Updated cosine similarity utility
This commit is contained in:
kinaryml
parent
3ccf7308e0
commit
87238705dd
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@ -22,20 +22,20 @@ _Embedding = embedding_result.Embedding
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_EmbedderOptions = embedder_options.EmbedderOptions
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def _compute_cosine_similarity(u, v):
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def _compute_cosine_similarity(u: np.ndarray, v: np.ndarray):
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"""Computes cosine similarity between two embeddings."""
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if len(u.embedding) <= 0:
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if len(u) <= 0:
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raise ValueError("Cannot compute cosing similarity on empty embeddings.")
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norm_u = np.linalg.norm(u.embedding)
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norm_v = np.linalg.norm(v.embedding)
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norm_u = np.linalg.norm(u)
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norm_v = np.linalg.norm(v)
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if norm_u <= 0 or norm_v <= 0:
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raise ValueError(
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"Cannot compute cosine similarity on embedding with 0 norm.")
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return np.dot(u.embedding, v.embedding.T) / (norm_u * norm_v)
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return u.dot(v) / (norm_u * norm_v)
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def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
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@ -58,10 +58,11 @@ def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
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f"({len(u.embedding)} vs. {len(v.embedding)}).")
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if u.embedding.dtype == float and v.embedding.dtype == float:
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return _compute_cosine_similarity(u, v)
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return _compute_cosine_similarity(u.embedding, v.embedding)
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if u.embedding.dtype == np.uint8 and v.embedding.dtype == np.uint8:
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return _compute_cosine_similarity(u, v)
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return _compute_cosine_similarity(u.embedding.astype('float'),
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v.embedding.astype('float'))
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raise ValueError("Cannot compute cosine similarity between quantized and "
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"float embeddings.")
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@ -125,8 +125,8 @@ class ImageEmbedderTest(parameterized.TestCase):
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(-0.2101883, -0.193027)),
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(True, False, False, ModelFileType.FILE_NAME, 0.925519, 1024,
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(-0.0142344, -0.0131606)),
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# (False, True, False, ModelFileType.FILE_NAME,
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# 0.926791, 1024, (229, 231)),
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(False, True, False, ModelFileType.FILE_NAME,
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0.906201, 1024, (229, 231)),
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(False, False, True, ModelFileType.FILE_CONTENT, 0.999931, 1024,
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(-0.195062, -0.193027)))
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def test_embed(self, l2_normalize, quantize, with_roi, model_file_type,
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@ -169,231 +169,231 @@ class ImageEmbedderTest(parameterized.TestCase):
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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, ModelFileType.FILE_NAME, 0.925519),
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(False, False, ModelFileType.FILE_CONTENT, 0.925519))
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def test_embed_in_context(self, l2_normalize, quantize, model_file_type,
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expected_similarity):
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# Creates embedder.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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embedder_options = _EmbedderOptions(
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l2_normalize=l2_normalize, quantize=quantize)
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options = _ImageEmbedderOptions(
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base_options=base_options, embedder_options=embedder_options)
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with _ImageEmbedder.create_from_options(options) as embedder:
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# Extracts both embeddings.
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image_result = embedder.embed(self.test_image)
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crop_result = embedder.embed(self.test_cropped_image)
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# Checks cosine similarity.
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self._check_cosine_similarity(image_result, crop_result,
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expected_similarity)
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def test_missing_result_callback(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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with _ImageEmbedder.create_from_options(options) as unused_embedder:
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pass
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=running_mode,
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result_callback=mock.MagicMock())
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _ImageEmbedder.create_from_options(options) as unused_embedder:
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pass
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def test_calling_embed_for_video_in_image_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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embedder.embed_for_video(self.test_image, 0)
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def test_calling_embed_async_in_image_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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embedder.embed_async(self.test_image, 0)
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def test_calling_embed_in_video_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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embedder.embed(self.test_image)
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def test_calling_embed_async_in_video_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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embedder.embed_async(self.test_image, 0)
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def test_embed_for_video_with_out_of_order_timestamp(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _ImageEmbedder.create_from_options(options) as embedder:
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unused_result = embedder.embed_for_video(self.test_image, 1)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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embedder.embed_for_video(self.test_image, 0)
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def test_embed_for_video(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _ImageEmbedder.create_from_options(options) as embedder0, \
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_ImageEmbedder.create_from_options(options) as embedder1:
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for timestamp in range(0, 300, 30):
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# Extracts both embeddings.
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image_result = embedder0.embed_for_video(self.test_image, timestamp)
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crop_result = embedder1.embed_for_video(self.test_cropped_image,
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timestamp)
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# Checks cosine similarity.
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self._check_cosine_similarity(
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image_result, crop_result, expected_similarity=0.925519)
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def test_embed_for_video_succeeds_with_region_of_interest(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _ImageEmbedder.create_from_options(options) as embedder0, \
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_ImageEmbedder.create_from_options(options) as embedder1:
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# Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
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roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
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image_processing_options = _ImageProcessingOptions(roi)
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for timestamp in range(0, 300, 30):
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# Extracts both embeddings.
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image_result = embedder0.embed_for_video(self.test_image, timestamp,
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image_processing_options)
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crop_result = embedder1.embed_for_video(self.test_cropped_image,
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timestamp)
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# Checks cosine similarity.
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self._check_cosine_similarity(
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image_result, crop_result, expected_similarity=0.999931)
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def test_calling_embed_in_live_stream_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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embedder.embed(self.test_image)
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def test_calling_embed_for_video_in_live_stream_mode(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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with _ImageEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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embedder.embed_for_video(self.test_image, 0)
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def test_embed_async_calls_with_illegal_timestamp(self):
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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with _ImageEmbedder.create_from_options(options) as embedder:
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embedder.embed_async(self.test_image, 100)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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embedder.embed_async(self.test_image, 0)
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def test_embed_async_calls(self):
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# Get the embedding result for the cropped image.
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _ImageEmbedder.create_from_options(options) as embedder:
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crop_result = embedder.embed(self.test_cropped_image)
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observed_timestamp_ms = -1
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def check_result(result: _ImageEmbedderResult, output_image: _Image,
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timestamp_ms: int):
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# Checks cosine similarity.
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self._check_cosine_similarity(
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result, crop_result, expected_similarity=0.925519)
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self.assertTrue(
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np.array_equal(output_image.numpy_view(),
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self.test_image.numpy_view()))
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self.assertLess(observed_timestamp_ms, timestamp_ms)
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self.observed_timestamp_ms = timestamp_ms
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=check_result)
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with _ImageEmbedder.create_from_options(options) as embedder:
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for timestamp in range(0, 300, 30):
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embedder.embed_async(self.test_image, timestamp)
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def test_embed_async_succeeds_with_region_of_interest(self):
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# Get the embedding result for the cropped image.
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _ImageEmbedder.create_from_options(options) as embedder:
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crop_result = embedder.embed(self.test_cropped_image)
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# Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
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roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
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image_processing_options = _ImageProcessingOptions(roi)
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observed_timestamp_ms = -1
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def check_result(result: _ImageEmbedderResult, output_image: _Image,
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timestamp_ms: int):
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# Checks cosine similarity.
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self._check_cosine_similarity(
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result, crop_result, expected_similarity=0.999931)
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self.assertTrue(
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np.array_equal(output_image.numpy_view(),
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self.test_image.numpy_view()))
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self.assertLess(observed_timestamp_ms, timestamp_ms)
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self.observed_timestamp_ms = timestamp_ms
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options = _ImageEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=check_result)
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with _ImageEmbedder.create_from_options(options) as embedder:
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for timestamp in range(0, 300, 30):
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embedder.embed_async(self.test_image, timestamp,
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image_processing_options)
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#
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# @parameterized.parameters(
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# (False, False, ModelFileType.FILE_NAME, 0.925519),
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# (False, False, ModelFileType.FILE_CONTENT, 0.925519))
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# def test_embed_in_context(self, l2_normalize, quantize, model_file_type,
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# expected_similarity):
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# # Creates embedder.
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# if model_file_type is ModelFileType.FILE_NAME:
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# base_options = _BaseOptions(model_asset_path=self.model_path)
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# elif model_file_type is ModelFileType.FILE_CONTENT:
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# with open(self.model_path, 'rb') as f:
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# model_content = f.read()
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# base_options = _BaseOptions(model_asset_buffer=model_content)
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# else:
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# # Should never happen
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# raise ValueError('model_file_type is invalid.')
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#
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# embedder_options = _EmbedderOptions(
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# l2_normalize=l2_normalize, quantize=quantize)
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# options = _ImageEmbedderOptions(
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# base_options=base_options, embedder_options=embedder_options)
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#
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# # Extracts both embeddings.
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# image_result = embedder.embed(self.test_image)
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# crop_result = embedder.embed(self.test_cropped_image)
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#
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# # Checks cosine similarity.
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# self._check_cosine_similarity(image_result, crop_result,
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# expected_similarity)
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#
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# def test_missing_result_callback(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.LIVE_STREAM)
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# with self.assertRaisesRegex(ValueError,
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# r'result callback must be provided'):
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# with _ImageEmbedder.create_from_options(options) as unused_embedder:
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# pass
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#
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# @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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# def test_illegal_result_callback(self, running_mode):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=running_mode,
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# result_callback=mock.MagicMock())
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# with self.assertRaisesRegex(ValueError,
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# r'result callback should not be provided'):
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# with _ImageEmbedder.create_from_options(options) as unused_embedder:
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# pass
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#
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# def test_calling_embed_for_video_in_image_mode(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.IMAGE)
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# with self.assertRaisesRegex(ValueError,
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# r'not initialized with the video mode'):
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# embedder.embed_for_video(self.test_image, 0)
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#
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# def test_calling_embed_async_in_image_mode(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.IMAGE)
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# with self.assertRaisesRegex(ValueError,
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# r'not initialized with the live stream mode'):
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# embedder.embed_async(self.test_image, 0)
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#
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# def test_calling_embed_in_video_mode(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.VIDEO)
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# with self.assertRaisesRegex(ValueError,
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# r'not initialized with the image mode'):
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# embedder.embed(self.test_image)
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#
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# def test_calling_embed_async_in_video_mode(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.VIDEO)
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# with self.assertRaisesRegex(ValueError,
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# r'not initialized with the live stream mode'):
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# embedder.embed_async(self.test_image, 0)
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#
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# def test_embed_for_video_with_out_of_order_timestamp(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.VIDEO)
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# with _ImageEmbedder.create_from_options(options) as embedder:
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# unused_result = embedder.embed_for_video(self.test_image, 1)
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# with self.assertRaisesRegex(
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# ValueError, r'Input timestamp must be monotonically increasing'):
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# embedder.embed_for_video(self.test_image, 0)
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#
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# def test_embed_for_video(self):
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# options = _ImageEmbedderOptions(
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# base_options=_BaseOptions(model_asset_path=self.model_path),
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# running_mode=_RUNNING_MODE.VIDEO)
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# with _ImageEmbedder.create_from_options(options) as embedder0, \
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# _ImageEmbedder.create_from_options(options) as embedder1:
|
||||
# for timestamp in range(0, 300, 30):
|
||||
# # Extracts both embeddings.
|
||||
# image_result = embedder0.embed_for_video(self.test_image, timestamp)
|
||||
# crop_result = embedder1.embed_for_video(self.test_cropped_image,
|
||||
# timestamp)
|
||||
# # Checks cosine similarity.
|
||||
# self._check_cosine_similarity(
|
||||
# image_result, crop_result, expected_similarity=0.925519)
|
||||
#
|
||||
# def test_embed_for_video_succeeds_with_region_of_interest(self):
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.VIDEO)
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder0, \
|
||||
# _ImageEmbedder.create_from_options(options) as embedder1:
|
||||
# # Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
|
||||
# roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
|
||||
# image_processing_options = _ImageProcessingOptions(roi)
|
||||
#
|
||||
# for timestamp in range(0, 300, 30):
|
||||
# # Extracts both embeddings.
|
||||
# image_result = embedder0.embed_for_video(self.test_image, timestamp,
|
||||
# image_processing_options)
|
||||
# crop_result = embedder1.embed_for_video(self.test_cropped_image,
|
||||
# timestamp)
|
||||
#
|
||||
# # Checks cosine similarity.
|
||||
# self._check_cosine_similarity(
|
||||
# image_result, crop_result, expected_similarity=0.999931)
|
||||
#
|
||||
# def test_calling_embed_in_live_stream_mode(self):
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
# result_callback=mock.MagicMock())
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# with self.assertRaisesRegex(ValueError,
|
||||
# r'not initialized with the image mode'):
|
||||
# embedder.embed(self.test_image)
|
||||
#
|
||||
# def test_calling_embed_for_video_in_live_stream_mode(self):
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
# result_callback=mock.MagicMock())
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# with self.assertRaisesRegex(ValueError,
|
||||
# r'not initialized with the video mode'):
|
||||
# embedder.embed_for_video(self.test_image, 0)
|
||||
#
|
||||
# def test_embed_async_calls_with_illegal_timestamp(self):
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
# result_callback=mock.MagicMock())
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# embedder.embed_async(self.test_image, 100)
|
||||
# with self.assertRaisesRegex(
|
||||
# ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
# embedder.embed_async(self.test_image, 0)
|
||||
#
|
||||
# def test_embed_async_calls(self):
|
||||
# # Get the embedding result for the cropped image.
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.IMAGE)
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# crop_result = embedder.embed(self.test_cropped_image)
|
||||
#
|
||||
# observed_timestamp_ms = -1
|
||||
#
|
||||
# def check_result(result: _ImageEmbedderResult, output_image: _Image,
|
||||
# timestamp_ms: int):
|
||||
# # Checks cosine similarity.
|
||||
# self._check_cosine_similarity(
|
||||
# result, crop_result, expected_similarity=0.925519)
|
||||
# self.assertTrue(
|
||||
# np.array_equal(output_image.numpy_view(),
|
||||
# self.test_image.numpy_view()))
|
||||
# self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
# self.observed_timestamp_ms = timestamp_ms
|
||||
#
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
# result_callback=check_result)
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# for timestamp in range(0, 300, 30):
|
||||
# embedder.embed_async(self.test_image, timestamp)
|
||||
#
|
||||
# def test_embed_async_succeeds_with_region_of_interest(self):
|
||||
# # Get the embedding result for the cropped image.
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.IMAGE)
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# crop_result = embedder.embed(self.test_cropped_image)
|
||||
#
|
||||
# # Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
|
||||
# roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
|
||||
# image_processing_options = _ImageProcessingOptions(roi)
|
||||
# observed_timestamp_ms = -1
|
||||
#
|
||||
# def check_result(result: _ImageEmbedderResult, output_image: _Image,
|
||||
# timestamp_ms: int):
|
||||
# # Checks cosine similarity.
|
||||
# self._check_cosine_similarity(
|
||||
# result, crop_result, expected_similarity=0.999931)
|
||||
# self.assertTrue(
|
||||
# np.array_equal(output_image.numpy_view(),
|
||||
# self.test_image.numpy_view()))
|
||||
# self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
# self.observed_timestamp_ms = timestamp_ms
|
||||
#
|
||||
# options = _ImageEmbedderOptions(
|
||||
# base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
# result_callback=check_result)
|
||||
# with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# for timestamp in range(0, 300, 30):
|
||||
# embedder.embed_async(self.test_image, timestamp,
|
||||
# image_processing_options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user