fix(server): recover original contents of the input da

server/clip_server/executors/clip_onnx.py

@@ -103,8 +103,9 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
             ):
                 minibatch.embeddings = self._model.encode_image(minibatch.tensors)
                 # recover original content
-                for _d, _ct in zip(minibatch, _contents):
-                    _d.content = _ct
+                if _contents:
+                    for _d, _ct in zip(minibatch, _contents):
+                        _d.content = _ct
 
         # for text
         if _txt_da:
@@ -115,8 +116,9 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
             ):
                 minibatch.embeddings = self._model.encode_text(minibatch.tensors)
                 # recover original content
-                for _d, _ct in zip(minibatch, _contents):
-                    _d.content = _ct
+                if _contents:
+                    for _d, _ct in zip(minibatch, _contents):
+                        _d.content = _ct
 
         # drop tensors
         docs.tensors = None

server/clip_server/executors/clip_tensorrt.py

@@ -80,12 +80,13 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
                     .astype(np.float32)
                 )
                 # recover original content
-                for _d, _ct in zip(minibatch, _contents):
-                    _d.content = _ct
+                if _contents:
+                    for _d, _ct in zip(minibatch, _contents):
+                        _d.content = _ct
 
         # for text
         if _txt_da:
-            for minibatch, _texts in _txt_da.map_batch(
+            for minibatch, _contents in _txt_da.map_batch(
                 partial(preproc_text, device=self._device, return_np=False),
                 batch_size=self._minibatch_size,
                 pool=self._pool,
@@ -98,8 +99,9 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
                     .astype(np.float32)
                 )
                 # recover original content
-                for _d, _ct in zip(minibatch, _contents):
-                    _d.content = _ct
+                if _contents:
+                    for _d, _ct in zip(minibatch, _contents):
+                        _d.content = _ct
 
         # drop tensors
         docs.tensors = None

server/clip_server/executors/clip_torch.py

@@ -95,8 +95,9 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
                     )
 
                     # recover original content
-                    for _d, _ct in zip(minibatch, _contents):
-                        _d.content = _ct
+                    if _contents:
+                        for _d, _ct in zip(minibatch, _contents):
+                            _d.content = _ct
 
             # for text
             if _txt_da:
@@ -113,8 +114,9 @@ async def encode(self, docs: 'DocumentArray', **kwargs):
                     )
 
                     # recover original content
-                    for _d, _ct in zip(minibatch, _contents):
-                        _d.content = _ct
+                    if _contents:
+                        for _d, _ct in zip(minibatch, _contents):
+                            _d.content = _ct
 
         # drop tensors
         docs.tensors = None

server/clip_server/executors/helper.py

@@ -94,10 +94,10 @@ def set_rank(docs, _logit_scale=np.exp(4.60517)):
 
         start_idx = end_idx
 
+        _candidates.embeddings = None  # remove embedding to save bandwidth
+
         final = sorted(
             _candidates, key=lambda _m: _m.scores['clip_score'].value, reverse=True
         )
 
-        final.embeddings = None  # remove embedding to save bandwidth
-
         q.matches = final