Merge pull request #7 from jina-ai/refactor-migration-jina3

refactor: change folder

config.yml

@@ -1,4 +1,4 @@
 jtype: MeshDataEncoder
 metas:
   py_modules:
-    - __init__.py
+    - executor/__init__.py

executor/__init__.py

@@ -0,0 +1 @@
+from .encoder import MeshDataEncoder

finetune.py

@@ -13,7 +13,7 @@
 from finetuner.tuner.pytorch.miner import TripletEasyHardMiner
 from docarray import Document, DocumentArray
 
-from models import MeshDataModel
+from executor.models import MeshDataModel
 
 
 def random_sample(pc, num):

tests/integration/test_encoder.py

@@ -0,0 +1,24 @@
+import numpy as np
+import pytest
+from jina import DocumentArray, Flow
+
+from executor import MeshDataEncoder
+
+
+@pytest.mark.parametrize('model_name', ['pointconv', 'pointnet'])
+def test_integration(model_name: str):
+    docs = DocumentArray.empty(5)
+    docs.tensors = np.random.random((5, 1024, 3))
+    with Flow(return_results=True).add(
+        uses=MeshDataEncoder,
+        uses_with={'pretrained_model': None, 'default_model_name': model_name},
+    ) as flow:
+        resp = flow.post(
+            on='/index',
+            inputs=docs,
+            return_results=True,
+        )
+
+        for doc in resp:
+            assert doc.embedding is not None
+            assert doc.embedding.shape == (1024,)

tests/requirements.txt

@@ -0,0 +1 @@
+pytest

tests/unit/test_exec.py

@@ -0,0 +1,17 @@
+import numpy as np
+import pytest
+from jina import Document, DocumentArray
+
+from executor import MeshDataEncoder
+
+
+@pytest.mark.parametrize('model_name', ['pointconv', 'pointnet'])
+def test_encoder(model_name):
+    encoder = MeshDataEncoder(pretrained_model=None, default_model_name=model_name)
+
+    docs = DocumentArray(Document(tensor=np.random.random((1024, 3))))
+
+    encoder.encode(docs)
+
+    assert docs[0].embedding is not None
+    assert docs[0].embedding.shape == (1024,)