229 lines
8.8 KiB
Python
229 lines
8.8 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for text classifier."""
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import enum
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import os
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from absl.testing import absltest
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from absl.testing import parameterized
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from mediapipe.tasks.python.components.containers import category
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from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
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from mediapipe.tasks.python.components.processors import classifier_options
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.text import text_classifier
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TextClassifierResult = classification_result_module.ClassificationResult
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_BaseOptions = base_options_module.BaseOptions
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_ClassifierOptions = classifier_options.ClassifierOptions
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_Category = category.Category
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_Classifications = classification_result_module.Classifications
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_TextClassifier = text_classifier.TextClassifier
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_TextClassifierOptions = text_classifier.TextClassifierOptions
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_BERT_MODEL_FILE = 'bert_text_classifier.tflite'
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_REGEX_MODEL_FILE = 'test_model_text_classifier_with_regex_tokenizer.tflite'
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
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_NEGATIVE_TEXT = 'What a waste of my time.'
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_POSITIVE_TEXT = ('This is the best movie I’ve seen in recent years.'
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'Strongly recommend it!')
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_BERT_NEGATIVE_RESULTS = TextClassifierResult(classifications=[
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_Classifications(categories=[
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_Category(
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index=0,
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score=0.999479,
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display_name='',
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category_name='negative'),
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_Category(
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index=1,
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score=0.00052154,
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display_name='',
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category_name='positive')
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],
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head_index=0,
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head_name='probability')
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],
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timestamp_ms=0)
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_BERT_POSITIVE_RESULTS = TextClassifierResult(classifications=[
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_Classifications(categories=[
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_Category(
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index=1,
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score=0.999466,
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display_name='',
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category_name='positive'),
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_Category(
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index=0,
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score=0.000533596,
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display_name='',
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category_name='negative')
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],
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head_index=0,
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head_name='probability')
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],
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timestamp_ms=0)
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_REGEX_NEGATIVE_RESULTS = TextClassifierResult(classifications=[
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_Classifications(categories=[
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_Category(
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index=0,
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score=0.81313,
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display_name='',
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category_name='Negative'),
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_Category(
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index=1,
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score=0.1868704,
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display_name='',
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category_name='Positive')
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],
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head_index=0,
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head_name='probability')
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],
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timestamp_ms=0)
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_REGEX_POSITIVE_RESULTS = TextClassifierResult(classifications=[
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_Classifications(categories=[
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_Category(
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index=1,
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score=0.5134273,
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display_name='',
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category_name='Positive'),
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_Category(
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index=0,
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score=0.486573,
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display_name='',
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category_name='Negative')
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],
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head_index=0,
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head_name='probability')
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],
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timestamp_ms=0)
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class ImageClassifierTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _BERT_MODEL_FILE))
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _TextClassifier.create_from_model_path(self.model_path) as classifier:
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self.assertIsInstance(classifier, _TextClassifier)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _TextClassifierOptions(base_options=base_options)
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with _TextClassifier.create_from_options(options) as classifier:
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self.assertIsInstance(classifier, _TextClassifier)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(model_asset_path='')
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options = _TextClassifierOptions(base_options=base_options)
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_TextClassifier.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _TextClassifierOptions(base_options=base_options)
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classifier = _TextClassifier.create_from_options(options)
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self.assertIsInstance(classifier, _TextClassifier)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
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_BERT_NEGATIVE_RESULTS), (ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
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_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS),
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(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _POSITIVE_TEXT,
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_BERT_POSITIVE_RESULTS), (ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
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_POSITIVE_TEXT, _BERT_POSITIVE_RESULTS),
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(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _NEGATIVE_TEXT,
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_REGEX_NEGATIVE_RESULTS), (ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE,
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_NEGATIVE_TEXT, _REGEX_NEGATIVE_RESULTS),
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(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _POSITIVE_TEXT,
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_REGEX_POSITIVE_RESULTS), (ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE,
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_POSITIVE_TEXT, _REGEX_POSITIVE_RESULTS))
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def test_classify(self, model_file_type, model_name, text,
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expected_classification_result):
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# Creates classifier.
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model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, model_name))
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _TextClassifierOptions(base_options=base_options)
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classifier = _TextClassifier.create_from_options(options)
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# Performs text classification on the input.
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text_result = classifier.classify(text)
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# Comparing results.
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test_utils.assert_proto_equals(self, text_result.to_pb2(),
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expected_classification_result.to_pb2())
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# Closes the classifier explicitly when the classifier is not used in
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# a context.
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classifier.close()
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@parameterized.parameters((ModelFileType.FILE_NAME, _BERT_MODEL_FILE,
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_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS),
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(ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
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_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS))
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def test_classify_in_context(self, model_file_type, model_name, text,
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expected_classification_result):
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# Creates classifier.
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model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, model_name))
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _TextClassifierOptions(base_options=base_options)
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with _TextClassifier.create_from_options(options) as classifier:
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# Performs text classification on the input.
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text_result = classifier.classify(text)
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# Comparing results.
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test_utils.assert_proto_equals(self, text_result.to_pb2(),
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expected_classification_result.to_pb2())
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if __name__ == '__main__':
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absltest.main()
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