Consistently use hf_model_id

timm/models/factory.py

@@ -55,7 +55,7 @@ def create_model(
             try:
                 model_cfg = load_hf_checkpoint_config(model_name, revision=kwargs.get("hf_revision"))
                 create_fn = model_entrypoint(model_cfg.pop("architecture"))
-                model = create_fn(**model_args, hf_checkpoint=model_name, **kwargs)
+                model = create_fn(**model_args, hf_model_id=model_name, **kwargs)
                 # Probably need some extra stuff, but this is a PoC of how the config in the model hub
                 # could overwrite the default config values.
                 # model.default_cfg.update(model_cfg)

timm/models/helpers.py

@@ -184,19 +184,19 @@ def adapt_input_conv(in_chans, conv_weight):
 
 
 def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True, 
-    progress=False, hf_checkpoint=None, hf_revision=None):
+    progress=False, hf_model_id=None, hf_revision=None):
     cfg = cfg or getattr(model, 'default_cfg')
-    if hf_checkpoint is None:
-        hg_checkpoint = cfg.get('hf_checkpoint')
+    if hf_model_id is None:
+        hg_checkpoint = cfg.get('hf_model_id')
     if hf_revision is None:
         hg_revision = cfg.get('hf_revision')
-    if cfg is None or (('url' not in cfg or not cfg['url']) and hf_checkpoint is None):
+    if cfg is None or (('url' not in cfg or not cfg['url']) and hf_model_id is None):
         _logger.warning("No pretrained weights exist for this model. Using random initialization.")
         return
 
-    if hf_checkpoint is not None:
+    if hf_model_id is not None:
         # TODO, progress is ignored.
-        url = hf_hub_url(hf_checkpoint, "pytorch_model.bin", revision=hf_revision)
+        url = hf_hub_url(hf_model_id, "pytorch_model.bin", revision=hf_revision)
         cached_filed = cached_download(
             url, library_name="timm", library_version=__version__, cache_dir=get_cache_dir()
         )
@@ -358,7 +358,7 @@ def build_model_with_cfg(
         pretrained_custom_load: bool = False,
         **kwargs):
     pruned = kwargs.pop('pruned', False)
-    hf_checkpoint = kwargs.pop('hf_checkpoint', None)
+    hf_model_id = kwargs.pop('hf_model_id', None)
     hf_revision = kwargs.pop('hf_revision', None)
     features = False
     feature_cfg = feature_cfg or {}
@@ -377,7 +377,7 @@ def build_model_with_cfg(
 
     # for classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
     num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
-    if pretrained or hf_checkpoint is not None:
+    if pretrained or hf_model_id is not None:
         if pretrained_custom_load:
             load_custom_pretrained(model)
         else:
@@ -385,7 +385,7 @@ def build_model_with_cfg(
                 model,
                 num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3),
                 filter_fn=pretrained_filter_fn, strict=pretrained_strict, 
-                hf_checkpoint=hf_checkpoint, hf_revision=hf_revision)
+                hf_model_id=hf_model_id, hf_revision=hf_revision)
     
     if features:
         feature_cls = FeatureListNet

timm/models/registry.py

@@ -37,7 +37,7 @@ def register_model(fn):
         # this will catch all models that have entrypoint matching cfg key, but miss any aliasing
         # entrypoints or non-matching combos
         has_pretrained = 'url' in mod.default_cfgs[model_name] and 'http' in mod.default_cfgs[model_name]['url']
-        has_pretrained = has_pretrained or 'hf_checkpoint' in mod.default_cfgs[model_name]
+        has_pretrained = has_pretrained or 'hf_model_id' in mod.default_cfgs[model_name]
     if has_pretrained:
         _model_has_pretrained.add(model_name)
     return fn