Add support for passing model args via hf hub config

timm/models/_factory.py

@@ -99,7 +99,10 @@ def create_model(
         assert not pretrained_cfg, 'pretrained_cfg should not be set when sourcing model from Hugging Face Hub.'
         # For model names specified in the form `hf-hub:path/architecture_name@revision`,
         # load model weights + pretrained_cfg from Hugging Face hub.
-        pretrained_cfg, model_name = load_model_config_from_hf(model_name)
+        pretrained_cfg, model_name, model_args = load_model_config_from_hf(model_name)
+        if model_args:
+            for k, v in model_args.items():
+                kwargs.setdefault(k, v)
     else:
         model_name, pretrained_tag = split_model_name_tag(model_name)
         if pretrained_tag and not pretrained_cfg:

timm/models/_hub.py

@@ -164,8 +164,9 @@ def load_model_config_from_hf(model_id: str):
     if 'label_descriptions' in hf_config:
         pretrained_cfg['label_descriptions'] = hf_config.pop('label_descriptions')
 
+    model_args = hf_config.get('model_args', {})
     model_name = hf_config['architecture']
-    return pretrained_cfg, model_name
+    return pretrained_cfg, model_name, model_args
 
 
 def load_state_dict_from_hf(model_id: str, filename: str = HF_WEIGHTS_NAME):
@@ -193,19 +194,23 @@ def load_state_dict_from_hf(model_id: str, filename: str = HF_WEIGHTS_NAME):
 def save_config_for_hf(
         model,
         config_path: str,
-        model_config: Optional[dict] = None
+        model_config: Optional[dict] = None,
+        model_args: Optional[dict] = None
 ):
     model_config = model_config or {}
     hf_config = {}
     pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
     # set some values at root config level
     hf_config['architecture'] = pretrained_cfg.pop('architecture')
-    hf_config['num_classes'] = model_config.get('num_classes', model.num_classes)
-    hf_config['num_features'] = model_config.get('num_features', model.num_features)
-    global_pool_type = model_config.get('global_pool', getattr(model, 'global_pool', None))
+    hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes)
+
+    # NOTE these attr saved for informational purposes, do not impact model build
+    hf_config['num_features'] = model_config.pop('num_features', model.num_features)
+    global_pool_type = model_config.pop('global_pool', getattr(model, 'global_pool', None))
     if isinstance(global_pool_type, str) and global_pool_type:
         hf_config['global_pool'] = global_pool_type
 
+    # Save class label info
     if 'labels' in model_config:
         _logger.warning(
             "'labels' as a config field for is deprecated. Please use 'label_names' and 'label_descriptions'."
@@ -225,6 +230,9 @@ def save_config_for_hf(
         # maps label names -> descriptions
         hf_config['label_descriptions'] = label_descriptions
 
+    if model_args:
+        hf_config['model_args'] = model_args
+
     hf_config['pretrained_cfg'] = pretrained_cfg
     hf_config.update(model_config)
 
@@ -236,6 +244,7 @@ def save_for_hf(
         model,
         save_directory: str,
         model_config: Optional[dict] = None,
+        model_args: Optional[dict] = None,
         safe_serialization: Union[bool, Literal["both"]] = False,
 ):
     assert has_hf_hub(True)
@@ -251,11 +260,16 @@ def save_for_hf(
         torch.save(tensors, save_directory / HF_WEIGHTS_NAME)
 
     config_path = save_directory / 'config.json'
-    save_config_for_hf(model, config_path, model_config=model_config)
+    save_config_for_hf(
+        model,
+        config_path,
+        model_config=model_config,
+        model_args=model_args,
+    )
 
 
 def push_to_hf_hub(
-        model,
+        model: torch.nn.Module,
         repo_id: str,
         commit_message: str = 'Add model',
         token: Optional[str] = None,
@@ -264,6 +278,7 @@ def push_to_hf_hub(
         create_pr: bool = False,
         model_config: Optional[dict] = None,
         model_card: Optional[dict] = None,
+        model_args: Optional[dict] = None,
         safe_serialization: Union[bool, Literal["both"]] = False,
 ):
     """
@@ -291,7 +306,13 @@ def push_to_hf_hub(
     # Dump model and push to Hub
     with TemporaryDirectory() as tmpdir:
         # Save model weights and config.
-        save_for_hf(model, tmpdir, model_config=model_config, safe_serialization=safe_serialization)
+        save_for_hf(
+            model,
+            tmpdir,
+            model_config=model_config,
+            model_args=model_args,
+            safe_serialization=safe_serialization,
+        )
 
         # Add readme if it does not exist
         if not has_readme: