-
Notifications
You must be signed in to change notification settings - Fork 244
Expand file tree
/
Copy pathtest_proto.py
More file actions
169 lines (138 loc) · 5.43 KB
/
Copy pathtest_proto.py
File metadata and controls
169 lines (138 loc) · 5.43 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import (
AnyEmbedding,
AnyTensor,
AnyUrl,
ImageBytes,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArrayEmbedding
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp
from docarray.typing import TensorFlowEmbedding, TensorFlowTensor
@pytest.mark.proto
def test_multi_modal_doc_proto():
class MyMultiModalDoc(BaseDoc):
image: ImageDoc
text: TextDoc
doc = MyMultiModalDoc(
image=ImageDoc(tensor=np.zeros((3, 224, 224))), text=TextDoc(text='hello')
)
MyMultiModalDoc.from_protobuf(doc.to_protobuf())
@pytest.mark.proto
def test_all_types():
class NestedDoc(BaseDoc):
tensor: NdArray
class MyDoc(BaseDoc):
img_url: ImageUrl
txt_url: TextUrl
mesh_url: Mesh3DUrl
point_cloud_url: PointCloud3DUrl
any_url: AnyUrl
torch_tensor: TorchTensor
torch_tensor_param: TorchTensor[224, 224, 3]
np_array: NdArray
np_array_param: NdArray[224, 224, 3]
generic_nd_array: AnyTensor
generic_torch_tensor: AnyTensor
embedding: AnyEmbedding
torch_embedding: TorchEmbedding[128]
np_embedding: NdArrayEmbedding[128]
nested_docs: DocArray[NestedDoc]
bytes_: bytes
img_bytes: ImageBytes
doc = MyDoc(
img_url='test.png',
txt_url='test.txt',
mesh_url='test.obj',
point_cloud_url='test.obj',
any_url='www.jina.ai',
torch_tensor=torch.zeros((3, 224, 224)),
torch_tensor_param=torch.zeros((3, 224, 224)),
np_array=np.zeros((3, 224, 224)),
np_array_param=np.zeros((3, 224, 224)),
generic_nd_array=np.zeros((3, 224, 224)),
generic_torch_tensor=torch.zeros((3, 224, 224)),
embedding=np.zeros((3, 224, 224)),
torch_embedding=torch.zeros((128,)),
np_embedding=np.zeros((128,)),
nested_docs=DocArray[NestedDoc]([NestedDoc(tensor=np.zeros((128,)))]),
bytes_=b'hello',
img_bytes=b'img',
)
doc = doc.to_protobuf()
doc = MyDoc.from_protobuf(doc)
assert doc.img_url == 'test.png'
assert doc.txt_url == 'test.txt'
assert doc.mesh_url == 'test.obj'
assert doc.point_cloud_url == 'test.obj'
assert doc.any_url == 'www.jina.ai'
assert (doc.torch_tensor == torch.zeros((3, 224, 224))).all()
assert isinstance(doc.torch_tensor, torch.Tensor)
assert (doc.torch_tensor_param == torch.zeros((224, 224, 3))).all()
assert isinstance(doc.torch_tensor_param, torch.Tensor)
assert (doc.np_array == np.zeros((3, 224, 224))).all()
assert isinstance(doc.np_array, np.ndarray)
assert doc.np_array.flags.writeable
assert (doc.np_array_param == np.zeros((224, 224, 3))).all()
assert isinstance(doc.np_array_param, np.ndarray)
assert (doc.generic_nd_array == np.zeros((3, 224, 224))).all()
assert isinstance(doc.generic_nd_array, np.ndarray)
assert (doc.generic_torch_tensor == torch.zeros((3, 224, 224))).all()
assert isinstance(doc.generic_torch_tensor, torch.Tensor)
assert (doc.torch_embedding == torch.zeros((128,))).all()
assert isinstance(doc.torch_embedding, torch.Tensor)
assert (doc.np_embedding == np.zeros((128,))).all()
assert isinstance(doc.np_embedding, np.ndarray)
assert (doc.embedding == np.zeros((3, 224, 224))).all()
assert doc.bytes_ == b'hello'
assert doc.img_bytes == b'img'
@pytest.mark.tensorflow
def test_tensorflow_types():
class NestedDoc(BaseDoc):
tensor: TensorFlowTensor
class MyDoc(BaseDoc):
tf_tensor: TensorFlowTensor
tf_tensor_param: TensorFlowTensor[224, 224, 3]
generic_tf_tensor: AnyTensor
embedding: AnyEmbedding
tf_embedding: TensorFlowEmbedding[128]
nested_docs: DocArray[NestedDoc]
doc = MyDoc(
tf_tensor=tf.zeros((3, 224, 224)),
tf_tensor_param=tf.zeros((3, 224, 224)),
generic_tf_tensor=tf.zeros((3, 224, 224)),
embedding=tf.zeros((3, 224, 224)),
tf_embedding=tf.zeros((128,)),
nested_docs=DocArray[NestedDoc]([NestedDoc(tensor=tf.zeros((128,)))]),
)
doc = doc.to_protobuf()
doc = MyDoc.from_protobuf(doc)
assert tnp.allclose(doc.tf_tensor.tensor, tf.zeros((3, 224, 224)))
assert isinstance(doc.tf_tensor.tensor, tf.Tensor)
assert isinstance(doc.tf_tensor, TensorFlowTensor)
assert tnp.allclose(doc.tf_tensor_param.tensor, tf.zeros((224, 224, 3)))
assert isinstance(doc.tf_tensor_param.tensor, tf.Tensor)
assert isinstance(doc.tf_tensor_param, TensorFlowTensor)
assert tnp.allclose(doc.generic_tf_tensor.tensor, tf.zeros((3, 224, 224)))
assert isinstance(doc.generic_tf_tensor.tensor, tf.Tensor)
assert isinstance(doc.generic_tf_tensor, TensorFlowTensor)
assert tnp.allclose(doc.tf_embedding.tensor, tf.zeros((128,)))
assert isinstance(doc.tf_embedding.tensor, tf.Tensor)
assert isinstance(doc.tf_embedding, TensorFlowTensor)
assert tnp.allclose(doc.embedding.tensor, tf.zeros((3, 224, 224)))
assert isinstance(doc.embedding.tensor, tf.Tensor)
assert isinstance(doc.embedding, TensorFlowTensor)