feat: update openclip loader

server/clip_server/model/mclip_model.py

@@ -73,5 +73,5 @@ def encode_text(
             input_ids=input_ids, attention_mask=attention_mask, **kwargs
         )
 
-    def encode_image(self, pixel_values: torch.Tensor, **kwargs):
+    def encode_image(self, pixel_values: torch.Tensor):
         return self._model.encode_image(pixel_values)

server/clip_server/model/openclip_model.py

@@ -6,11 +6,17 @@
 # Ludwig Schmidt
 
 from typing import TYPE_CHECKING
+from copy import deepcopy
+import torch
 
 from clip_server.model.clip_model import CLIPModel
 from clip_server.model.pretrained_models import get_model_url_md5, download_model
-import open_clip
-from open_clip.openai import load_openai_model
+
+from open_clip.model import (
+    CLIP,
+    convert_weights_to_fp16,
+)
+from open_clip.factory import _MODEL_CONFIGS, load_state_dict, load_openai_model
 
 if TYPE_CHECKING:
     import torch
@@ -20,28 +26,46 @@ class OpenCLIPModel(CLIPModel):
     def __init__(self, name: str, device: str = 'cpu', jit: bool = False, **kwargs):
         super().__init__(name, **kwargs)
 
+        if '::' in name:
+            model_name, pretrained = name.split('::')
+        else:
+            # default pretrained model is from openai
+            model_name = name
+            pretrained = 'openai'
+
+        self._model_name = model_name
+
         model_url, md5sum = get_model_url_md5(name)
-        if model_url:
-            model_path = download_model(model_url, md5sum=md5sum)
+        model_path = download_model(model_url, md5sum=md5sum)
+        if pretrained.lower() == 'openai':
             self._model = load_openai_model(model_path, device=device, jit=jit)
-            self._model_name = name.split('::')[0]
         else:
-            model_name, pretrained = name.split('::')
-            self._model = open_clip.create_model(
-                model_name, pretrained=pretrained, device=device, jit=jit
-            )
-            self._model_name = model_name
+            if model_name in _MODEL_CONFIGS:
+                model_cfg = deepcopy(_MODEL_CONFIGS[model_name])
+            else:
+                raise RuntimeError(f'Model config for {model_name} not found.')
+
+            self._model = CLIP(**model_cfg)
+
+            state_dict = load_state_dict(model_path)
+            self._model.load_state_dict(state_dict, strict=True)
+
+            if str(device) == 'cuda':
+                convert_weights_to_fp16(self._model)
+            if jit:
+                self._model = torch.jit.script(self._model)
+
+            self._model.to(device=torch.device(device))
+            self._model.eval()
 
     @property
     def model_name(self):
         if self._model_name == 'ViT-L/14@336px':
             return 'ViT-L-14-336'
-        elif self._model_name.endswith('-quickgelu'):
-            return self._model_name[:-10]
         return self._model_name.replace('/', '-')
 
     def encode_text(self, input_ids: 'torch.Tensor', **kwargs):
         return self._model.encode_text(input_ids)
 
-    def encode_image(self, pixel_values: 'torch.Tensor', **kwargs):
+    def encode_image(self, pixel_values: 'torch.Tensor'):
         return self._model.encode_image(pixel_values)

server/clip_server/model/pretrained_models.py

@@ -82,7 +82,6 @@
     'ViT-B-32': 224,
     'ViT-B-16': 224,
     'ViT-B-16-plus-240': 240,
-    'ViT-B-16-plus-240': 240,
     'ViT-L-14': 224,
     'ViT-L-14-336': 336,
     'Vit-B-16Plus': 240,

server/setup.py

@@ -49,7 +49,7 @@
         'torchvision',
         'jina>=3.6.0',
         'prometheus-client',
-        'open_clip_torch',
+        'open_clip_torch>=1.3.0',
     ],
     extras_require={
         'onnx': [