def encode(self, docs: DocumentArray, **kwargs):
with torch.inference_mode():
_input = torch.from_numpy(docs.blobs.astype('float32'))
_features = self._get_features(_input).detach()
_features = _features.numpy()
_features = self._get_pooling(_features)
docs.embeddings = _features
def encode(self, docs: DocumentArray, **kwargs):
content = np.stack(docs.get_attributes('blob'))
_input = torch.from_numpy(content.astype('float32'))
_features = self._get_features(_input).detach()
_features = _features.numpy()
_features = self._get_pooling(_features)
docs.embeddings = _features
def test_embeddings_setter_da():
emb = np.random.random((100, 128))
da = DocumentArray([Document() for _ in range(100)])
da.embeddings = emb
np.testing.assert_almost_equal(da.embeddings, emb)
for x, doc in zip(emb, da):
np.testing.assert_almost_equal(x, doc.embedding)
def test_embeddings_wrong_len():
da = DocumentArray([Document() for _ in range(100)])
embeddings = np.ones((2, 10))
with pytest.raises(ValueError, match='the number of rows in the'):
da.embeddings = embeddings
def test_set_embeddings_multi_kind(array):
da = DocumentArray([Document() for _ in range(10)])
da.embeddings = array
