load CLIP model and tokenizer from local path

src/open_clip/factory.py

@@ -74,7 +74,10 @@ def get_model_config(model_name):
 
 
 def _get_hf_config(model_id, cache_dir=None):
-    config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
+    if cache_dir is None:
+        config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
+    else:
+        config_path = os.path.join(cache_dir, model_id, 'open_clip_config.json')
     with open(config_path, 'r', encoding='utf-8') as f:
         config = json.load(f)
     return config
@@ -83,12 +86,13 @@ def _get_hf_config(model_id, cache_dir=None):
 def get_tokenizer(
         model_name: str = '',
         context_length: Optional[int] = None,
+        cache_dir: Optional[str] = None,
         **kwargs,
 ):
     if model_name.startswith(HF_HUB_PREFIX):
         model_name = model_name[len(HF_HUB_PREFIX):]
         try:
-            config = _get_hf_config(model_name)['model_cfg']
+            config = _get_hf_config(model_name, cache_dir=cache_dir)['model_cfg']
         except Exception:
             tokenizer = HFTokenizer(
                 model_name,
@@ -185,7 +189,10 @@ def create_model(
     has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
     if has_hf_hub_prefix:
         model_id = model_name[len(HF_HUB_PREFIX):]
-        checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
+        if cache_dir is None:
+            checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
+        else:
+            checkpoint_path = os.path.join(cache_dir, model_id, "open_clip_pytorch_model.bin")
         config = _get_hf_config(model_id, cache_dir)
         preprocess_cfg = merge_preprocess_dict(preprocess_cfg, config['preprocess_cfg'])
         model_cfg = config['model_cfg']