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@@ -75,9 +75,9 @@ class TestCLIP:
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embedding = clip_encoder.predict(pil_image)
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assert clip_encoder.mode == "vision"
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- assert isinstance(embedding, list)
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- assert len(embedding) == clip_model_cfg["embed_dim"]
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- assert all([isinstance(num, float) for num in embedding])
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+ assert isinstance(embedding, np.ndarray)
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+ assert embedding.shape[0] == clip_model_cfg["embed_dim"]
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+ assert embedding.dtype == np.float32
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clip_encoder.vision_model.run.assert_called_once()
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def test_basic_text(
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@@ -97,9 +97,9 @@ class TestCLIP:
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embedding = clip_encoder.predict("test search query")
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assert clip_encoder.mode == "text"
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- assert isinstance(embedding, list)
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- assert len(embedding) == clip_model_cfg["embed_dim"]
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- assert all([isinstance(num, float) for num in embedding])
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+ assert isinstance(embedding, np.ndarray)
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+ assert embedding.shape[0] == clip_model_cfg["embed_dim"]
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+ assert embedding.dtype == np.float32
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clip_encoder.text_model.run.assert_called_once()
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@@ -133,9 +133,9 @@ class TestFaceRecognition:
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for face in faces:
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assert face["imageHeight"] == 800
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assert face["imageWidth"] == 600
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- assert isinstance(face["embedding"], list)
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- assert len(face["embedding"]) == 512
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- assert all([isinstance(num, float) for num in face["embedding"]])
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+ assert isinstance(face["embedding"], np.ndarray)
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+ assert face["embedding"].shape[0] == 512
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+ assert face["embedding"].dtype == np.float32
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det_model.detect.assert_called_once()
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assert rec_model.get_feat.call_count == num_faces
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Mert