Merge pull request #6 from jina-ai/refactor-migration-jina3

refactor: migration docarray
jina-ai · Feb 21, 2022 · f39f3b9 · f39f3b9
2 parents c995888 + 7054c1f
commit f39f3b9
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diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # 3D Mesh Encoder
 
-An Executor that receives Documents containing point sets data in its blob attribute, with shape `(N, 3)` and encodes it to embeddings of shape `(D,)`.
+An Executor that receives Documents containing point sets data in its `tensor` attribute, with shape `(N, 3)` and encodes it to embeddings of shape `(D,)`.
 Now, the following pretrained models are ready to be used to create embeddings:
 
 - **PointConv-Shapenet-d512**: A **PointConv** model resulted in **512** dimension of embeddings, which is finetuned based on ShapeNet dataset.

diff --git a/executor.py b/executor.py
@@ -38,11 +38,11 @@
 
 
 def normalize(doc: 'Document'):
-    points = doc.blob
+    points = doc.tensor
     points = points - np.expand_dims(np.mean(points, axis=0), 0)  # center
     dist = np.max(np.sqrt(np.sum(points ** 2, axis=1)), 0)
     points = points / dist  # scale
-    doc.blob = points.astype(np.float32)
+    doc.tensor = points.astype(np.float32)
     return doc