# 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 nl_classifier."""
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 class_pb2
from tensorflow_lite_support.python.task.processor.proto import classification_options_pb2
from tensorflow_lite_support.python.task.processor.proto import classifications_pb2
from tensorflow_lite_support.python.task.text import nl_classifier
from tensorflow_lite_support.python.test import test_util
_BaseOptions = base_options_module.BaseOptions
_NLClassifier = nl_classifier.NLClassifier
_Category = class_pb2.Category
_Classifications = classifications_pb2.Classifications
_ClassificationResult = classifications_pb2.ClassificationResult
_NLClassifierOptions = nl_classifier.NLClassifierOptions
_ClassificationOptions = classification_options_pb2.ClassificationOptions
_REGEX_TOKENIZER_MODEL = 'test_model_nl_classifier_with_regex_tokenizer.tflite'
_POSITIVE_INPUT = ('This is the best movie I’ve seen in recent years. Strongly '
'recommend it!')
_EXPECTED_RESULTS_OF_POSITIVE_INPUT = _ClassificationResult(classifications=[
_Classifications(
categories=[
_Category(
index=0,
score=0.486573,
display_name='',
category_name='Negative'),
_Category(
index=0,
score=0.513427,
display_name='',
category_name='Positive'),
],
head_index=0,
head_name='')
])
_NEGATIVE_INPUT = 'What a waste of my time.'
_EXPECTED_RESULTS_OF_NEGATIVE_INPUT = _ClassificationResult(classifications=[
_Classifications(
categories=[
_Category(
index=0,
score=0.813130,
display_name='',
category_name='Negative'),
_Category(
index=0,
score=0.186870,
display_name='',
category_name='Positive'),
],
head_index=0,
head_name='')
])
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
class NLClassifierTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super().setUp()
self.model_path = test_util.get_test_data_path(_REGEX_TOKENIZER_MODEL)
def test_create_from_file_succeeds_with_valid_model_path(self):
# Creates with default option and valid model file successfully.
classifier = _NLClassifier.create_from_file(self.model_path)
self.assertIsInstance(classifier, _NLClassifier)
def test_create_from_options_succeeds_with_valid_model_path(self):
# Creates with options containing model file successfully.
base_options = _BaseOptions(file_name=self.model_path)
options = _NLClassifierOptions(base_options=base_options)
classifier = _NLClassifier.create_from_options(options)
self.assertIsInstance(classifier, _NLClassifier)
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'."):
base_options = _BaseOptions(file_name='')
options = _NLClassifierOptions(base_options=base_options)
_NLClassifier.create_from_options(options)
def test_create_from_options_succeeds_with_valid_model_content(self):
# Creates with options containing model content successfully.
with open(self.model_path, 'rb') as f:
base_options = _BaseOptions(file_content=f.read())
options = _NLClassifierOptions(base_options=base_options)
classifier = _NLClassifier.create_from_options(options)
self.assertIsInstance(classifier, _NLClassifier)
@parameterized.parameters(
# Regex tokenizer model.
(_REGEX_TOKENIZER_MODEL, ModelFileType.FILE_NAME, _POSITIVE_INPUT,
_EXPECTED_RESULTS_OF_POSITIVE_INPUT),
(_REGEX_TOKENIZER_MODEL, ModelFileType.FILE_NAME, _NEGATIVE_INPUT,
_EXPECTED_RESULTS_OF_NEGATIVE_INPUT),
(_REGEX_TOKENIZER_MODEL, ModelFileType.FILE_CONTENT, _POSITIVE_INPUT,
_EXPECTED_RESULTS_OF_POSITIVE_INPUT),
(_REGEX_TOKENIZER_MODEL, ModelFileType.FILE_CONTENT, _NEGATIVE_INPUT,
_EXPECTED_RESULTS_OF_NEGATIVE_INPUT))
def test_classify_model(self, model_name, model_file_type, text,
expected_classification_result):
# Creates classifier.
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.')
options = _NLClassifierOptions(base_options=base_options)
classifier = _NLClassifier.create_from_options(options)
# Classifies text using the given model.
text_classification_result = classifier.classify(text)
self.assertProtoEquals(text_classification_result.to_pb2(),
expected_classification_result.to_pb2())
if __name__ == '__main__':
tf.test.main()
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