docarray · samsja · Jan 27, 2023 · Jan 26, 2023 · Jan 26, 2023 · Jan 26, 2023
diff --git a/docarray/documents/audio.py b/docarray/documents/audio.py
@@ -1,9 +1,19 @@
-from typing import Optional, TypeVar
+from typing import Any, Optional, Type, TypeVar, Union
+
+import numpy as np
 
 from docarray.base_document import BaseDocument
 from docarray.typing import AnyEmbedding, AudioUrl
+from docarray.typing.tensor.abstract_tensor import AbstractTensor
 from docarray.typing.tensor.audio.audio_tensor import AudioTensor
 
+try:
+    import torch
+
+    torch_available = True
+except ImportError:
+    torch_available = False
+
 T = TypeVar('T', bound='Audio')
 
 
@@ -76,3 +86,17 @@ class MultiModalDoc(Document):
     url: Optional[AudioUrl]
     tensor: Optional[AudioTensor]
     embedding: Optional[AnyEmbedding]
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, AbstractTensor, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(url=value)
+        elif isinstance(value, (AbstractTensor, np.ndarray)) or (
+            torch_available and isinstance(value, torch.Tensor)
+        ):
+            value = cls(tensor=value)
+
+        return super().validate(value)
diff --git a/docarray/documents/image.py b/docarray/documents/image.py
@@ -1,7 +1,19 @@
-from typing import Optional
+from typing import Any, Optional, Type, TypeVar, Union
+
+import numpy as np
 
 from docarray.base_document import BaseDocument
 from docarray.typing import AnyEmbedding, AnyTensor, ImageUrl
+from docarray.typing.tensor.abstract_tensor import AbstractTensor
+
+T = TypeVar('T', bound='Image')
+
+try:
+    import torch
+
+    torch_available = True
+except ImportError:
+    torch_available = False
 
 
 class Image(BaseDocument):
@@ -67,3 +79,17 @@ class MultiModalDoc(BaseDocument):
     url: Optional[ImageUrl]
     tensor: Optional[AnyTensor]
     embedding: Optional[AnyEmbedding]
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, AbstractTensor, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(url=value)
+        elif isinstance(value, (AbstractTensor, np.ndarray)) or (
+            torch_available and isinstance(value, torch.Tensor)
+        ):
+            value = cls(tensor=value)
+
+        return super().validate(value)
diff --git a/docarray/documents/mesh.py b/docarray/documents/mesh.py
@@ -1,8 +1,10 @@
-from typing import Optional
+from typing import Any, Optional, Type, TypeVar, Union
 
 from docarray.base_document import BaseDocument
 from docarray.typing import AnyEmbedding, AnyTensor, Mesh3DUrl
 
+T = TypeVar('T', bound='Mesh3D')
+
 
 class Mesh3D(BaseDocument):
     """
@@ -77,3 +79,12 @@ class MultiModalDoc(BaseDocument):
     vertices: Optional[AnyTensor]
     faces: Optional[AnyTensor]
     embedding: Optional[AnyEmbedding]
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(url=value)
+        return super().validate(value)
diff --git a/docarray/documents/point_cloud.py b/docarray/documents/point_cloud.py
@@ -1,7 +1,19 @@
-from typing import Optional
+from typing import Any, Optional, Type, TypeVar, Union
+
+import numpy as np
 
 from docarray.base_document import BaseDocument
 from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
+from docarray.typing.tensor.abstract_tensor import AbstractTensor
+
+try:
+    import torch
+
+    torch_available = True
+except ImportError:
+    torch_available = False
+
+T = TypeVar('T', bound='PointCloud3D')
 
 
 class PointCloud3D(BaseDocument):
@@ -75,3 +87,17 @@ class MultiModalDoc(BaseDocument):
     url: Optional[PointCloud3DUrl]
     tensor: Optional[AnyTensor]
     embedding: Optional[AnyEmbedding]
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, AbstractTensor, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(url=value)
+        elif isinstance(value, (AbstractTensor, np.ndarray)) or (
+            torch_available and isinstance(value, torch.Tensor)
+        ):
+            value = cls(tensor=value)
+
+        return super().validate(value)
diff --git a/docarray/documents/text.py b/docarray/documents/text.py
@@ -1,9 +1,11 @@
-from typing import Optional
+from typing import Any, Optional, Type, TypeVar, Union
 
 from docarray.base_document import BaseDocument
 from docarray.typing import TextUrl
 from docarray.typing.tensor.embedding import AnyEmbedding
 
+T = TypeVar('T', bound='Text')
+
 
 class Text(BaseDocument):
     """
@@ -68,3 +70,12 @@ class MultiModalDoc(BaseDocument):
     text: Optional[str] = None
     url: Optional[TextUrl] = None
     embedding: Optional[AnyEmbedding] = None
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(text=value)
+        return super().validate(value)
diff --git a/docarray/documents/video.py b/docarray/documents/video.py
@@ -1,11 +1,21 @@
-from typing import Optional, TypeVar
+from typing import Any, Optional, Type, TypeVar, Union
+
+import numpy as np
 
 from docarray.base_document import BaseDocument
 from docarray.documents import Audio
 from docarray.typing import AnyEmbedding, AnyTensor
+from docarray.typing.tensor.abstract_tensor import AbstractTensor
 from docarray.typing.tensor.video.video_tensor import VideoTensor
 from docarray.typing.url.video_url import VideoUrl
 
+try:
+    import torch
+
+    torch_available = True
+except ImportError:
+    torch_available = False
+
 T = TypeVar('T', bound='Video')
 
 
@@ -83,3 +93,17 @@ class MultiModalDoc(BaseDocument):
     tensor: Optional[VideoTensor]
     key_frame_indices: Optional[AnyTensor]
     embedding: Optional[AnyEmbedding]
+
+    @classmethod
+    def validate(
+        cls: Type[T],
+        value: Union[str, AbstractTensor, Any],
+    ) -> T:
+        if isinstance(value, str):
+            value = cls(url=value)
+        elif isinstance(value, (AbstractTensor, np.ndarray)) or (
+            torch_available and isinstance(value, torch.Tensor)
+        ):
+            value = cls(tensor=value)
+
+        return super().validate(value)
diff --git a/tests/integrations/predefined_document/test_audio.py b/tests/integrations/predefined_document/test_audio.py
@@ -6,6 +6,7 @@
 import torch
 from pydantic import parse_obj_as
 
+from docarray import BaseDocument
 from docarray.documents import Audio
 from docarray.typing import AudioUrl
 from docarray.typing.tensor.audio import AudioNdArray, AudioTorchTensor
@@ -83,3 +84,29 @@ class MyAudio(Audio):
 
     assert isinstance(my_audio.tensor, AudioNdArray)
     assert isinstance(my_audio.url, AudioUrl)
+
+
+def test_audio_np():
+    audio = parse_obj_as(Audio, np.zeros((10, 10, 3)))
+    assert (audio.tensor == np.zeros((10, 10, 3))).all()
+
+
+def test_audio_torch():
+    audio = parse_obj_as(Audio, torch.zeros(10, 10, 3))
+    assert (audio.tensor == torch.zeros(10, 10, 3)).all()
+
+
+def test_audio_shortcut_doc():
+    class MyDoc(BaseDocument):
+        audio: Audio
+        audio2: Audio
+        audio3: Audio
+
+    doc = MyDoc(
+        audio='http://myurl.wav',
+        audio2=np.zeros((10, 10, 3)),
+        audio3=torch.zeros(10, 10, 3),
+    )
+    assert doc.audio.url == 'http://myurl.wav'
+    assert (doc.audio2.tensor == np.zeros((10, 10, 3))).all()
+    assert (doc.audio3.tensor == torch.zeros(10, 10, 3)).all()
diff --git a/tests/integrations/predefined_document/test_image.py b/tests/integrations/predefined_document/test_image.py
@@ -1,6 +1,9 @@
 import numpy as np
 import pytest
+import torch
+from pydantic import parse_obj_as
 
+from docarray import BaseDocument
 from docarray.documents import Image
 
 REMOTE_JPG = (
@@ -12,9 +15,39 @@
 @pytest.mark.slow
 @pytest.mark.internet
 def test_image():
-
     image = Image(url=REMOTE_JPG)
 
     image.tensor = image.url.load()
 
     assert isinstance(image.tensor, np.ndarray)
+
+
+def test_image_str():
+    image = parse_obj_as(Image, 'http://myurl.jpg')
+    assert image.url == 'http://myurl.jpg'
+
+
+def test_image_np():
+    image = parse_obj_as(Image, np.zeros((10, 10, 3)))
+    assert (image.tensor == np.zeros((10, 10, 3))).all()
+
+
+def test_image_torch():
+    image = parse_obj_as(Image, torch.zeros(10, 10, 3))
+    assert (image.tensor == torch.zeros(10, 10, 3)).all()
+
+
+def test_image_shortcut_doc():
+    class MyDoc(BaseDocument):
+        image: Image
+        image2: Image
+        image3: Image
+
+    doc = MyDoc(
+        image='http://myurl.jpg',
+        image2=np.zeros((10, 10, 3)),
+        image3=torch.zeros(10, 10, 3),
+    )
+    assert doc.image.url == 'http://myurl.jpg'
+    assert (doc.image2.tensor == np.zeros((10, 10, 3))).all()
+    assert (doc.image3.tensor == torch.zeros(10, 10, 3)).all()
diff --git a/tests/integrations/predefined_document/test_mesh.py b/tests/integrations/predefined_document/test_mesh.py
@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
+from pydantic import parse_obj_as
 
+from docarray import BaseDocument
 from docarray.documents import Mesh3D
 from tests import TOYDATA_DIR
 
@@ -19,3 +21,19 @@ def test_mesh(file_url):
 
     assert isinstance(mesh.vertices, np.ndarray)
     assert isinstance(mesh.faces, np.ndarray)
+
+
+def test_str_init():
+    t = parse_obj_as(Mesh3D, 'http://hello.ply')
+    assert t.url == 'http://hello.ply'
+
+
+def test_doc():
+    class MyDoc(BaseDocument):
+        mesh1: Mesh3D
+        mesh2: Mesh3D
+
+    doc = MyDoc(mesh1='http://hello.ply', mesh2=Mesh3D(url='http://hello.ply'))
+
+    assert doc.mesh1.url == 'http://hello.ply'
+    assert doc.mesh2.url == 'http://hello.ply'
diff --git a/tests/integrations/predefined_document/test_point_cloud.py b/tests/integrations/predefined_document/test_point_cloud.py
@@ -1,6 +1,9 @@
 import numpy as np
 import pytest
+import torch
+from pydantic import parse_obj_as
 
+from docarray import BaseDocument
 from docarray.documents import PointCloud3D
 from tests import TOYDATA_DIR
 
@@ -18,3 +21,29 @@ def test_point_cloud(file_url):
     point_cloud.tensor = point_cloud.url.load(samples=100)
 
     assert isinstance(point_cloud.tensor, np.ndarray)
+
+
+def test_point_cloud_np():
+    image = parse_obj_as(PointCloud3D, np.zeros((10, 10, 3)))
+    assert (image.tensor == np.zeros((10, 10, 3))).all()
+
+
+def test_point_cloud_torch():
+    image = parse_obj_as(PointCloud3D, torch.zeros(10, 10, 3))
+    assert (image.tensor == torch.zeros(10, 10, 3)).all()
+
+
+def test_point_cloud_shortcut_doc():
+    class MyDoc(BaseDocument):
+        image: PointCloud3D
+        image2: PointCloud3D
+        image3: PointCloud3D
+
+    doc = MyDoc(
+        image='http://myurl.ply',
+        image2=np.zeros((10, 10, 3)),
+        image3=torch.zeros(10, 10, 3),
+    )
+    assert doc.image.url == 'http://myurl.ply'
+    assert (doc.image2.tensor == np.zeros((10, 10, 3))).all()
+    assert (doc.image3.tensor == torch.zeros(10, 10, 3)).all()
diff --git a/tests/integrations/predefined_document/test_text.py b/tests/integrations/predefined_document/test_text.py
@@ -0,0 +1,25 @@
+from pydantic import parse_obj_as
+
+from docarray import BaseDocument
+from docarray.documents import Text
+
+
+def test_simple_init():
+    t = Text(text='hello')
+    assert t.text == 'hello'
+
+
+def test_str_init():
+    t = parse_obj_as(Text, 'hello')
+    assert t.text == 'hello'
+
+
+def test_doc():
+    class MyDoc(BaseDocument):
+        text1: Text
+        text2: Text
+
+    doc = MyDoc(text1='hello', text2=Text(text='world'))
+
+    assert doc.text1.text == 'hello'
+    assert doc.text2.text == 'world'