Added files needed for the text embedder's implementation and tests

mediapipe/python/BUILD

@@ -98,6 +98,7 @@ cc_library(
         "//conditions:default": [
             "//mediapipe/tasks/cc/audio/audio_classifier:audio_classifier_graph",
             "//mediapipe/tasks/cc/text/text_classifier:text_classifier_graph",
+            "//mediapipe/tasks/cc/text/text_embedder:text_embedder_graph",
         ],
     }),
 )

mediapipe/tasks/python/components/containers/BUILD

@@ -104,3 +104,12 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )
+
+py_library(
+    name = "embedding_result",
+    srcs = ["embedding_result.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

mediapipe/tasks/python/components/containers/embedding_result.py

@@ -0,0 +1,210 @@
+# 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.
+"""Embeddings data class."""
+
+import dataclasses
+from typing import Any, Optional, List
+
+import numpy as np
+from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_FloatEmbeddingProto = embeddings_pb2.FloatEmbedding
+_QuantizedEmbeddingProto = embeddings_pb2.QuantizedEmbedding
+_EmbeddingProto = embeddings_pb2.Embedding
+_EmbeddingResultProto = embeddings_pb2.EmbeddingResult
+
+
+@dataclasses.dataclass
+class FloatEmbedding:
+  """Defines a dense floating-point embedding.
+
+  Attributes:
+    values: A NumPy array indicating the raw output of the embedding layer.
+  """
+
+  values: np.ndarray
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FloatEmbeddingProto:
+    """Generates a FloatEmbedding protobuf object."""
+    return _FloatEmbeddingProto(values=self.values)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding':
+    """Creates a `FloatEmbedding` object from the given protobuf object."""
+    return FloatEmbedding(values=np.array(pb2_obj.value_float, dtype=float))
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, FloatEmbedding):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class QuantizedEmbedding:
+  """Defines a dense scalar-quantized embedding.
+
+  Attributes:
+    values: A NumPy array indicating the raw output of the embedding layer.
+  """
+
+  values: np.ndarray
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _QuantizedEmbeddingProto:
+    """Generates a QuantizedEmbedding protobuf object."""
+    return _QuantizedEmbeddingProto(values=self.values)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _QuantizedEmbeddingProto) -> 'QuantizedEmbedding':
+    """Creates a `QuantizedEmbedding` object from the given protobuf object."""
+    return QuantizedEmbedding(
+        values=np.array(bytearray(pb2_obj.value_string), dtype=np.uint8))
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, QuantizedEmbedding):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class Embedding:
+  """Embedding result for a given embedder head.
+
+  Attributes:
+    embedding: The actual embedding, either floating-point or scalar-quantized.
+    head_index: The index of the embedder head that produced this embedding.
+      This is useful for multi-head models.
+    head_name: The name of the embedder head, which is the corresponding tensor
+      metadata name (if any). This is useful for multi-head models.
+  """
+
+  embedding: np.ndarray
+  head_index: Optional[int] = None
+  head_name: Optional[str] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbeddingProto:
+    """Generates a Embedding protobuf object."""
+
+    if self.embedding.dtype == float:
+      return _EmbeddingProto(float_embedding=self.embedding,
+                             head_index=self.head_index,
+                             head_name=self.head_name)
+
+    elif self.embedding.dtype == np.uint8:
+      return _EmbeddingProto(quantized_embedding=bytes(self.embedding),
+                             head_index=self.head_index,
+                             head_name=self.head_name)
+
+    else:
+      raise ValueError("Invalid dtype. Only float and np.uint8 are supported.")
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _EmbeddingProto) -> 'Embedding':
+    """Creates a `Embedding` object from the given protobuf object."""
+
+    quantized_embedding = np.array(
+        bytearray(pb2_obj.quantized_embedding.values))
+    float_embedding = np.array(pb2_obj.float_embedding.values, dtype=float)
+
+    if len(quantized_embedding) == 0:
+      return Embedding(embedding=float_embedding,
+                       head_index=pb2_obj.head_index,
+                       head_name=pb2_obj.head_name)
+    else:
+      return Embedding(embedding=quantized_embedding,
+                       head_index=pb2_obj.head_index,
+                       head_name=pb2_obj.head_name)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Embedding):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class EmbeddingResult:
+  """Embedding results for a given embedder model.
+  Attributes:
+    embeddings: A list of `Embedding` objects.
+    timestamp_ms: The optional timestamp (in milliseconds) of the start of the
+      chunk of data corresponding to these results. This is only used for
+      embedding extraction on time series (e.g. audio embedding). In these use
+      cases, the amount of data to process might exceed the maximum size that
+      the model can process: to solve this, the input data is split into
+      multiple chunks starting at different timestamps.
+  """
+
+  embeddings: List[Embedding]
+  timestamp_ms: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbeddingResultProto:
+    """Generates a EmbeddingResult protobuf object."""
+    return _EmbeddingResultProto(
+        embeddings=[
+            embedding.to_pb2() for embedding in self.embeddings
+        ])
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult':
+    """Creates a `EmbeddingResult` object from the given protobuf object."""
+    return EmbeddingResult(
+        embeddings=[
+            Embedding.create_from_pb2(embedding)
+            for embedding in pb2_obj.embeddings
+        ])
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, EmbeddingResult):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/processors/BUILD

@@ -28,3 +28,12 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )
+
+py_library(
+    name = "embedder_options",
+    srcs = ["embedder_options.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

mediapipe/tasks/python/components/processors/embedder_options.py

@@ -0,0 +1,70 @@
+# 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.
+"""Embedder options data class."""
+
+import dataclasses
+from typing import Any, Optional
+
+from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
+
+
+@dataclasses.dataclass
+class EmbedderOptions:
+  """Shared options used by all embedding extraction tasks.
+
+  Attributes:
+    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
+      Use this option only if the model does not already contain a native
+      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
+      L2 norm is thus achieved through TF Lite inference.
+    quantize: Whether the returned embedding should be quantized to bytes via
+      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
+      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
+      the l2_normalize option if this is not the case.
+  """
+
+  l2_normalize: Optional[bool] = None
+  quantize: Optional[bool] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbedderOptionsProto:
+    """Generates a EmbedderOptions protobuf object."""
+    return _EmbedderOptionsProto(
+        l2_normalize=self.l2_normalize,
+        quantize=self.quantize)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _EmbedderOptionsProto) -> 'EmbedderOptions':
+    """Creates a `EmbedderOptions` object from the given protobuf object."""
+    return EmbedderOptions(
+        l2_normalize=pb2_obj.l2_normalize,
+        quantize=pb2_obj.quantize)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, EmbedderOptions):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/utils/BUILD

@@ -0,0 +1,28 @@
+# 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.
+
+# Placeholder for internal Python strict library compatibility macro.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+py_library(
+    name = "cosine_similarity",
+    srcs = ["cosine_similarity.py"],
+    deps = [
+        "//mediapipe/tasks/python/components/containers:embedding_result",
+        "//mediapipe/tasks/python/components/processors:embedder_options",
+    ],
+)

mediapipe/tasks/python/components/utils/__init__.py

@@ -0,0 +1,13 @@
+# 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/tasks/python/components/utils/cosine_similarity.py

@@ -0,0 +1,61 @@
+# 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.
+"""Cosine similarity utilities."""
+
+import numpy as np
+
+from mediapipe.tasks.python.components.containers import embedding_result
+from mediapipe.tasks.python.components.processors import embedder_options
+
+_Embedding = embedding_result.Embedding
+_EmbedderOptions = embedder_options.EmbedderOptions
+
+
+def _compute_cosine_similarity(u, v):
+  if len(u.embedding) <= 0:
+    raise ValueError("Cannot compute cosing similarity on empty embeddings.")
+
+  norm_u = np.linalg.norm(u.embedding)
+  norm_v = np.linalg.norm(v.embedding)
+
+  if norm_u <= 0 or norm_v <= 0:
+    raise ValueError(
+        "Cannot compute cosine similarity on embedding with 0 norm.")
+
+  return np.dot(u.embedding, v.embedding.T) / (norm_u * norm_v)
+
+
+def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
+  """Utility function to compute cosine similarity between two embedding.
+  May return an InvalidArgumentError if e.g. the feature vectors are of
+  different types (quantized vs. float), have different sizes, or have an
+  L2-norm of 0.
+
+  Args:
+    u: An embedding.
+    v: An embedding.
+  """
+  if len(u.embedding) != len(v.embedding):
+    raise ValueError(f"Cannot compute cosine similarity between embeddings "
+                     f"of different sizes "
+                     f"({len(u.embedding)} vs. {len(v.embedding)}).")
+
+  if u.embedding.dtype == float and v.embedding.dtype == float:
+    return _compute_cosine_similarity(u, v)
+
+  if u.embedding.dtype == np.uint8 and v.embedding.dtype == np.uint8:
+    return _compute_cosine_similarity(u, v)
+
+  raise ValueError("Cannot compute cosine similarity between quantized and "
+                   "float embeddings.")

mediapipe/tasks/python/test/text/BUILD

@@ -34,3 +34,20 @@ py_test(
         "//mediapipe/tasks/python/text:text_classifier",
     ],
 )
+
+py_test(
+    name = "text_embedder_test",
+    srcs = ["text_embedder_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/text:mobilebert_embedding_model",
+        "//mediapipe/tasks/testdata/text:regex_embedding_with_metadata",
+    ],
+    deps = [
+        "//mediapipe/tasks/python/components/processors:embedder_options",
+        "//mediapipe/tasks/python/components/utils:cosine_similarity",
+        "//mediapipe/tasks/python/components/containers:embedding_result",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/text:text_embedder",
+    ],
+)

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

@@ -0,0 +1,207 @@
+# 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 embedder."""
+
+import enum
+import os
+from unittest import mock
+
+import numpy as np
+from absl.testing import absltest
+from absl.testing import parameterized
+
+from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
+from mediapipe.tasks.python.components.containers import embedding_result as embedding_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 text_embedder
+
+ImageEmbedderResult = embedding_result_module.EmbeddingResult
+_BaseOptions = base_options_module.BaseOptions
+_EmbedderOptions = embedder_options_module.EmbedderOptions
+_FloatEmbedding = embedding_result_module.FloatEmbedding
+_QuantizedEmbedding = embedding_result_module.QuantizedEmbedding
+_Embedding = embedding_result_module.Embedding
+_TextEmbedder = text_embedder.TextEmbedder
+_TextEmbedderOptions = text_embedder.TextEmbedderOptions
+
+_BERT_MODEL_FILE = 'mobilebert_embedding_with_metadata.tflite'
+_REGEX_MODEL_FILE = 'regex_one_embedding_with_metadata.tflite'
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
+# Tolerance for embedding vector coordinate values.
+_EPSILON = 1e-4
+# Tolerance for cosine similarity evaluation.
+_SIMILARITY_TOLERANCE = 1e-6
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageEmbedderTest(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 _TextEmbedder.create_from_model_path(self.model_path) as embedder:
+      self.assertIsInstance(embedder, _TextEmbedder)
+
+  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 = _TextEmbedderOptions(base_options=base_options)
+    with _TextEmbedder.create_from_options(options) as embedder:
+      self.assertIsInstance(embedder, _TextEmbedder)
+
+  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 = _TextEmbedderOptions(base_options=base_options)
+      _TextEmbedder.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 = _TextEmbedderOptions(base_options=base_options)
+      embedder = _TextEmbedder.create_from_options(options)
+      self.assertIsInstance(embedder, _TextEmbedder)
+
+  def _check_embedding_value(self, result, expected_first_value):
+    # Check embedding first value.
+    self.assertAlmostEqual(result.embeddings[0].embedding[0],
+                           expected_first_value, delta=_EPSILON)
+
+  def _check_embedding_size(self, result, quantize, expected_embedding_size):
+    # Check embedding size.
+    self.assertLen(result.embeddings, 1)
+    embedding_result = result.embeddings[0]
+    self.assertLen(embedding_result.embedding, expected_embedding_size)
+    if quantize:
+      self.assertEqual(embedding_result.embedding.dtype, np.uint8)
+    else:
+      self.assertEqual(embedding_result.embedding.dtype, float)
+
+  def _check_cosine_similarity(self, result0, result1, expected_similarity):
+    # Checks cosine similarity.
+    similarity = _TextEmbedder.cosine_similarity(
+        result0.embeddings[0], result1.embeddings[0])
+    self.assertAlmostEqual(similarity, expected_similarity,
+                           delta=_SIMILARITY_TOLERANCE)
+
+  @parameterized.parameters(
+      (False, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.969514, 512, (19.9016, 22.626251)),
+      (True, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.969514, 512, (0.0585837, 0.0723035)),
+      (False, False, _REGEX_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.999937, 16, (0.0309356, 0.0312863)),
+      (True, False, _REGEX_MODEL_FILE, ModelFileType.FILE_CONTENT,
+       0.999937, 16, (0.549632, 0.552879)),
+  )
+  def test_embed(self, l2_normalize, quantize, model_name, model_file_type,
+                 expected_similarity, expected_size, expected_first_values):
+    # Creates embedder.
+    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.')
+
+    embedder_options = _EmbedderOptions(l2_normalize=l2_normalize,
+                                        quantize=quantize)
+    options = _TextEmbedderOptions(
+        base_options=base_options, embedder_options=embedder_options)
+    embedder = _TextEmbedder.create_from_options(options)
+
+    # Extracts both embeddings.
+    positive_text0 = "it's a charming and often affecting journey"
+    positive_text1 = "what a great and fantastic trip"
+
+    result0 = embedder.embed(positive_text0)
+    result1 = embedder.embed(positive_text1)
+
+    # Checks embeddings and cosine similarity.
+    expected_result0_value, expected_result1_value = expected_first_values
+    self._check_embedding_size(result0, quantize, expected_size)
+    self._check_embedding_size(result1, quantize, expected_size)
+    self._check_embedding_value(result0, expected_result0_value)
+    self._check_embedding_value(result1, expected_result1_value)
+    self._check_cosine_similarity(result0, result1, expected_similarity)
+    # Closes the embedder explicitly when the embedder is not used in
+    # a context.
+    embedder.close()
+
+  @parameterized.parameters(
+      (False, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.969514, 512, (19.9016, 22.626251)),
+      (True, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.969514, 512, (0.0585837, 0.0723035)),
+      (False, False, _REGEX_MODEL_FILE, ModelFileType.FILE_NAME,
+       0.999937, 16, (0.0309356, 0.0312863)),
+      (True, False, _REGEX_MODEL_FILE, ModelFileType.FILE_CONTENT,
+       0.999937, 16, (0.549632, 0.552879)),
+  )
+  def test_embed_in_context(self, l2_normalize, quantize, model_name,
+                            model_file_type, expected_similarity,
+                            expected_size, expected_first_values):
+    # Creates embedder.
+    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.')
+
+    embedder_options = _EmbedderOptions(l2_normalize=l2_normalize,
+                                        quantize=quantize)
+    options = _TextEmbedderOptions(
+      base_options=base_options, embedder_options=embedder_options)
+    with _TextEmbedder.create_from_options(options) as embedder:
+      # Extracts both embeddings.
+      positive_text0 = "it's a charming and often affecting journey"
+      positive_text1 = "what a great and fantastic trip"
+
+      result0 = embedder.embed(positive_text0)
+      result1 = embedder.embed(positive_text1)
+
+      # Checks embeddings and cosine similarity.
+      expected_result0_value, expected_result1_value = expected_first_values
+      self._check_embedding_size(result0, quantize, expected_size)
+      self._check_embedding_size(result1, quantize, expected_size)
+      self._check_embedding_value(result0, expected_result0_value)
+      self._check_embedding_value(result1, expected_result1_value)
+      self._check_cosine_similarity(result0, result1, expected_similarity)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/text/BUILD

@@ -36,3 +36,23 @@ py_library(
         "//mediapipe/tasks/python/text/core:base_text_task_api",
     ],
 )
+
+py_library(
+    name = "text_embedder",
+    srcs = [
+        "text_embedder.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_py_pb2",
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/python/components/containers:embedding_result",
+        "//mediapipe/tasks/python/components/processors:embedder_options",
+        "//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/text_embedder.py

@@ -0,0 +1,166 @@
+# 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 text embedder task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.tasks.cc.text.text_embedder.proto import text_embedder_graph_options_pb2
+from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.python.components.processors import embedder_options
+from mediapipe.tasks.python.components.utils import cosine_similarity
+from mediapipe.tasks.python.components.containers import embedding_result as embedding_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
+
+TextEmbedderResult = embedding_result_module.EmbeddingResult
+_BaseOptions = base_options_module.BaseOptions
+_TextEmbedderGraphOptionsProto = text_embedder_graph_options_pb2.TextEmbedderGraphOptions
+_EmbedderOptions = embedder_options.EmbedderOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_EMBEDDINGS_OUT_STREAM_NAME = 'embeddings_out'
+_EMBEDDINGS_TAG = 'EMBEDDINGS'
+_TEXT_IN_STREAM_NAME = 'text_in'
+_TEXT_TAG = 'TEXT'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_embedder.TextEmbedderGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+@dataclasses.dataclass
+class TextEmbedderOptions:
+  """Options for the text embedder task.
+
+  Attributes:
+    base_options: Base options for the text embedder task.
+    embedder_options: Options for the text embedder task.
+  """
+  base_options: _BaseOptions
+  embedder_options: _EmbedderOptions = _EmbedderOptions()
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _TextEmbedderGraphOptionsProto:
+    """Generates an TextEmbedderOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    embedder_options_proto = self.embedder_options.to_pb2()
+
+    return _TextEmbedderGraphOptionsProto(
+        base_options=base_options_proto,
+        embedder_options=embedder_options_proto
+    )
+
+
+class TextEmbedder(base_text_task_api.BaseTextTaskApi):
+  """Class that performs embedding extraction on text."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'ImageEmbedder':
+    """Creates an `TextEmbedder` object from a TensorFlow Lite model and the
+      default `TextEmbedderOptions`.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `TextEmbedder` object that's created from the model file and the default
+      `TextEmbedderOptions`.
+
+    Raises:
+      ValueError: If failed to create `TextEmbedder` 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 = TextEmbedderOptions(base_options=base_options)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: TextEmbedderOptions) -> 'TextEmbedder':
+    """Creates the `TextEmbedder` object from text embedder options.
+
+    Args:
+      options: Options for the text embedder task.
+
+    Returns:
+      `TextEmbedder` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `TextEmbedder` object from
+        `TextEmbedderOptions` 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([
+          _EMBEDDINGS_TAG,
+          _EMBEDDINGS_OUT_STREAM_NAME
+        ])
+      ],
+      task_options=options)
+    return cls(task_info.generate_graph_config())
+
+  def embed(
+      self,
+      text: str,
+  ) -> TextEmbedderResult:
+    """Performs text embedding extraction on the provided text.
+
+    Args:
+      text: The input text.
+
+    Returns:
+      An embedding result object that contains a list of embeddings.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If text embedder failed to run.
+    """
+    output_packets = self._runner.process(
+        {_TEXT_IN_STREAM_NAME: packet_creator.create_string(text)})
+
+    embedding_result_proto = embeddings_pb2.EmbeddingResult()
+    embedding_result_proto.CopyFrom(
+      packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME]))
+
+    return TextEmbedderResult.create_from_pb2(embedding_result_proto)
+
+  @staticmethod
+  def cosine_similarity(u: embedding_result_module.Embedding,
+                        v: embedding_result_module.Embedding) -> float:
+    """Utility function to compute cosine similarity [1] between two embedding
+    entries. May return an InvalidArgumentError if e.g. the feature vectors are
+    of different types (quantized vs. float), have different sizes, or have a
+    an L2-norm of 0.
+
+    Args:
+      u: An embedding entry.
+      v: An embedding entry.
+
+    Returns:
+      The cosine similarity for the two embeddings.
+
+    Raises:
+      ValueError: May return an error if e.g. the feature vectors are of
+        different types (quantized vs. float), have different sizes, or have
+        an L2-norm of 0
+    """
+    return cosine_similarity.cosine_similarity(u, v)