mirror of
https://github.com/qdrant/fastembed.git
synced 2026-07-23 11:20:51 -05:00
b389798d8e
* Migration of models to dataclasses * Model description file * Test fix * kw_only support * Multitask embeddings test fix * list_supported_models type fix * Dim fix for sparsemodels * Dim fix for sparsemodels * Dim fix for sparsemodels (x2) * Model management type fix * Interface docstring fixes * Mypy fixes * Typing fix again * Typing fix again * Special cast to SparseModelDescription * Special cast to SparseModelDescription * Special cast to SparseModelDescription * typing fix for colpali * typing fix for colpali * typing fix for colpali * typing fix for colpali * Let's try generic typing for ModelManagment * wip: dataclass idea, small fixes (#475) * wip: dataclass idea, small fixes * fix: fix exception message in base model description * remove custom model descriptions * make license, description and size in gb mandatory in model description * fix: introduce _list_supported_models which returns model description objects * test: add test for list supported models * fix: fix list supported models usage in tests --------- Co-authored-by: George <george.panchuk@qdrant.tech>
124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
import os
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from io import BytesIO
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import numpy as np
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import pytest
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import requests
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from PIL import Image
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from fastembed import ImageEmbedding
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from tests.config import TEST_MISC_DIR
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from tests.utils import delete_model_cache
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CANONICAL_VECTOR_VALUES = {
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"Qdrant/clip-ViT-B-32-vision": np.array([-0.0098, 0.0128, -0.0274, 0.002, -0.0059]),
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"Qdrant/resnet50-onnx": np.array(
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01046245, 0.01171397, 0.00705971, 0.0]
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),
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"Qdrant/Unicom-ViT-B-16": np.array(
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[0.0170, -0.0361, 0.0125, -0.0428, -0.0232, 0.0232, -0.0602, -0.0333, 0.0155, 0.0497]
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),
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"Qdrant/Unicom-ViT-B-32": np.array(
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[0.0418, 0.0550, 0.0003, 0.0253, -0.0185, 0.0016, -0.0368, -0.0402, -0.0891, -0.0186]
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),
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"jinaai/jina-clip-v1": np.array(
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[-0.029, 0.0216, 0.0396, 0.0283, -0.0023, 0.0151, 0.011, -0.0235, 0.0251, -0.0343]
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),
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}
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def test_embedding() -> None:
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is_ci = os.getenv("CI")
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for model_desc in ImageEmbedding._list_supported_models():
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if not is_ci and model_desc.size_in_GB > 1:
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continue
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dim = model_desc.dim
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model = ImageEmbedding(model_name=model_desc.model)
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images = [
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TEST_MISC_DIR / "image.jpeg",
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str(TEST_MISC_DIR / "small_image.jpeg"),
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Image.open((TEST_MISC_DIR / "small_image.jpeg")),
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Image.open(BytesIO(requests.get("https://qdrant.tech/img/logo.png").content)),
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]
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embeddings = list(model.embed(images))
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embeddings = np.stack(embeddings, axis=0)
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assert embeddings.shape == (len(images), dim)
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canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
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assert np.allclose(
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embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
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), model_desc.model
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assert np.allclose(embeddings[1], embeddings[2]), model_desc.model
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if is_ci:
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delete_model_cache(model.model._model_dir)
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@pytest.mark.parametrize("n_dims,model_name", [(512, "Qdrant/clip-ViT-B-32-vision")])
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def test_batch_embedding(n_dims: int, model_name: str) -> None:
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is_ci = os.getenv("CI")
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model = ImageEmbedding(model_name=model_name)
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n_images = 32
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test_images = [
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TEST_MISC_DIR / "image.jpeg",
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str(TEST_MISC_DIR / "small_image.jpeg"),
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Image.open(TEST_MISC_DIR / "small_image.jpeg"),
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]
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images = test_images * n_images
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embeddings = list(model.embed(images, batch_size=10))
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embeddings = np.stack(embeddings, axis=0)
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assert embeddings.shape == (len(test_images) * n_images, n_dims)
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if is_ci:
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delete_model_cache(model.model._model_dir)
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@pytest.mark.parametrize("n_dims,model_name", [(512, "Qdrant/clip-ViT-B-32-vision")])
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def test_parallel_processing(n_dims: int, model_name: str) -> None:
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is_ci = os.getenv("CI")
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model = ImageEmbedding(model_name=model_name)
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n_images = 32
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test_images = [
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TEST_MISC_DIR / "image.jpeg",
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str(TEST_MISC_DIR / "small_image.jpeg"),
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Image.open(TEST_MISC_DIR / "small_image.jpeg"),
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]
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images = test_images * n_images
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embeddings = list(model.embed(images, batch_size=10, parallel=2))
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embeddings = np.stack(embeddings, axis=0)
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embeddings_2 = list(model.embed(images, batch_size=10, parallel=None))
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embeddings_2 = np.stack(embeddings_2, axis=0)
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embeddings_3 = list(model.embed(images, batch_size=10, parallel=0))
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embeddings_3 = np.stack(embeddings_3, axis=0)
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assert embeddings.shape == (n_images * len(test_images), n_dims)
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assert np.allclose(embeddings, embeddings_2, atol=1e-3)
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assert np.allclose(embeddings, embeddings_3, atol=1e-3)
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if is_ci:
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delete_model_cache(model.model._model_dir)
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@pytest.mark.parametrize("model_name", ["Qdrant/clip-ViT-B-32-vision"])
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def test_lazy_load(model_name: str) -> None:
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is_ci = os.getenv("CI")
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model = ImageEmbedding(model_name=model_name, lazy_load=True)
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assert not hasattr(model.model, "model")
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images = [
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TEST_MISC_DIR / "image.jpeg",
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str(TEST_MISC_DIR / "small_image.jpeg"),
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]
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list(model.embed(images))
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assert hasattr(model.model, "model")
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if is_ci:
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delete_model_cache(model.model._model_dir)
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