A few minor fixes and bit more cleanup on the huggingface hub integration.

timm/models/factory.py

@@ -1,7 +1,7 @@
 from .registry import is_model, is_model_in_modules, model_entrypoint
 from .helpers import load_checkpoint
 from .layers import set_layer_config
-from .hub import load_config_from_hf
+from .hub import load_model_config_from_hf
 
 
 def split_model_name(model_name):
@@ -67,11 +67,9 @@ def create_model(
     kwargs = {k: v for k, v in kwargs.items() if v is not None}
 
     if source_name == 'hf_hub':
-        # Load model weights + default_cfg from Hugging Face hub.
-        # For model names specified in the form `hf_hub:path/architecture_name#revision`
-        hf_default_cfg = load_config_from_hf(model_name)
-        hf_default_cfg['hf_hub'] = model_name  # insert hf_hub id for pretrained weight load during creation
-        model_name = hf_default_cfg.get('architecture')
+        # For model names specified in the form `hf_hub:path/architecture_name#revision`,
+        # load model weights + default_cfg from Hugging Face hub.
+        hf_default_cfg, model_name = load_model_config_from_hf(model_name)
         kwargs['external_default_cfg'] = hf_default_cfg  # FIXME revamp default_cfg interface someday
 
     if is_model(model_name):

timm/models/helpers.py

@@ -323,17 +323,14 @@ def default_cfg_for_features(default_cfg):
     return default_cfg
 
 
-def overlay_external_default_cfg(kwargs, default_cfg):
-    """ Overlay 'default_cfg' in kwargs on top of default_cfg arg.
+def overlay_external_default_cfg(default_cfg, kwargs):
+    """ Overlay 'external_default_cfg' in kwargs on top of default_cfg arg.
     """
-    default_cfg = default_cfg or {}
     external_default_cfg = kwargs.pop('external_default_cfg', None)
     if external_default_cfg:
-        default_cfg = deepcopy(default_cfg)
         default_cfg.pop('url', None)  # url should come from external cfg
         default_cfg.pop('hf_hub', None)  # hf hub id should come from external cfg
         default_cfg.update(external_default_cfg)
-    return default_cfg
 
 
 def set_default_kwargs(kwargs, names, default_cfg):
@@ -344,7 +341,7 @@ def set_default_kwargs(kwargs, names, default_cfg):
             input_size = default_cfg.get('input_size', None)
             if input_size is not None:
                 assert len(input_size) == 3
-                kwargs.setdefault(n, input_size[:-2])
+                kwargs.setdefault(n, input_size[-2:])
         elif n == 'in_chans':
             input_size = default_cfg.get('input_size', None)
             if input_size is not None:
@@ -363,6 +360,25 @@ def filter_kwargs(kwargs, names):
         kwargs.pop(n, None)
 
 
+def update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter):
+    """ Update the default_cfg and kwargs before passing to model
+
+    FIXME this sequence of overlay default_cfg, set default kwargs, filter kwargs
+    could/should be replaced by an improved configuration mechanism
+
+    Args:
+        default_cfg: input default_cfg (updated in-place)
+        kwargs: keyword args passed to model build fn (updated in-place)
+        kwargs_filter: keyword arg keys that must be removed before model __init__
+    """
+    # Overlay default cfg values from `external_default_cfg` if it exists in kwargs
+    overlay_external_default_cfg(default_cfg, kwargs)
+    # Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
+    set_default_kwargs(kwargs, names=('num_classes', 'global_pool', 'in_chans'), default_cfg=default_cfg)
+    # Filter keyword args for task specific model variants (some 'features only' models, etc.)
+    filter_kwargs(kwargs, names=kwargs_filter)
+
+
 def build_model_with_cfg(
         model_cls: Callable,
         variant: str,
@@ -399,29 +415,20 @@ def build_model_with_cfg(
     pruned = kwargs.pop('pruned', False)
     features = False
     feature_cfg = feature_cfg or {}
+    default_cfg = deepcopy(default_cfg) if default_cfg else {}
+    update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter)
+    default_cfg.setdefault('architecture', variant)
 
-    # Setup for featyre extraction wrapper done at end of this fn
+    # Setup for feature extraction wrapper done at end of this fn
     if kwargs.pop('features_only', False):
         features = True
         feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
         if 'out_indices' in kwargs:
             feature_cfg['out_indices'] = kwargs.pop('out_indices')
 
-    # FIXME this next sequence of overlay default_cfg, set default kwargs, filter kwargs
-    # could/should be replaced by an improved configuration mechanism
-
-    # Overlay default cfg values from `external_default_cfg` if it exists in kwargs
-    default_cfg = overlay_external_default_cfg(kwargs, default_cfg)
-
-    # Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
-    set_default_kwargs(kwargs, names=('num_classes', 'global_pool', 'in_chans'), default_cfg=default_cfg)
-
-    # Filter keyword args for task specific model variants (some 'features only' models, etc.)
-    filter_kwargs(kwargs, names=kwargs_filter)
-
     # Build the model
     model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
-    model.default_cfg = deepcopy(default_cfg)
+    model.default_cfg = default_cfg
     
     if pruned:
         model = adapt_model_from_file(model, variant)

timm/models/hub.py

@@ -23,7 +23,7 @@ except ImportError:
 _logger = logging.getLogger(__name__)
 
 
-def get_cache_dir(child=''):
+def get_cache_dir(child_dir=''):
     """
     Returns the location of the directory where models are cached (and creates it if necessary).
     """
@@ -32,8 +32,8 @@ def get_cache_dir(child=''):
         _logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
 
     hub_dir = get_dir()
-    children = () if not child else child,
-    model_dir = os.path.join(hub_dir, 'checkpoints', *children)
+    child_dir = () if not child_dir else (child_dir,)
+    model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
     os.makedirs(model_dir, exist_ok=True)
     return model_dir
 
@@ -80,10 +80,13 @@ def _download_from_hf(model_id: str, filename: str):
     return cached_download(url, cache_dir=get_cache_dir('hf'))
 
 
-def load_config_from_hf(model_id: str):
+def load_model_config_from_hf(model_id: str):
     assert has_hf_hub(True)
     cached_file = _download_from_hf(model_id, 'config.json')
-    return load_cfg_from_json(cached_file)
+    default_cfg = load_cfg_from_json(cached_file)
+    default_cfg['hf_hub'] = model_id  # insert hf_hub id for pretrained weight load during model creation
+    model_name = default_cfg.get('architecture')
+    return default_cfg, model_name
 
 
 def load_state_dict_from_hf(model_id: str):

timm/models/vision_transformer.py

@@ -21,6 +21,7 @@ import math
 import logging
 from functools import partial
 from collections import OrderedDict
+from copy import deepcopy
 
 import torch
 import torch.nn as nn
@@ -462,9 +463,10 @@ def checkpoint_filter_fn(state_dict, model):
 
 
 def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs):
-    default_cfg = overlay_external_default_cfg(kwargs, default_cfgs[variant])
+    default_cfg = deepcopy(default_cfgs[variant])
+    overlay_external_default_cfg(default_cfg, kwargs)
     default_num_classes = default_cfg['num_classes']
-    default_img_size = default_cfg['input_size'][-1]
+    default_img_size = default_cfg['input_size'][-2:]
 
     num_classes = kwargs.pop('num_classes', default_num_classes)
     img_size = kwargs.pop('img_size', default_img_size)