Merge pull request #4325 from kinaryml:language-detector-python
PiperOrigin-RevId: 529213966
This commit is contained in:
Copybara-Service
commit
8c324fbd77
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@ -49,3 +49,18 @@ py_test(
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"//mediapipe/tasks/python/text:text_embedder",
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],
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)
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py_test(
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name = "language_detector_test",
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srcs = ["language_detector_test.py"],
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data = [
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"//mediapipe/tasks/testdata/text:language_detector",
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],
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deps = [
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:classification_result",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/text:language_detector",
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],
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)
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|
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228
mediapipe/tasks/python/test/text/language_detector_test.py
Normal file
228
mediapipe/tasks/python/test/text/language_detector_test.py
Normal file
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@ -0,0 +1,228 @@
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# Copyright 2023 The MediaPipe Authors.
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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 language detector."""
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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.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 language_detector
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LanguageDetectorResult = language_detector.LanguageDetectorResult
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LanguageDetectorPrediction = (
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language_detector.LanguageDetectorResult.Detection
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)
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_BaseOptions = base_options_module.BaseOptions
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_Category = category.Category
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_Classifications = classification_result_module.Classifications
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_LanguageDetector = language_detector.LanguageDetector
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_LanguageDetectorOptions = language_detector.LanguageDetectorOptions
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_LANGUAGE_DETECTOR_MODEL = "language_detector.tflite"
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_TEST_DATA_DIR = "mediapipe/tasks/testdata/text"
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_SCORE_THRESHOLD = 0.3
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_EN_TEXT = "To be, or not to be, that is the question"
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_EN_EXPECTED_RESULT = LanguageDetectorResult(
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[LanguageDetectorPrediction("en", 0.999856)]
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)
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_FR_TEXT = (
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"Il y a beaucoup de bouches qui parlent et fort peu de têtes qui pensent."
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)
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_FR_EXPECTED_RESULT = LanguageDetectorResult(
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[LanguageDetectorPrediction("fr", 0.999781)]
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)
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_RU_TEXT = "это какой-то английский язык"
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_RU_EXPECTED_RESULT = LanguageDetectorResult(
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[LanguageDetectorPrediction("ru", 0.993362)]
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)
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_MIXED_TEXT = "分久必合合久必分"
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_MIXED_EXPECTED_RESULT = LanguageDetectorResult([
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LanguageDetectorPrediction("zh", 0.505424),
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LanguageDetectorPrediction("ja", 0.481617),
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])
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_TOLERANCE = 1e-6
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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 LanguageDetectorTest(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, _LANGUAGE_DETECTOR_MODEL)
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)
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def _expect_language_detector_result_correct(
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self,
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actual_result: LanguageDetectorResult,
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expect_result: LanguageDetectorResult,
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):
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for i, prediction in enumerate(actual_result.detections):
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expected_prediction = expect_result.detections[i]
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self.assertEqual(
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prediction.language_code,
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expected_prediction.language_code,
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)
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self.assertAlmostEqual(
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prediction.probability,
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expected_prediction.probability,
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delta=_TOLERANCE,
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)
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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 _LanguageDetector.create_from_model_path(self.model_path) as detector:
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self.assertIsInstance(detector, _LanguageDetector)
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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 = _LanguageDetectorOptions(base_options=base_options)
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with _LanguageDetector.create_from_options(options) as detector:
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self.assertIsInstance(detector, _LanguageDetector)
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def test_create_from_options_fails_with_invalid_model_path(self):
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with self.assertRaisesRegex(
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RuntimeError, "Unable to open file at /path/to/invalid/model.tflite"
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):
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base_options = _BaseOptions(
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model_asset_path="/path/to/invalid/model.tflite"
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)
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options = _LanguageDetectorOptions(base_options=base_options)
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_LanguageDetector.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 = _LanguageDetectorOptions(base_options=base_options)
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detector = _LanguageDetector.create_from_options(options)
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self.assertIsInstance(detector, _LanguageDetector)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _EN_TEXT, _EN_EXPECTED_RESULT),
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(ModelFileType.FILE_CONTENT, _EN_TEXT, _EN_EXPECTED_RESULT),
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(ModelFileType.FILE_NAME, _FR_TEXT, _FR_EXPECTED_RESULT),
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(ModelFileType.FILE_CONTENT, _FR_TEXT, _FR_EXPECTED_RESULT),
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(ModelFileType.FILE_NAME, _RU_TEXT, _RU_EXPECTED_RESULT),
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(ModelFileType.FILE_CONTENT, _RU_TEXT, _RU_EXPECTED_RESULT),
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(ModelFileType.FILE_NAME, _MIXED_TEXT, _MIXED_EXPECTED_RESULT),
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(ModelFileType.FILE_CONTENT, _MIXED_TEXT, _MIXED_EXPECTED_RESULT),
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)
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def test_detect(self, model_file_type, text, expected_result):
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# Creates detector.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.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 = _LanguageDetectorOptions(
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base_options=base_options, score_threshold=_SCORE_THRESHOLD
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)
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detector = _LanguageDetector.create_from_options(options)
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# Performs language detection on the input.
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text_result = detector.detect(text)
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# Comparing results.
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self._expect_language_detector_result_correct(text_result, expected_result)
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# Closes the detector explicitly when the detector is not used in
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# a context.
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detector.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _EN_TEXT, _EN_EXPECTED_RESULT),
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(ModelFileType.FILE_NAME, _FR_TEXT, _FR_EXPECTED_RESULT),
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(ModelFileType.FILE_NAME, _RU_TEXT, _RU_EXPECTED_RESULT),
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(ModelFileType.FILE_CONTENT, _MIXED_TEXT, _MIXED_EXPECTED_RESULT),
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)
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def test_detect_in_context(self, model_file_type, text, expected_result):
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# Creates detector.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.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 = _LanguageDetectorOptions(
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base_options=base_options, score_threshold=_SCORE_THRESHOLD
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)
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with _LanguageDetector.create_from_options(options) as detector:
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# Performs language detection on the input.
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text_result = detector.detect(text)
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# Comparing results.
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self._expect_language_detector_result_correct(
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text_result, expected_result
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)
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def test_allowlist_option(self):
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# Creates detector.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _LanguageDetectorOptions(
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base_options=base_options,
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score_threshold=_SCORE_THRESHOLD,
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category_allowlist=["ja"],
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)
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with _LanguageDetector.create_from_options(options) as detector:
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# Performs language detection on the input.
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text_result = detector.detect(_MIXED_TEXT)
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# Comparing results.
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expected_result = LanguageDetectorResult(
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[LanguageDetectorPrediction("ja", 0.481617)]
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)
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self._expect_language_detector_result_correct(
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text_result, expected_result
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)
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def test_denylist_option(self):
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# Creates detector.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _LanguageDetectorOptions(
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base_options=base_options,
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score_threshold=_SCORE_THRESHOLD,
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category_denylist=["ja"],
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)
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with _LanguageDetector.create_from_options(options) as detector:
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# Performs language detection on the input.
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text_result = detector.detect(_MIXED_TEXT)
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# Comparing results.
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expected_result = LanguageDetectorResult(
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[LanguageDetectorPrediction("zh", 0.505424)]
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)
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self._expect_language_detector_result_correct(
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text_result, expected_result
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)
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if __name__ == "__main__":
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absltest.main()
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@ -57,3 +57,22 @@ py_library(
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"//mediapipe/tasks/python/text/core:base_text_task_api",
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],
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)
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py_library(
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name = "language_detector",
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srcs = [
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"language_detector.py",
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],
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deps = [
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"//mediapipe/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
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"//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
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"//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_py_pb2",
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"//mediapipe/tasks/python/components/containers:classification_result",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:task_info",
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"//mediapipe/tasks/python/text/core:base_text_task_api",
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],
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)
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|
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220
mediapipe/tasks/python/text/language_detector.py
Normal file
220
mediapipe/tasks/python/text/language_detector.py
Normal file
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@ -0,0 +1,220 @@
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# Copyright 2023 The MediaPipe Authors.
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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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"""MediaPipe language detector task."""
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import dataclasses
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from typing import List, Optional
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
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from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
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from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2
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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.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.text.core import base_text_task_api
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_ClassificationResult = classification_result_module.ClassificationResult
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_BaseOptions = base_options_module.BaseOptions
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_TextClassifierGraphOptionsProto = (
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text_classifier_graph_options_pb2.TextClassifierGraphOptions
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)
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_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
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_TaskInfo = task_info_module.TaskInfo
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_CLASSIFICATIONS_STREAM_NAME = 'classifications_out'
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_CLASSIFICATIONS_TAG = 'CLASSIFICATIONS'
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_TEXT_IN_STREAM_NAME = 'text_in'
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_TEXT_TAG = 'TEXT'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'
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@dataclasses.dataclass
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class LanguageDetectorResult:
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@dataclasses.dataclass
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class Detection:
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"""A language code and its probability."""
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# An i18n language / locale code, e.g. "en" for English, "uz" for Uzbek,
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# "ja"-Latn for Japanese (romaji).
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language_code: str
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probability: float
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detections: List[Detection]
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def _extract_language_detector_result(
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classification_result: classification_result_module.ClassificationResult,
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) -> LanguageDetectorResult:
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"""Extracts a LanguageDetectorResult from a ClassificationResult."""
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if len(classification_result.classifications) != 1:
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raise ValueError(
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'The LanguageDetector TextClassifierGraph should have exactly one '
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'classification head.'
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)
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languages_and_scores = classification_result.classifications[0]
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language_detector_result = LanguageDetectorResult([])
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for category in languages_and_scores.categories:
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if category.category_name is None:
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raise ValueError(
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'LanguageDetector ClassificationResult has a missing language code.'
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)
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prediction = LanguageDetectorResult.Detection(
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category.category_name, category.score
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)
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language_detector_result.detections.append(prediction)
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return language_detector_result
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@dataclasses.dataclass
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class LanguageDetectorOptions:
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"""Options for the language detector task.
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Attributes:
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base_options: Base options for the language detector task.
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display_names_locale: The locale to use for display names specified through
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the TFLite Model Metadata.
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max_results: The maximum number of top-scored classification results to
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return.
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score_threshold: Overrides the ones provided in the model metadata. Results
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below this value are rejected.
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category_allowlist: Allowlist of category names. If non-empty,
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classification results whose category name is not in this set will be
|
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filtered out. Duplicate or unknown category names are ignored. Mutually
|
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exclusive with `category_denylist`.
|
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category_denylist: Denylist of category names. If non-empty, classification
|
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results whose category name is in this set will be filtered out. Duplicate
|
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or unknown category names are ignored. Mutually exclusive with
|
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`category_allowlist`.
|
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"""
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base_options: _BaseOptions
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display_names_locale: Optional[str] = None
|
||||
max_results: Optional[int] = None
|
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score_threshold: Optional[float] = None
|
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category_allowlist: Optional[List[str]] = None
|
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category_denylist: Optional[List[str]] = None
|
||||
|
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _TextClassifierGraphOptionsProto:
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"""Generates an TextClassifierOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
|
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classifier_options_proto = _ClassifierOptionsProto(
|
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score_threshold=self.score_threshold,
|
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category_allowlist=self.category_allowlist,
|
||||
category_denylist=self.category_denylist,
|
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display_names_locale=self.display_names_locale,
|
||||
max_results=self.max_results,
|
||||
)
|
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|
||||
return _TextClassifierGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
classifier_options=classifier_options_proto,
|
||||
)
|
||||
|
||||
|
||||
class LanguageDetector(base_text_task_api.BaseTextTaskApi):
|
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"""Class that predicts the language of an input text.
|
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|
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This API expects a TFLite model with TFLite Model Metadata that contains the
|
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mandatory (described below) input tensors, output tensor, and the language
|
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codes in an AssociatedFile.
|
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|
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Input tensors:
|
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(kTfLiteString)
|
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- 1 input tensor that is scalar or has shape [1] containing the input
|
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string.
|
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Output tensor:
|
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(kTfLiteFloat32)
|
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- 1 output tensor of shape`[1 x N]` where `N` is the number of languages.
|
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"""
|
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|
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'LanguageDetector':
|
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"""Creates an `LanguageDetector` object from a TensorFlow Lite model and the default `LanguageDetectorOptions`.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`LanguageDetector` object that's created from the model file and the
|
||||
default `LanguageDetectorOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `LanguageDetector` object from the
|
||||
provided
|
||||
file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = LanguageDetectorOptions(base_options=base_options)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: LanguageDetectorOptions
|
||||
) -> 'LanguageDetector':
|
||||
"""Creates the `LanguageDetector` object from language detector options.
|
||||
|
||||
Args:
|
||||
options: Options for the language detector task.
|
||||
|
||||
Returns:
|
||||
`LanguageDetector` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `LanguageDetector` object from
|
||||
`LanguageDetectorOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[':'.join([_TEXT_TAG, _TEXT_IN_STREAM_NAME])],
|
||||
output_streams=[
|
||||
':'.join([_CLASSIFICATIONS_TAG, _CLASSIFICATIONS_STREAM_NAME])
|
||||
],
|
||||
task_options=options,
|
||||
)
|
||||
return cls(task_info.generate_graph_config())
|
||||
|
||||
def detect(self, text: str) -> LanguageDetectorResult:
|
||||
"""Predicts the language of the input `text`.
|
||||
|
||||
Args:
|
||||
text: The input text.
|
||||
|
||||
Returns:
|
||||
A `LanguageDetectorResult` object that contains a list of languages and
|
||||
scores.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If language detection failed to run.
|
||||
"""
|
||||
output_packets = self._runner.process(
|
||||
{_TEXT_IN_STREAM_NAME: packet_creator.create_string(text)}
|
||||
)
|
||||
|
||||
classification_result_proto = classifications_pb2.ClassificationResult()
|
||||
classification_result_proto.CopyFrom(
|
||||
packet_getter.get_proto(output_packets[_CLASSIFICATIONS_STREAM_NAME])
|
||||
)
|
||||
|
||||
classification_result = _ClassificationResult.create_from_pb2(
|
||||
classification_result_proto
|
||||
)
|
||||
return _extract_language_detector_result(classification_result)
|
||||
|
|
@ -88,7 +88,7 @@ class InteractiveSegmenterOptions:
|
|||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
|
||||
"""Generates an InteractiveSegmenterOptions protobuf object."""
|
||||
"""Generates an ImageSegmenterGraphOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
base_options_proto.use_stream_mode = False
|
||||
segmenter_options_proto = _SegmenterOptionsProto()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user