Merge pull request #3846 from kinaryml:text-embedder-python
PiperOrigin-RevId: 488198025
This commit is contained in:
commit
0dfa91a166
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@ -93,12 +93,13 @@ cc_library(
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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] + select({
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] + select({
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# TODO: Build text_classifier_graph on Windows.
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# TODO: Build text_classifier_graph and text_embedder_graph on Windows.
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# TODO: Build audio_classifier_graph on Windows.
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# TODO: Build audio_classifier_graph on Windows.
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"//mediapipe:windows": [],
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"//mediapipe:windows": [],
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"//conditions:default": [
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"//conditions:default": [
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"//mediapipe/tasks/cc/audio/audio_classifier:audio_classifier_graph",
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"//mediapipe/tasks/cc/audio/audio_classifier:audio_classifier_graph",
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"//mediapipe/tasks/cc/text/text_classifier:text_classifier_graph",
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"//mediapipe/tasks/cc/text/text_classifier:text_classifier_graph",
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"//mediapipe/tasks/cc/text/text_embedder:text_embedder_graph",
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],
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],
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}),
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}),
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)
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)
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@ -26,42 +26,6 @@ _EmbeddingProto = embeddings_pb2.Embedding
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_EmbeddingResultProto = embeddings_pb2.EmbeddingResult
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_EmbeddingResultProto = embeddings_pb2.EmbeddingResult
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@dataclasses.dataclass
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class FloatEmbedding:
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"""Defines a dense floating-point embedding.
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Attributes:
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values: A NumPy array indicating the raw output of the embedding layer.
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"""
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values: np.ndarray
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding':
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"""Creates a `FloatEmbedding` object from the given protobuf object."""
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return FloatEmbedding(values=np.array(pb2_obj.values, dtype=float))
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@dataclasses.dataclass
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class QuantizedEmbedding:
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"""Defines a dense scalar-quantized embedding.
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Attributes:
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values: A NumPy array indicating the raw output of the embedding layer.
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"""
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values: np.ndarray
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _QuantizedEmbeddingProto) -> 'QuantizedEmbedding':
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"""Creates a `QuantizedEmbedding` object from the given protobuf object."""
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return QuantizedEmbedding(
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values=np.array(bytearray(pb2_obj.values), dtype=np.uint8))
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@dataclasses.dataclass
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@dataclasses.dataclass
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class Embedding:
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class Embedding:
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"""Embedding result for a given embedder head.
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"""Embedding result for a given embedder head.
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@ -87,7 +51,7 @@ class Embedding:
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bytearray(pb2_obj.quantized_embedding.values))
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bytearray(pb2_obj.quantized_embedding.values))
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float_embedding = np.array(pb2_obj.float_embedding.values, dtype=float)
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float_embedding = np.array(pb2_obj.float_embedding.values, dtype=float)
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if not quantized_embedding:
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if not pb2_obj.quantized_embedding.values:
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return Embedding(
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return Embedding(
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embedding=float_embedding,
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embedding=float_embedding,
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head_index=pb2_obj.head_index,
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head_index=pb2_obj.head_index,
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@ -34,3 +34,19 @@ py_test(
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"//mediapipe/tasks/python/text:text_classifier",
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"//mediapipe/tasks/python/text:text_classifier",
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],
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],
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)
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)
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py_test(
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name = "text_embedder_test",
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srcs = ["text_embedder_test.py"],
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data = [
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"//mediapipe/tasks/testdata/text:mobilebert_embedding_model",
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"//mediapipe/tasks/testdata/text:regex_embedding_with_metadata",
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],
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deps = [
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"//mediapipe/tasks/python/components/containers:embedding_result",
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"//mediapipe/tasks/python/components/processors:embedder_options",
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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:text_embedder",
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],
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)
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203
mediapipe/tasks/python/test/text/text_embedder_test.py
Normal file
203
mediapipe/tasks/python/test/text/text_embedder_test.py
Normal file
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@ -0,0 +1,203 @@
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# 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 embedder."""
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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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import numpy as np
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from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
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from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_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 text_embedder
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_BaseOptions = base_options_module.BaseOptions
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_EmbedderOptions = embedder_options_module.EmbedderOptions
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_Embedding = embedding_result_module.Embedding
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_TextEmbedder = text_embedder.TextEmbedder
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_TextEmbedderOptions = text_embedder.TextEmbedderOptions
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_BERT_MODEL_FILE = 'mobilebert_embedding_with_metadata.tflite'
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_REGEX_MODEL_FILE = 'regex_one_embedding_with_metadata.tflite'
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
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# Tolerance for embedding vector coordinate values.
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_EPSILON = 1e-4
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# Tolerance for cosine similarity evaluation.
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_SIMILARITY_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 TextEmbedderTest(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 _TextEmbedder.create_from_model_path(self.model_path) as embedder:
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self.assertIsInstance(embedder, _TextEmbedder)
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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 = _TextEmbedderOptions(base_options=base_options)
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with _TextEmbedder.create_from_options(options) as embedder:
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self.assertIsInstance(embedder, _TextEmbedder)
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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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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite')
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options = _TextEmbedderOptions(base_options=base_options)
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_TextEmbedder.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 = _TextEmbedderOptions(base_options=base_options)
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embedder = _TextEmbedder.create_from_options(options)
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self.assertIsInstance(embedder, _TextEmbedder)
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def _check_embedding_value(self, result, expected_first_value):
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# Check embedding first value.
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self.assertAlmostEqual(
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result.embeddings[0].embedding[0], expected_first_value, delta=_EPSILON)
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def _check_embedding_size(self, result, quantize, expected_embedding_size):
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# Check embedding size.
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self.assertLen(result.embeddings, 1)
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embedding_result = result.embeddings[0]
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self.assertLen(embedding_result.embedding, expected_embedding_size)
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if quantize:
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self.assertEqual(embedding_result.embedding.dtype, np.uint8)
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else:
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self.assertEqual(embedding_result.embedding.dtype, float)
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def _check_cosine_similarity(self, result0, result1, expected_similarity):
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# Checks cosine similarity.
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similarity = _TextEmbedder.cosine_similarity(result0.embeddings[0],
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result1.embeddings[0])
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self.assertAlmostEqual(
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similarity, expected_similarity, delta=_SIMILARITY_TOLERANCE)
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@parameterized.parameters(
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(False, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME, 0.969514, 512,
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(19.9016, 22.626251)),
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(True, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME, 0.969514, 512,
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(0.0585837, 0.0723035)),
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(False, False, _REGEX_MODEL_FILE, ModelFileType.FILE_NAME, 0.999937, 16,
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(0.0309356, 0.0312863)),
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(True, False, _REGEX_MODEL_FILE, ModelFileType.FILE_CONTENT, 0.999937, 16,
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(0.549632, 0.552879)),
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)
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def test_embed(self, l2_normalize, quantize, model_name, model_file_type,
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expected_similarity, expected_size, expected_first_values):
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# Creates embedder.
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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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embedder_options = _EmbedderOptions(
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l2_normalize=l2_normalize, quantize=quantize)
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options = _TextEmbedderOptions(
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base_options=base_options, embedder_options=embedder_options)
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embedder = _TextEmbedder.create_from_options(options)
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# Extracts both embeddings.
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positive_text0 = "it's a charming and often affecting journey"
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positive_text1 = 'what a great and fantastic trip'
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result0 = embedder.embed(positive_text0)
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result1 = embedder.embed(positive_text1)
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# Checks embeddings and cosine similarity.
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expected_result0_value, expected_result1_value = expected_first_values
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self._check_embedding_size(result0, quantize, expected_size)
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self._check_embedding_size(result1, quantize, expected_size)
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self._check_embedding_value(result0, expected_result0_value)
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self._check_embedding_value(result1, expected_result1_value)
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self._check_cosine_similarity(result0, result1, expected_similarity)
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# Closes the embedder explicitly when the embedder is not used in
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# a context.
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embedder.close()
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@parameterized.parameters(
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(False, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME, 0.969514, 512,
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(19.9016, 22.626251)),
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(True, False, _BERT_MODEL_FILE, ModelFileType.FILE_NAME, 0.969514, 512,
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(0.0585837, 0.0723035)),
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(False, False, _REGEX_MODEL_FILE, ModelFileType.FILE_NAME, 0.999937, 16,
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(0.0309356, 0.0312863)),
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(True, False, _REGEX_MODEL_FILE, ModelFileType.FILE_CONTENT, 0.999937, 16,
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(0.549632, 0.552879)),
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)
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def test_embed_in_context(self, l2_normalize, quantize, model_name,
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model_file_type, expected_similarity, expected_size,
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expected_first_values):
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# Creates embedder.
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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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embedder_options = _EmbedderOptions(
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l2_normalize=l2_normalize, quantize=quantize)
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options = _TextEmbedderOptions(
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base_options=base_options, embedder_options=embedder_options)
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with _TextEmbedder.create_from_options(options) as embedder:
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# Extracts both embeddings.
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positive_text0 = "it's a charming and often affecting journey"
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positive_text1 = 'what a great and fantastic trip'
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result0 = embedder.embed(positive_text0)
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result1 = embedder.embed(positive_text1)
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# Checks embeddings and cosine similarity.
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expected_result0_value, expected_result1_value = expected_first_values
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self._check_embedding_size(result0, quantize, expected_size)
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self._check_embedding_size(result1, quantize, expected_size)
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self._check_embedding_value(result0, expected_result0_value)
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self._check_embedding_value(result1, expected_result1_value)
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self._check_cosine_similarity(result0, result1, expected_similarity)
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if __name__ == '__main__':
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absltest.main()
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@ -31,16 +31,14 @@ from mediapipe.tasks.python.vision import image_embedder
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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|
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ImageEmbedderResult = embedding_result_module.EmbeddingResult
|
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_Rect = rect.Rect
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_Rect = rect.Rect
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_BaseOptions = base_options_module.BaseOptions
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_BaseOptions = base_options_module.BaseOptions
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_EmbedderOptions = embedder_options_module.EmbedderOptions
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_EmbedderOptions = embedder_options_module.EmbedderOptions
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_FloatEmbedding = embedding_result_module.FloatEmbedding
|
|
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_QuantizedEmbedding = embedding_result_module.QuantizedEmbedding
|
|
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_Embedding = embedding_result_module.Embedding
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_Embedding = embedding_result_module.Embedding
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_Image = image_module.Image
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_Image = image_module.Image
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_ImageEmbedder = image_embedder.ImageEmbedder
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_ImageEmbedder = image_embedder.ImageEmbedder
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_ImageEmbedderOptions = image_embedder.ImageEmbedderOptions
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_ImageEmbedderOptions = image_embedder.ImageEmbedderOptions
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_ImageEmbedderResult = image_embedder.ImageEmbedderResult
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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|
|
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|
@ -345,7 +343,7 @@ class ImageEmbedderTest(parameterized.TestCase):
|
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|
|
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observed_timestamp_ms = -1
|
observed_timestamp_ms = -1
|
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|
|
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def check_result(result: ImageEmbedderResult, output_image: _Image,
|
def check_result(result: _ImageEmbedderResult, output_image: _Image,
|
||||||
timestamp_ms: int):
|
timestamp_ms: int):
|
||||||
# Checks cosine similarity.
|
# Checks cosine similarity.
|
||||||
self._check_cosine_similarity(
|
self._check_cosine_similarity(
|
||||||
|
@ -377,7 +375,7 @@ class ImageEmbedderTest(parameterized.TestCase):
|
||||||
image_processing_options = _ImageProcessingOptions(roi)
|
image_processing_options = _ImageProcessingOptions(roi)
|
||||||
observed_timestamp_ms = -1
|
observed_timestamp_ms = -1
|
||||||
|
|
||||||
def check_result(result: ImageEmbedderResult, output_image: _Image,
|
def check_result(result: _ImageEmbedderResult, output_image: _Image,
|
||||||
timestamp_ms: int):
|
timestamp_ms: int):
|
||||||
# Checks cosine similarity.
|
# Checks cosine similarity.
|
||||||
self._check_cosine_similarity(
|
self._check_cosine_similarity(
|
||||||
|
|
|
@ -36,3 +36,23 @@ py_library(
|
||||||
"//mediapipe/tasks/python/text/core:base_text_task_api",
|
"//mediapipe/tasks/python/text/core:base_text_task_api",
|
||||||
],
|
],
|
||||||
)
|
)
|
||||||
|
|
||||||
|
py_library(
|
||||||
|
name = "text_embedder",
|
||||||
|
srcs = [
|
||||||
|
"text_embedder.py",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:packet_creator",
|
||||||
|
"//mediapipe/python:packet_getter",
|
||||||
|
"//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
|
||||||
|
"//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_py_pb2",
|
||||||
|
"//mediapipe/tasks/python/components/containers:embedding_result",
|
||||||
|
"//mediapipe/tasks/python/components/processors:embedder_options",
|
||||||
|
"//mediapipe/tasks/python/components/utils:cosine_similarity",
|
||||||
|
"//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",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
158
mediapipe/tasks/python/text/text_embedder.py
Normal file
158
mediapipe/tasks/python/text/text_embedder.py
Normal file
|
@ -0,0 +1,158 @@
|
||||||
|
# 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 mediapipe.python import packet_creator
|
||||||
|
from mediapipe.python import packet_getter
|
||||||
|
from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
|
||||||
|
from mediapipe.tasks.cc.text.text_embedder.proto import text_embedder_graph_options_pb2
|
||||||
|
from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
|
||||||
|
from mediapipe.tasks.python.components.processors import embedder_options
|
||||||
|
from mediapipe.tasks.python.components.utils import cosine_similarity
|
||||||
|
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'
|
||||||
|
|
||||||
|
|
||||||
|
@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) -> 'TextEmbedder':
|
||||||
|
"""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)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def cosine_similarity(cls, u: embedding_result_module.Embedding,
|
||||||
|
v: embedding_result_module.Embedding) -> float:
|
||||||
|
"""Utility function to compute cosine similarity 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)
|
Loading…
Reference in New Issue
Block a user