247 lines
9.1 KiB
Python
247 lines
9.1 KiB
Python
# Copyright 2022 The MediaPipe 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 classifier."""
|
||
|
||
import enum
|
||
import os
|
||
|
||
from absl.testing import absltest
|
||
from absl.testing import parameterized
|
||
|
||
|
||
from mediapipe.tasks.python.components.containers import category
|
||
from mediapipe.tasks.python.components.containers import classifications as classifications_module
|
||
from mediapipe.tasks.python.components.processors import classifier_options
|
||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||
from mediapipe.tasks.python.test import test_utils
|
||
from mediapipe.tasks.python.text import text_classifier
|
||
|
||
_BaseOptions = base_options_module.BaseOptions
|
||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||
_Category = category.Category
|
||
_ClassificationEntry = classifications_module.ClassificationEntry
|
||
_Classifications = classifications_module.Classifications
|
||
_ClassificationResult = classifications_module.ClassificationResult
|
||
_TextClassifier = text_classifier.TextClassifier
|
||
_TextClassifierOptions = text_classifier.TextClassifierOptions
|
||
|
||
_BERT_MODEL_FILE = 'bert_text_classifier.tflite'
|
||
_REGEX_MODEL_FILE = 'test_model_text_classifier_with_regex_tokenizer.tflite'
|
||
_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
|
||
|
||
_NEGATIVE_TEXT = "What a waste of my time."
|
||
_POSITIVE_TEXT = ("This is the best movie I’ve seen in recent years."
|
||
"Strongly recommend it!")
|
||
|
||
_BERT_NEGATIVE_RESULTS = _ClassificationResult(
|
||
classifications=[
|
||
_Classifications(
|
||
entries=[
|
||
_ClassificationEntry(
|
||
categories=[
|
||
_Category(
|
||
index=0, score=0.999479, display_name='',
|
||
category_name='negative'),
|
||
_Category(
|
||
index=1, score=0.00052154, display_name='',
|
||
category_name='positive')
|
||
],
|
||
timestamp_ms=0
|
||
)
|
||
],
|
||
head_index=0,
|
||
head_name='probability')
|
||
])
|
||
_BERT_POSITIVE_RESULTS = _ClassificationResult(
|
||
classifications=[
|
||
_Classifications(
|
||
entries=[
|
||
_ClassificationEntry(
|
||
categories=[
|
||
_Category(
|
||
index=1, score=0.999466, display_name='',
|
||
category_name='positive'),
|
||
_Category(
|
||
index=0, score=0.000533596, display_name='',
|
||
category_name='negative')
|
||
],
|
||
timestamp_ms=0
|
||
)
|
||
],
|
||
head_index=0,
|
||
head_name='probability')
|
||
])
|
||
_REGEX_NEGATIVE_RESULTS = _ClassificationResult(
|
||
classifications=[
|
||
_Classifications(
|
||
entries=[
|
||
_ClassificationEntry(
|
||
categories=[
|
||
_Category(
|
||
index=0, score=0.81313, display_name='',
|
||
category_name='Negative'),
|
||
_Category(
|
||
index=1, score=0.1868704, display_name='',
|
||
category_name='Positive')
|
||
],
|
||
timestamp_ms=0
|
||
)
|
||
],
|
||
head_index=0,
|
||
head_name='probability')
|
||
])
|
||
_REGEX_POSITIVE_RESULTS = _ClassificationResult(
|
||
classifications=[
|
||
_Classifications(
|
||
entries=[
|
||
_ClassificationEntry(
|
||
categories=[
|
||
_Category(
|
||
index=1, score=0.5134273, display_name='',
|
||
category_name='Positive'),
|
||
_Category(
|
||
index=0, score=0.486573, display_name='',
|
||
category_name='Negative')
|
||
],
|
||
timestamp_ms=0
|
||
)
|
||
],
|
||
head_index=0,
|
||
head_name='probability')
|
||
])
|
||
|
||
|
||
class ModelFileType(enum.Enum):
|
||
FILE_CONTENT = 1
|
||
FILE_NAME = 2
|
||
|
||
|
||
class ImageClassifierTest(parameterized.TestCase):
|
||
|
||
def setUp(self):
|
||
super().setUp()
|
||
self.model_path = test_utils.get_test_data_path(
|
||
os.path.join(_TEST_DATA_DIR, _BERT_MODEL_FILE))
|
||
|
||
def test_create_from_file_succeeds_with_valid_model_path(self):
|
||
# Creates with default option and valid model file successfully.
|
||
with _TextClassifier.create_from_model_path(self.model_path) as classifier:
|
||
self.assertIsInstance(classifier, _TextClassifier)
|
||
|
||
def test_create_from_options_succeeds_with_valid_model_path(self):
|
||
# Creates with options containing model file successfully.
|
||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||
options = _TextClassifierOptions(base_options=base_options)
|
||
with _TextClassifier.create_from_options(options) as classifier:
|
||
self.assertIsInstance(classifier, _TextClassifier)
|
||
|
||
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', 'file_pointer_meta' or 'file_descriptor_meta'."):
|
||
base_options = _BaseOptions(model_asset_path='')
|
||
options = _TextClassifierOptions(base_options=base_options)
|
||
_TextClassifier.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(model_asset_buffer=f.read())
|
||
options = _TextClassifierOptions(base_options=base_options)
|
||
classifier = _TextClassifier.create_from_options(options)
|
||
self.assertIsInstance(classifier, _TextClassifier)
|
||
|
||
@parameterized.parameters(
|
||
(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_BERT_NEGATIVE_RESULTS),
|
||
(ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_BERT_NEGATIVE_RESULTS),
|
||
(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _POSITIVE_TEXT,
|
||
_BERT_POSITIVE_RESULTS),
|
||
(ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE, _POSITIVE_TEXT,
|
||
_BERT_POSITIVE_RESULTS),
|
||
(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_REGEX_NEGATIVE_RESULTS),
|
||
(ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_REGEX_NEGATIVE_RESULTS),
|
||
(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _POSITIVE_TEXT,
|
||
_REGEX_POSITIVE_RESULTS),
|
||
(ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE, _POSITIVE_TEXT,
|
||
_REGEX_POSITIVE_RESULTS))
|
||
def test_classify(self, model_file_type, model_name, text,
|
||
expected_classification_result):
|
||
# Creates classifier.
|
||
model_path = test_utils.get_test_data_path(
|
||
os.path.join(_TEST_DATA_DIR, model_name))
|
||
if model_file_type is ModelFileType.FILE_NAME:
|
||
base_options = _BaseOptions(model_asset_path=model_path)
|
||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||
with open(model_path, 'rb') as f:
|
||
model_content = f.read()
|
||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||
else:
|
||
# Should never happen
|
||
raise ValueError('model_file_type is invalid.')
|
||
|
||
custom_classifier_options = _ClassifierOptions()
|
||
options = _TextClassifierOptions(
|
||
base_options=base_options, classifier_options=custom_classifier_options)
|
||
classifier = _TextClassifier.create_from_options(options)
|
||
|
||
# Performs text classification on the input.
|
||
text_result = classifier.classify(text)
|
||
# Comparing results.
|
||
test_utils.assert_proto_equals(self, text_result.to_pb2(),
|
||
expected_classification_result.to_pb2())
|
||
# Closes the classifier explicitly when the classifier is not used in
|
||
# a context.
|
||
classifier.close()
|
||
|
||
@parameterized.parameters(
|
||
(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_BERT_NEGATIVE_RESULTS),
|
||
(ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
|
||
_BERT_NEGATIVE_RESULTS))
|
||
def test_classify_in_context(self, model_file_type, model_name, text,
|
||
expected_classification_result):
|
||
# Creates classifier.
|
||
model_path = test_utils.get_test_data_path(
|
||
os.path.join(_TEST_DATA_DIR, model_name))
|
||
if model_file_type is ModelFileType.FILE_NAME:
|
||
base_options = _BaseOptions(model_asset_path=model_path)
|
||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||
with open(model_path, 'rb') as f:
|
||
model_content = f.read()
|
||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||
else:
|
||
# Should never happen
|
||
raise ValueError('model_file_type is invalid.')
|
||
|
||
custom_classifier_options = _ClassifierOptions()
|
||
options = _TextClassifierOptions(
|
||
base_options=base_options, classifier_options=custom_classifier_options)
|
||
|
||
with _TextClassifier.create_from_options(options) as classifier:
|
||
# Performs text classification on the input.
|
||
text_result = classifier.classify(text)
|
||
# Comparing results.
|
||
test_utils.assert_proto_equals(self, text_result.to_pb2(),
|
||
expected_classification_result.to_pb2())
|
||
|
||
|
||
if __name__ == '__main__':
|
||
absltest.main()
|