docarray
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‎docarray/typing/tensor/torch_tensor.py‎
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‎docarray/typing/tensor/torch_tensor.py‎
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+1-1Lines changed: 1 addition & 1 deletion
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‎tests/units/typing/tensor/test_torch_tensor.py‎
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‎tests/units/typing/tensor/test_torch_tensor.py‎
Copy file name to clipboardExpand all lines: tests/units/typing/tensor/test_torch_tensor.py
+59-1Lines changed: 59 additions & 1 deletion
@@ -137,7 +137,7 @@ def _docarray_to_json_compatible(self) -> np.ndarray:
         Convert `TorchTensor` into a json compatible object
         :return: a representation of the tensor compatible with orjson
         """
-        return self.numpy()  ## might need to check device later
+        return self.detach().numpy()  # might need to check device later
 
     def unwrap(self) -> torch.Tensor:
         """
 
@@ -1,11 +1,19 @@
+import json
+
 import pytest
 import torch
 from pydantic.tools import parse_obj_as, schema_json_of
 
+from docarray import BaseDoc
 from docarray.base_doc.io.json import orjson_dumps
+from docarray.proto import DocProto
 from docarray.typing import TorchEmbedding, TorchTensor
 
 
+class MyDoc(BaseDoc):
+    tens: TorchTensor
+
+
 @pytest.mark.proto
 def test_proto_tensor():
     tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
@@ -63,7 +71,7 @@ def test_wrong_but_reshapable():
         parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(224, 224))
 
 
-def test_inependent_variable_dim():
+def test_independent_variable_dim():
     # test independent variable dimensions
     tensor = parse_obj_as(TorchTensor[3, 'x', 'y'], torch.zeros(3, 224, 224))
     assert isinstance(tensor, TorchTensor)
@@ -177,3 +185,53 @@ class MMdoc(BaseDoc):
 
     doc_copy.embedding = torch.randn(32)
     assert not (doc.embedding == doc_copy.embedding).all()
+
+
+@pytest.mark.parametrize('requires_grad', [True, False])
+def test_json_serialization(requires_grad):
+    orig_doc = MyDoc(tens=torch.rand(10, requires_grad=requires_grad))
+    serialized_doc = orig_doc.to_json()
+    assert serialized_doc
+    assert isinstance(serialized_doc, str)
+
+    json_doc = json.loads(serialized_doc)
+    assert json_doc['tens']
+    assert len(json_doc['tens']) == 10
+
+
+@pytest.mark.parametrize('protocol', ['pickle', 'protobuf'])
+@pytest.mark.parametrize('requires_grad', [True, False])
+def test_bytes_serialization(requires_grad, protocol):
+    orig_doc = MyDoc(tens=torch.rand(10, requires_grad=requires_grad))
+    serialized_doc = orig_doc.to_bytes(protocol=protocol)
+    assert serialized_doc
+    assert isinstance(serialized_doc, bytes)
+
+    conv_doc = MyDoc.from_bytes(serialized_doc, protocol=protocol)
+    assert isinstance(conv_doc.tens, TorchTensor)
+    assert conv_doc.tens.shape == (10,)
+
+
+@pytest.mark.parametrize('protocol', ['pickle', 'protobuf'])
+@pytest.mark.parametrize('requires_grad', [True, False])
+def test_base64_serialization(requires_grad, protocol):
+    orig_doc = MyDoc(tens=torch.rand(10, requires_grad=requires_grad))
+    serialized_doc = orig_doc.to_base64(protocol=protocol)
+    assert serialized_doc
+    assert isinstance(serialized_doc, str)
+
+    conv_doc = MyDoc.from_base64(serialized_doc, protocol=protocol)
+    assert isinstance(conv_doc.tens, TorchTensor)
+    assert conv_doc.tens.shape == (10,)
+
+
+@pytest.mark.parametrize('requires_grad', [True, False])
+def test_protobuf_serialization(requires_grad):
+    orig_doc = MyDoc(tens=torch.rand(10, requires_grad=requires_grad))
+    serialized_doc = orig_doc.to_protobuf()
+    assert serialized_doc
+    assert isinstance(serialized_doc, DocProto)
+
+    conv_doc = MyDoc.from_protobuf(serialized_doc)
+    assert isinstance(conv_doc.tens, TorchTensor)
+    assert conv_doc.tens.shape == (10,)