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348 lines (285 loc) · 11.2 KB
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# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Dict, Optional, Union
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocList
from docarray.array import DocVec
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(BaseDoc):
tensor: TorchTensor[3, 224, 224]
batch = DocList[Image]([Image(tensor=torch.zeros(3, 224, 224)) for _ in range(10)])
return batch.to_doc_vec()
@pytest.mark.proto
def test_proto_stacked_mode_torch(batch):
batch.from_protobuf(batch.to_protobuf())
@pytest.mark.proto
def test_proto_stacked_mode_numpy():
class MyDoc(BaseDoc):
tensor: NdArray[3, 224, 224]
da = DocList[MyDoc]([MyDoc(tensor=np.zeros((3, 224, 224))) for _ in range(10)])
da = da.to_doc_vec()
da.from_protobuf(da.to_protobuf())
@pytest.mark.proto
def test_stacked_proto():
class CustomDocument(BaseDoc):
image: NdArray
da = DocList[CustomDocument](
[CustomDocument(image=np.zeros((3, 224, 224))) for _ in range(10)]
).to_doc_vec()
da2 = DocVec[CustomDocument].from_protobuf(da.to_protobuf())
assert isinstance(da2, DocVec)
assert da.doc_type == da2.doc_type
assert (da2.image == da.image).all()
@pytest.mark.proto
def test_proto_none_tensor_column():
class MyOtherDoc(BaseDoc):
embedding: Union[NdArray, None] = None
other_embedding: NdArray
third_embedding: Union[NdArray, None] = None
da = DocVec[MyOtherDoc](
[
MyOtherDoc(
other_embedding=np.random.random(512),
),
MyOtherDoc(other_embedding=np.random.random(512)),
]
)
assert da._storage.tensor_columns['embedding'] is None
assert da._storage.tensor_columns['other_embedding'] is not None
assert da._storage.tensor_columns['third_embedding'] is None
proto = da.to_protobuf()
da_after = DocVec[MyOtherDoc].from_protobuf(proto)
assert da_after._storage.tensor_columns['embedding'] is None
assert da_after._storage.tensor_columns['other_embedding'] is not None
assert (
da_after._storage.tensor_columns['other_embedding']
== da._storage.tensor_columns['other_embedding']
).all()
assert da_after._storage.tensor_columns['third_embedding'] is None
@pytest.mark.proto
def test_proto_none_doc_column():
class InnerDoc(BaseDoc):
embedding: NdArray
class MyDoc(BaseDoc):
inner: Union[InnerDoc, None] = None
other_inner: Union[InnerDoc, None] = None
da = DocVec[MyDoc](
[
MyDoc(other_inner=InnerDoc(embedding=np.random.random(512))),
MyDoc(other_inner=InnerDoc(embedding=np.random.random(512))),
]
)
assert da._storage.doc_columns['inner'] is None
assert len(da._storage.doc_columns['other_inner']) == 2
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto)
assert da_after._storage.doc_columns['inner'] is None
assert len(da._storage.doc_columns['other_inner']) == 2
assert (da.other_inner.embedding == da_after.other_inner.embedding).all()
@pytest.mark.proto
def test_proto_none_docvec_column():
class InnerDoc(BaseDoc):
embedding: NdArray
class MyDoc(BaseDoc):
inner_l: Union[DocList[InnerDoc], None] = None
inner_v: Union[DocVec[InnerDoc], None] = None
inner_exists_v: Union[DocVec[InnerDoc], None] = None
inner_exists_l: Union[DocList[InnerDoc], None] = None
def _make_inner_list():
return DocList[InnerDoc](
[
InnerDoc(embedding=np.random.random(512)),
InnerDoc(embedding=np.random.random(512)),
]
)
da = DocVec[MyDoc](
[
MyDoc(
inner_exists_l=_make_inner_list(),
inner_exists_v=_make_inner_list().to_doc_vec(),
),
MyDoc(
inner_exists_l=_make_inner_list(),
inner_exists_v=_make_inner_list().to_doc_vec(),
),
]
)
assert da._storage.docs_vec_columns['inner_l'] is None
assert da._storage.docs_vec_columns['inner_v'] is None
assert len(da._storage.docs_vec_columns['inner_exists_l']) == 2
assert len(da._storage.docs_vec_columns['inner_exists_v']) == 2
assert da.inner_exists_l[0].embedding.shape == (2, 512)
assert da.inner_exists_l[1].embedding.shape == (2, 512)
assert da.inner_exists_v[0].embedding.shape == (2, 512)
assert da.inner_exists_v[1].embedding.shape == (2, 512)
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto)
assert da_after._storage.docs_vec_columns['inner_l'] is None
assert da_after._storage.docs_vec_columns['inner_v'] is None
assert len(da._storage.docs_vec_columns['inner_exists_l']) == 2
assert len(da._storage.docs_vec_columns['inner_exists_v']) == 2
assert (
da.inner_exists_l[0].embedding == da_after.inner_exists_l[0].embedding
).all()
assert (
da.inner_exists_l[1].embedding == da_after.inner_exists_l[1].embedding
).all()
assert (
da.inner_exists_v[0].embedding == da_after.inner_exists_v[0].embedding
).all()
assert (
da.inner_exists_v[1].embedding == da_after.inner_exists_v[1].embedding
).all()
@pytest.mark.proto
def test_proto_any_column():
class MyDoc(BaseDoc):
embedding: NdArray
text: str
d: Dict
da = DocVec[MyDoc](
[
MyDoc(
embedding=np.random.random(512),
text='hi',
d={'a': 1},
),
MyDoc(embedding=np.random.random(512), text='there', d={'b': 2}),
]
)
assert da._storage.tensor_columns['embedding'].shape == (2, 512)
assert da._storage.any_columns['text'] == ['hi', 'there']
assert da._storage.any_columns['d'] == [{'a': 1}, {'b': 2}]
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto)
assert da_after.doc_type == da.doc_type
assert da._storage.tensor_columns['embedding'].shape == (2, 512)
assert (
da_after._storage.tensor_columns['embedding']
== da._storage.tensor_columns['embedding']
).all()
assert da._storage.any_columns['text'] == ['hi', 'there']
assert da._storage.any_columns['d'] == [{'a': 1}, {'b': 2}]
assert (da_after.embedding == da.embedding).all()
assert da_after.text == da.text
assert da_after.d == da.d
@pytest.mark.proto
def test_proto_none_any_column():
class MyDoc(BaseDoc):
text: Optional[str] = None
d: Optional[Dict] = None
da = DocVec[MyDoc](
[
MyDoc(),
MyDoc(),
]
)
assert da._storage.any_columns['text'] == [None, None]
assert da._storage.any_columns['d'] == [None, None]
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto)
assert da_after._storage.any_columns['text'] == [None, None]
assert da_after._storage.any_columns['d'] == [None, None]
@pytest.mark.skipif('GITHUB_WORKFLOW' in os.environ, reason='Flaky in Github')
@pytest.mark.proto
@pytest.mark.parametrize('tensor_type', [NdArray, TorchTensor])
def test_proto_tensor_type(tensor_type):
class InnerDoc(BaseDoc):
embedding: tensor_type
class MyDoc(BaseDoc):
tensor: tensor_type
inner: InnerDoc
inner_v: DocVec[InnerDoc]
def _get_rand_tens():
arr = np.random.random(512)
return tensor_type.from_ndarray(arr) if tensor_type == TorchTensor else arr
da = DocVec[MyDoc](
[
MyDoc(
tensor=_get_rand_tens(),
inner=InnerDoc(embedding=_get_rand_tens()),
inner_v=DocVec[InnerDoc]([InnerDoc(embedding=_get_rand_tens())]),
),
MyDoc(
tensor=_get_rand_tens(),
inner=InnerDoc(embedding=_get_rand_tens()),
inner_v=DocVec[InnerDoc]([InnerDoc(embedding=_get_rand_tens())]),
),
]
)
assert isinstance(da.tensor, tensor_type)
assert da.tensor.shape == (2, 512)
assert isinstance(da.inner.embedding, tensor_type)
assert da.inner.embedding.shape == (2, 512)
assert isinstance(da.inner_v[0].embedding, tensor_type)
assert da.inner_v[0].embedding.shape == (1, 512)
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto, tensor_type=tensor_type)
assert isinstance(da_after.tensor, tensor_type)
assert (da.tensor == da_after.tensor).all()
assert isinstance(da_after.inner.embedding, tensor_type)
assert (da.inner.embedding == da_after.inner.embedding).all()
assert isinstance(da_after.inner_v[0].embedding, tensor_type)
assert (da.inner_v[0].embedding == da_after.inner_v[0].embedding).all()
@pytest.mark.tensorflow
def test_proto_tensor_type_tf():
import tensorflow as tf
from docarray.typing import TensorFlowTensor
class InnerDoc(BaseDoc):
embedding: TensorFlowTensor
class MyDoc(BaseDoc):
tensor: TensorFlowTensor
inner: InnerDoc
inner_v: DocVec[InnerDoc]
def _get_rand_tens():
arr = np.random.random(512)
return TensorFlowTensor.from_ndarray(arr)
da = DocVec[MyDoc](
[
MyDoc(
tensor=_get_rand_tens(),
inner=InnerDoc(embedding=_get_rand_tens()),
inner_v=DocVec[InnerDoc]([InnerDoc(embedding=_get_rand_tens())]),
),
MyDoc(
tensor=_get_rand_tens(),
inner=InnerDoc(embedding=_get_rand_tens()),
inner_v=DocVec[InnerDoc]([InnerDoc(embedding=_get_rand_tens())]),
),
]
)
assert isinstance(da.tensor, TensorFlowTensor)
assert len(da.tensor) == 2
assert isinstance(da.inner.embedding, TensorFlowTensor)
assert len(da.inner.embedding) == 2
assert isinstance(da.inner_v[0].embedding, TensorFlowTensor)
assert len(da.inner_v[0].embedding) == 1
proto = da.to_protobuf()
da_after = DocVec[MyDoc].from_protobuf(proto, tensor_type=TensorFlowTensor)
assert isinstance(da_after.tensor, TensorFlowTensor)
assert tf.math.reduce_all(tf.equal(da.tensor.tensor, da_after.tensor.tensor))
assert isinstance(da_after.inner.embedding, TensorFlowTensor)
assert tf.math.reduce_all(
tf.equal(da.inner.embedding.tensor, da_after.inner.embedding.tensor)
)
assert isinstance(da_after.inner_v[0].embedding, TensorFlowTensor)
assert tf.math.reduce_all(
tf.equal(da.inner_v[0].embedding.tensor, da_after.inner_v[0].embedding.tensor)
)