcherOptions(
base_options=_BaseOptions(file_content=f.read()),
search_options=_SearchOptions(index_file_name=self.index_path))
searcher = _TextSearcher.create_from_options(options)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_succeeds_with_valid_index_content(self):
# Creates with options containing index content successfully.
with open(self.index_path, 'rb') as f:
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.embedder_model_path),
search_options=_SearchOptions(index_file_content=f.read()))
searcher = _TextSearcher.create_from_options(options)
self.assertIsInstance(searcher, _TextSearcher)
def test_create_from_options_fails_with_invalid_index_path(self):
# Invalid index path.
with self.assertRaisesRegex(
ValueError,
r'Unable to find index file: SearchOptions.index_file is not set and '
r'no AssociatedFile with type SCANN_INDEX_FILE could be found in the '
r'output tensor metadata.'):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.embedder_model_path))
_TextSearcher.create_from_options(options)
def test_create_from_options_fails_with_invalid_model_path(self):
# Invalid empty model path.
with self.assertRaisesRegex(
ValueError,
r"ExternalFile must specify at least one of 'file_content', "
r"'file_name' or 'file_descriptor_meta'."):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=''),
search_options=_SearchOptions(index_file_name=self.index_path))
_TextSearcher.create_from_options(options)
def test_create_from_options_fails_with_invalid_quantization(self):
# Invalid quantization option.
with self.assertRaisesRegex(
ValueError,
r'Setting EmbeddingOptions.quantize = true is not allowed in '
r'searchers.'):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.embedder_model_path),
embedding_options=_EmbeddingOptions(quantize=True),
search_options=_SearchOptions(index_file_name=self.index_path))
_TextSearcher.create_from_options(options)
def test_create_from_options_fails_with_invalid_max_results(self):
# Invalid max results option.
with self.assertRaisesRegex(
ValueError, r'SearchOptions.max_results must be > 0, found -1.'):
options = _TextSearcherOptions(
base_options=_BaseOptions(file_name=self.embedder_model_path),
search_options=_SearchOptions(
index_file_name=self.index_path, max_results=-1))
_TextSearcher.create_from_options(options)
def test_search_with_default_options(self):
# Create searcher.
searcher = _TextSearcher.create_from_file(self.embedder_model_path,
self.index_path)
# Perform text search.
text_search_result = searcher.search('The weather was excellent.')
self.assertProtoEquals(
text_search_result.to_pb2(),
_EXPECTED_REGEX_DEFAULT_OPTIONS_SEARCH_RESULT.to_pb2())
@parameterized.parameters(
(_REGEX_EMBEDDER_MODEL, _REGEX_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_NAME, _EXPECTED_REGEX_SEARCH_RESULT),
(_REGEX_EMBEDDER_MODEL, _REGEX_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_NAME, _EXPECTED_REGEX_SEARCH_RESULT),
(_REGEX_EMBEDDER_MODEL, _REGEX_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_CONTENT, _EXPECTED_REGEX_SEARCH_RESULT),
(_REGEX_EMBEDDER_MODEL, _REGEX_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_CONTENT, _EXPECTED_REGEX_SEARCH_RESULT),
(_REGEX_SEARCHER_MODEL, None, ModelFileType.FILE_NAME, IndexFileType.NONE,
_EXPECTED_REGEX_SEARCH_RESULT),
(_REGEX_SEARCHER_MODEL, None, ModelFileType.FILE_CONTENT,
IndexFileType.NONE, _EXPECTED_REGEX_SEARCH_RESULT),
(_BERT_EMBEDDER_MODEL, _BERT_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_NAME, _EXPECTED_BERT_SEARCH_RESULT),
(_BERT_EMBEDDER_MODEL, _BERT_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_NAME, _EXPECTED_BERT_SEARCH_RESULT),
(_BERT_EMBEDDER_MODEL, _BERT_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_CONTENT, _EXPECTED_BERT_SEARCH_RESULT),
(_BERT_EMBEDDER_MODEL, _BERT_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_CONTENT, _EXPECTED_BERT_SEARCH_RESULT),
(_BERT_SEARCHER_MODEL, None, ModelFileType.FILE_NAME, IndexFileType.NONE,
_EXPECTED_BERT_SEARCH_RESULT),
(_BERT_SEARCHER_MODEL, None, ModelFileType.FILE_CONTENT,
IndexFileType.NONE, _EXPECTED_BERT_SEARCH_RESULT),
(_USE_EMBEDDER_MODEL, _USE_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_NAME, _EXPECTED_USE_SEARCH_RESULT),
(_USE_EMBEDDER_MODEL, _USE_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_NAME, _EXPECTED_USE_SEARCH_RESULT),
(_USE_EMBEDDER_MODEL, _USE_INDEX, ModelFileType.FILE_NAME,
IndexFileType.FILE_CONTENT, _EXPECTED_USE_SEARCH_RESULT),
(_USE_EMBEDDER_MODEL, _USE_INDEX, ModelFileType.FILE_CONTENT,
IndexFileType.FILE_CONTENT, _EXPECTED_USE_SEARCH_RESULT),
(_USE_SEARCHER_MODEL, None, ModelFileType.FILE_NAME, IndexFileType.NONE,
_EXPECTED_USE_SEARCH_RESULT),
(_USE_SEARCHER_MODEL, None, ModelFileType.FILE_CONTENT,
IndexFileType.NONE, _EXPECTED_USE_SEARCH_RESULT),
)
def test_search(self, model_name, index_name, model_file_type,
index_file_type, expected_search_result):
# Create BaseOptions.
model_path = test_util.get_test_data_path(model_name)
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(file_name=model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(model_path, 'rb') as f:
model_content = f.read()
base_options = _BaseOptions(file_content=model_content)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
# Create SearchOptions.
if index_file_type is IndexFileType.NONE:
search_options = _SearchOptions()
else:
index_path = test_util.get_test_data_path(index_name)
if index_file_type is IndexFileType.FILE_NAME:
search_options = _SearchOptions(index_file_name=index_path)
elif index_file_type is IndexFileType.FILE_CONTENT:
with open(index_path, 'rb') as f:
index_content = f.read()
search_options = _SearchOptions(index_file_content=index_content)
else:
# Should never happen
raise ValueError('index_file_type is invalid.')
# Create searcher.
options = _TextSearcherOptions(
base_options, _EmbeddingOptions(l2_normalize=True, quantize=False),
search_options)
searcher = _TextSearcher.create_from_options(options)
# Perform text search.
text_search_result = searcher.search('The weather was excellent.')
# Comparing results.
self.assertProtoEquals(text_search_result.to_pb2(),
expected_search_result.to_pb2())
# Get user info and compare values.
self.assertEqual(searcher.get_user_info(), 'userinfo')
def test_max_results_option(self):
# Create searcher.
base_options = _BaseOptions(file_name=self.embedder_model_path)
search_options = _SearchOptions(
index_file_name=self.index_path, max_results=_MAX_RESULTS)
options = _TextSearcherOptions(base_options,
_EmbeddingOptions(l2_normalize=True),
search_options)
searcher = _TextSearcher.create_from_options(options)
# Perform text search.
text_search_result = searcher.search('The weather was excellent.')
nearest_neighbors = text_search_result.nearest_neighbors
self.assertLessEqual(
len(nearest_neighbors), _MAX_RESULTS, 'Too many results returned.')
if __name__ == '__main__':
tf.test.main()
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