Added Language Detector Python API and fixed a typo in Interactive Segmenter Options' docstring

mediapipe/tasks/python/test/text/BUILD

@@ -49,3 +49,18 @@ py_test(
         "//mediapipe/tasks/python/text:text_embedder",
     ],
 )
+
+py_test(
+    name = "language_detector_test",
+    srcs = ["language_detector_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/text:language_detector_model",
+    ],
+    deps = [
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:classification_result",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/text:language_detector",
+    ],
+)

mediapipe/tasks/python/test/text/language_detector_test.py

@@ -0,0 +1,225 @@
+# 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 language detector."""
+
+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 classification_result as classification_result_module
+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 language_detector
+
+LanguageDetectorResult = language_detector.LanguageDetectorResult
+LanguageDetectorPrediction = language_detector.LanguageDetectorResult.LanguageDetectorPrediction
+_BaseOptions = base_options_module.BaseOptions
+_Category = category.Category
+_Classifications = classification_result_module.Classifications
+_LanguageDetector = language_detector.LanguageDetector
+_LanguageDetectorOptions = language_detector.LanguageDetectorOptions
+
+_LANGUAGE_DETECTOR_MODEL = 'language_detector.tflite'
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
+
+_SCORE_THRESHOLD = 0.3
+_EN_TEXT = "To be, or not to be, that is the question"
+_EN_EXPECTED_RESULT = LanguageDetectorResult(
+    [
+        LanguageDetectorPrediction("en", 0.999856)
+    ]
+)
+_FR_TEXT = (
+    "Il y a beaucoup de bouches qui parlent et fort peu de têtes qui pensent."
+)
+_FR_EXPECTED_RESULT = LanguageDetectorResult(
+    [
+        LanguageDetectorPrediction("fr", 0.999781)
+    ]
+)
+_RU_TEXT = "это какой-то английский язык"
+_RU_EXPECTED_RESULT = LanguageDetectorResult(
+    [
+        LanguageDetectorPrediction("ru", 0.993362)
+    ]
+)
+_MIXED_TEXT = "分久必合合久必分"
+_MIXED_EXPECTED_RESULT = LanguageDetectorResult(
+    [
+        LanguageDetectorPrediction("zh", 0.505424),
+        LanguageDetectorPrediction("ja", 0.481617)
+    ]
+)
+_TOLERANCE = 1e-6
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class LanguageDetectorTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _LANGUAGE_DETECTOR_MODEL))
+
+  def _expect_language_detector_result_correct(
+      self,
+      actual_result: LanguageDetectorResult,
+      expect_result: LanguageDetectorResult
+  ):
+    for i, prediction in enumerate(actual_result.languages_and_scores):
+      expected_prediction = expect_result.languages_and_scores[i]
+      self.assertEqual(
+          prediction.language_code, expected_prediction.language_code,
+      )
+      self.assertAlmostEqual(
+          prediction.probability, expected_prediction.probability,
+          delta=_TOLERANCE
+      )
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _LanguageDetector.create_from_model_path(self.model_path) as detector:
+      self.assertIsInstance(detector, _LanguageDetector)
+
+  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 = _LanguageDetectorOptions(base_options=base_options)
+    with _LanguageDetector.create_from_options(options) as detector:
+      self.assertIsInstance(detector, _LanguageDetector)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    with self.assertRaisesRegex(
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite')
+      options = _LanguageDetectorOptions(base_options=base_options)
+      _LanguageDetector.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 = _LanguageDetectorOptions(base_options=base_options)
+      detector = _LanguageDetector.create_from_options(options)
+      self.assertIsInstance(detector, _LanguageDetector)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _EN_TEXT, _EN_EXPECTED_RESULT),
+      (ModelFileType.FILE_CONTENT, _EN_TEXT, _EN_EXPECTED_RESULT),
+      (ModelFileType.FILE_NAME, _FR_TEXT, _FR_EXPECTED_RESULT),
+      (ModelFileType.FILE_CONTENT, _FR_TEXT, _FR_EXPECTED_RESULT),
+      (ModelFileType.FILE_NAME, _RU_TEXT, _RU_EXPECTED_RESULT),
+      (ModelFileType.FILE_CONTENT, _RU_TEXT, _RU_EXPECTED_RESULT),
+      (ModelFileType.FILE_NAME, _MIXED_TEXT, _MIXED_EXPECTED_RESULT),
+      (ModelFileType.FILE_CONTENT, _MIXED_TEXT, _MIXED_EXPECTED_RESULT)
+  )
+  def test_detect(self, model_file_type, text, expected_result):
+    # Creates detector.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.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.')
+
+    options = _LanguageDetectorOptions(
+        base_options=base_options, score_threshold=_SCORE_THRESHOLD
+    )
+    detector = _LanguageDetector.create_from_options(options)
+
+    # Performs language detection on the input.
+    text_result = detector.detect(text)
+    # Comparing results.
+    self._expect_language_detector_result_correct(text_result, expected_result)
+    # Closes the detector explicitly when the detector is not used in
+    # a context.
+    detector.close()
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _EN_TEXT, _EN_EXPECTED_RESULT),
+      (ModelFileType.FILE_NAME, _FR_TEXT, _FR_EXPECTED_RESULT),
+      (ModelFileType.FILE_NAME, _RU_TEXT, _RU_EXPECTED_RESULT),
+      (ModelFileType.FILE_CONTENT, _MIXED_TEXT, _MIXED_EXPECTED_RESULT)
+  )
+  def test_detect_in_context(self, model_file_type, text, expected_result):
+    # Creates detector.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.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.')
+
+    options = _LanguageDetectorOptions(
+        base_options=base_options, score_threshold=_SCORE_THRESHOLD
+    )
+    with _LanguageDetector.create_from_options(options) as detector:
+      # Performs language detection on the input.
+      text_result = detector.detect(text)
+      # Comparing results.
+      self._expect_language_detector_result_correct(text_result, expected_result)
+
+  def test_allowlist_option(self):
+    # Creates detector.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _LanguageDetectorOptions(
+        base_options=base_options, score_threshold=_SCORE_THRESHOLD,
+        category_allowlist=["ja"]
+    )
+    with _LanguageDetector.create_from_options(options) as detector:
+      # Performs language detection on the input.
+      text_result = detector.detect(_MIXED_TEXT)
+      # Comparing results.
+      expected_result = LanguageDetectorResult(
+          [
+              LanguageDetectorPrediction("ja", 0.481617)
+          ]
+      )
+      self._expect_language_detector_result_correct(text_result, expected_result)
+
+  def test_denylist_option(self):
+    # Creates detector.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _LanguageDetectorOptions(
+      base_options=base_options, score_threshold=_SCORE_THRESHOLD,
+      category_denylist=["ja"]
+    )
+    with _LanguageDetector.create_from_options(options) as detector:
+      # Performs language detection on the input.
+      text_result = detector.detect(_MIXED_TEXT)
+      # Comparing results.
+      expected_result = LanguageDetectorResult(
+          [
+              LanguageDetectorPrediction("zh", 0.505424)
+          ]
+      )
+      self._expect_language_detector_result_correct(text_result, expected_result)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/text/BUILD

@@ -57,3 +57,23 @@ py_library(
         "//mediapipe/tasks/python/text/core:base_text_task_api",
     ],
 )
+
+py_library(
+    name = "language_detector",
+    srcs = [
+        "language_detector.py",
+    ],
+    visibility = ["//mediapipe/tasks:users"],
+    deps = [
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
+        "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:classification_result",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/text/core:base_text_task_api",
+    ],
+)

mediapipe/tasks/python/text/language_detector.py

@@ -0,0 +1,205 @@
+# 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.
+"""MediaPipe language detector task."""
+
+import dataclasses
+from typing import Optional, List
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
+from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2
+from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.text.core import base_text_task_api
+
+_ClassificationResult = classification_result_module.ClassificationResult
+_BaseOptions = base_options_module.BaseOptions
+_TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions
+_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_CLASSIFICATIONS_STREAM_NAME = 'classifications_out'
+_CLASSIFICATIONS_TAG = 'CLASSIFICATIONS'
+_TEXT_IN_STREAM_NAME = 'text_in'
+_TEXT_TAG = 'TEXT'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'
+
+
+@dataclasses.dataclass
+class LanguageDetectorResult:
+
+  @dataclasses.dataclass
+  class LanguageDetectorPrediction:
+    """A language code and its probability."""
+    language_code: str
+    probability: float
+
+  languages_and_scores: List[LanguageDetectorPrediction]
+
+
+def _extract_language_detector_result(
+    classification_result: classification_result_module.ClassificationResult
+) -> LanguageDetectorResult:
+  if len(classification_result.classifications) != 1:
+    raise ValueError(
+        "The LanguageDetector TextClassifierGraph should have exactly one "
+        "classification head."
+    )
+  languages_and_scores = classification_result.classifications[0]
+  language_detector_result = LanguageDetectorResult([])
+  for category in languages_and_scores.categories:
+    if category.category_name is None:
+      raise ValueError(
+          "LanguageDetector ClassificationResult has a missing language code.")
+    prediction = LanguageDetectorResult.LanguageDetectorPrediction(
+        category.category_name, category.score
+    )
+    language_detector_result.languages_and_scores.append(prediction)
+  return language_detector_result
+
+
+@dataclasses.dataclass
+class LanguageDetectorOptions:
+  """Options for the language detector task.
+
+  Attributes:
+    base_options: Base options for the language detector task.
+    display_names_locale: The locale to use for display names specified through
+      the TFLite Model Metadata.
+    max_results: The maximum number of top-scored classification results to
+      return.
+    score_threshold: Overrides the ones provided in the model metadata. Results
+      below this value are rejected.
+    category_allowlist: Allowlist of category names. If non-empty,
+      classification results whose category name is not in this set will be
+      filtered out. Duplicate or unknown category names are ignored. Mutually
+      exclusive with `category_denylist`.
+    category_denylist: Denylist of category names. If non-empty, classification
+      results whose category name is in this set will be filtered out. Duplicate
+      or unknown category names are ignored. Mutually exclusive with
+      `category_allowlist`.
+  """
+  base_options: _BaseOptions
+  display_names_locale: Optional[str] = None
+  max_results: Optional[int] = None
+  score_threshold: Optional[float] = None
+  category_allowlist: Optional[List[str]] = None
+  category_denylist: Optional[List[str]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _TextClassifierGraphOptionsProto:
+    """Generates an TextClassifierOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    classifier_options_proto = _ClassifierOptionsProto(
+        score_threshold=self.score_threshold,
+        category_allowlist=self.category_allowlist,
+        category_denylist=self.category_denylist,
+        display_names_locale=self.display_names_locale,
+        max_results=self.max_results)
+
+    return _TextClassifierGraphOptionsProto(
+        base_options=base_options_proto,
+        classifier_options=classifier_options_proto)
+
+
+class LanguageDetector(base_text_task_api.BaseTextTaskApi):
+  """Class that predicts the language of an input text.
+
+  This API expects a TFLite model with TFLite Model Metadata that contains the
+  mandatory (described below) input tensors, output tensor, and the language
+  codes in an AssociatedFile.
+
+  Input tensors:
+    (kTfLiteString)
+    - 1 input tensor that is scalar or has shape [1] containing the input
+      string.
+  Output tensor:
+    (kTfLiteFloat32)
+    - 1 output tensor of shape`[1 x N]` where `N` is the number of languages.
+  """
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'LanguageDetector':
+    """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)

mediapipe/tasks/python/vision/interactive_segmenter.py

@@ -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()

mediapipe/tasks/testdata/text/BUILD

@@ -31,6 +31,7 @@ mediapipe_files(srcs = [
     "bert_text_classifier.tflite",
     "mobilebert_embedding_with_metadata.tflite",
     "mobilebert_with_metadata.tflite",
+    "language_detector.tflite",
     "regex_one_embedding_with_metadata.tflite",
     "test_model_text_classifier_bool_output.tflite",
     "test_model_text_classifier_with_regex_tokenizer.tflite",
@@ -78,6 +79,11 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "language_detector_model",
+    srcs = ["language_detector.tflite"],
+)
+
 filegroup(
     name = "text_classifier_models",
     srcs = [