fix: replace transform ndarray with transform blob

server/clip_server/executors/clip_onnx.py

@@ -68,7 +68,7 @@ def __init__(
         self._model = CLIPOnnxModel(name, model_path, dtype)
         self._tokenizer = Tokenizer(name)
 
-        self._image_transform = clip._transform_ndarray(self._model.image_size)
+        self._image_transform = clip._transform_blob(self._model.image_size)
 
         # define the priority order for the execution providers
         providers = ['CPUExecutionProvider']

server/clip_server/executors/clip_tensorrt.py

@@ -68,7 +68,7 @@ def __init__(
         self._model.start_engines()
 
         self._tokenizer = Tokenizer(name)
-        self._image_transform = clip._transform_ndarray(self._model.image_size)
+        self._image_transform = clip._transform_blob(self._model.image_size)
 
         if not self.tracer:
             self.tracer = NoOpTracer()

server/clip_server/executors/clip_torch.py

@@ -92,7 +92,7 @@ def __init__(
             name, device=self._device, jit=jit, dtype=dtype, **kwargs
         )
         self._tokenizer = Tokenizer(name)
-        self._image_transform = clip._transform_ndarray(self._model.image_size)
+        self._image_transform = clip._transform_blob(self._model.image_size)
 
         if not self.tracer:
             self.tracer = NoOpTracer()

server/clip_server/executors/helper.py

@@ -33,14 +33,13 @@ def preproc_image(
 
     for d in da:
         content = d.content
-
-        if d.blob:
-            d.convert_blob_to_image_tensor()
-        elif d.tensor is None and d.uri:
+        if d.tensor is not None:
+            d.convert_image_tensor_to_blob()
+        elif d.content_type != 'blob' and d.uri:
             # in case user uses HTTP protocol and send data via curl not using .blob (base64), but in .uri
-            d.load_uri_to_image_tensor()
+            d.load_uri_to_blob()
 
-        tensors_batch.append(preprocess_fn(d.tensor).detach())
+        tensors_batch.append(preprocess_fn(d.blob).detach())
 
         # recover doc content
         d.content = content

tests/test_helper.py

@@ -87,15 +87,15 @@ def test_split_img_txt_da(inputs):
             [
                 Document(
                     uri='https://clip-as-service.jina.ai/_static/favicon.png',
-                ).load_uri_to_blob(),
+                ).load_uri_to_image_tensor(),
             ]
         )
     ],
 )
 def test_preproc_image(inputs):
     from clip_server.model import clip
 
-    preprocess_fn = clip._transform_ndarray(224)
+    preprocess_fn = clip._transform_blob(224)
     da, pixel_values = preproc_image(inputs, preprocess_fn, drop_image_content=True)
     assert len(da) == 1
     assert not da[0].blob