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Copy pathtest_array_save_load.py
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122 lines (99 loc) · 3.59 KB
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import os
import numpy as np
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['pickle-array', 'protobuf-array', 'protobuf', 'pickle', 'json-array']
)
@pytest.mark.parametrize('compress', ['lz4', 'bz2', 'lzma', 'zlib', 'gzip', None])
@pytest.mark.parametrize('show_progress', [False, True])
@pytest.mark.parametrize('array_cls', [DocList, DocVec])
def test_array_save_load_binary(protocol, compress, tmp_path, show_progress, array_cls):
tmp_file = os.path.join(tmp_path, 'test')
da = array_cls[MyDoc](
[
MyDoc(
embedding=[1, 2, 3, 4, 5], text='hello', image=ImageDoc(url='aux.png')
),
MyDoc(embedding=[5, 4, 3, 2, 1], text='hello world', image=ImageDoc()),
]
)
da.save_binary(
tmp_file, protocol=protocol, compress=compress, show_progress=show_progress
)
da2 = array_cls[MyDoc].load_binary(
tmp_file, protocol=protocol, compress=compress, show_progress=show_progress
)
assert len(da2) == 2
assert len(da) == len(da2)
for d1, d2 in zip(da, da2):
assert d1.embedding.tolist() == d2.embedding.tolist()
assert d1.text == d2.text
assert d1.image.url == d2.image.url
assert da[1].image.url is None
assert da2[1].image.url is None
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['pickle-array', 'protobuf-array', 'protobuf', 'pickle', 'json-array']
)
@pytest.mark.parametrize('compress', ['lz4', 'bz2', 'lzma', 'zlib', 'gzip', None])
@pytest.mark.parametrize('show_progress', [False, True])
@pytest.mark.parametrize('to_doc_vec', [True, False])
def test_array_save_load_binary_streaming(
protocol, compress, tmp_path, show_progress, to_doc_vec
):
tmp_file = os.path.join(tmp_path, 'test')
array_cls = DocVec if to_doc_vec else DocList
da = DocList[MyDoc]()
def _extend_da(num_docs=100):
for _ in range(num_docs):
da.extend(
[
MyDoc(
embedding=np.random.rand(3, 2),
text='hello',
image=ImageDoc(url='aux.png'),
),
]
)
_extend_da()
if to_doc_vec:
da = da.to_doc_vec()
da.save_binary(
tmp_file, protocol=protocol, compress=compress, show_progress=show_progress
)
da_after = array_cls[MyDoc].load_binary(
tmp_file, protocol=protocol, compress=compress, show_progress=show_progress
)
for i, doc in enumerate(da_after):
assert doc.id == da[i].id
assert doc.text == da[i].text
assert doc.image.url == da[i].image.url
assert i == 99
@pytest.mark.parametrize('tensor_type', [NdArray, TorchTensor])
def test_save_load_tensor_type(tensor_type, tmp_path):
tmp_file = os.path.join(tmp_path, 'test123')
class MyDoc(BaseDoc):
embedding: tensor_type
text: str
image: ImageDoc
da = DocVec[MyDoc](
[
MyDoc(
embedding=[1, 2, 3, 4, 5], text='hello', image=ImageDoc(url='aux.png')
),
MyDoc(embedding=[5, 4, 3, 2, 1], text='hello world', image=ImageDoc()),
],
tensor_type=tensor_type,
)
da.save_binary(tmp_file)
da2 = DocVec[MyDoc].load_binary(tmp_file, tensor_type=tensor_type)
assert da2.tensor_type == tensor_type
assert isinstance(da2.embedding, tensor_type)