327 lines
11 KiB
Python
327 lines
11 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for text 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.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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_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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_USE_MODEL_FILE = 'universal_sentence_encoder_qa_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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(
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False,
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False,
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_BERT_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.969514,
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512,
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(19.9016, 22.626251),
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),
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(
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True,
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False,
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_BERT_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.969514,
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512,
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(0.0585837, 0.0723035),
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),
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(
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False,
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False,
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_REGEX_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.999937,
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16,
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(0.0309356, 0.0312863),
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),
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(
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True,
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False,
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_REGEX_MODEL_FILE,
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ModelFileType.FILE_CONTENT,
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0.999937,
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16,
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(0.549632, 0.552879),
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),
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(
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False,
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False,
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_USE_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.851961,
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100,
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(1.422951, 1.404664),
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),
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(
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True,
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False,
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_USE_MODEL_FILE,
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ModelFileType.FILE_CONTENT,
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0.851961,
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100,
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(0.127049, 0.125416),
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),
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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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options = _TextEmbedderOptions(
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base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
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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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(
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False,
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False,
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_BERT_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.969514,
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512,
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(19.9016, 22.626251),
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),
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(
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True,
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False,
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_BERT_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.969514,
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512,
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(0.0585837, 0.0723035),
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),
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(
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False,
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False,
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_REGEX_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.999937,
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16,
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(0.0309356, 0.0312863),
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),
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(
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True,
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False,
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_REGEX_MODEL_FILE,
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ModelFileType.FILE_CONTENT,
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0.999937,
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16,
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(0.549632, 0.552879),
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),
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(
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False,
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False,
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_USE_MODEL_FILE,
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ModelFileType.FILE_NAME,
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0.851961,
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100,
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(1.422951, 1.404664),
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),
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(
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True,
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False,
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_USE_MODEL_FILE,
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ModelFileType.FILE_CONTENT,
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0.851961,
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100,
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(0.127049, 0.125416),
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),
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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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options = _TextEmbedderOptions(
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base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
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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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@parameterized.parameters(
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# TODO: The similarity should likely be lower
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(_BERT_MODEL_FILE, 0.980880),
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(_USE_MODEL_FILE, 0.780334),
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)
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def test_embed_with_different_themes(self, model_file, expected_similarity):
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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_file)
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)
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base_options = _BaseOptions(model_asset_path=model_path)
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options = _TextEmbedderOptions(base_options=base_options)
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embedder = _TextEmbedder.create_from_options(options)
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# Extracts both embeddings.
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text0 = (
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'When you go to this restaurant, they hold the pancake upside-down '
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"before they hand it to you. It's a great gimmick."
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)
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result0 = embedder.embed(text0)
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text1 = "Let's make a plan to steal the declaration of independence."
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result1 = embedder.embed(text1)
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similarity = _TextEmbedder.cosine_similarity(
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result0.embeddings[0], result1.embeddings[0]
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)
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self.assertAlmostEqual(
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similarity, expected_similarity, delta=_SIMILARITY_TOLERANCE
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)
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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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if __name__ == '__main__':
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absltest.main()
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