Initial multi-weight support, handled so old pretraing config handling co-exists with new tags.

tests/test_models.py

@@ -13,8 +13,7 @@ except ImportError:
     has_fx_feature_extraction = False
 
 import timm
-from timm import list_models, create_model, set_scriptable, has_pretrained_cfg_key, is_pretrained_cfg_key, \
-    get_pretrained_cfg_value
+from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
 from timm.models.fx_features import _leaf_modules, _autowrap_functions    
 
 if hasattr(torch._C, '_jit_set_profiling_executor'):

timm/__init__.py

@@ -1,4 +1,4 @@
 from .version import __version__
 from .models import create_model, list_models, is_model, list_modules, model_entrypoint, \
-    is_scriptable, is_exportable, set_scriptable, set_exportable, has_pretrained_cfg_key, is_pretrained_cfg_key, \
-    get_pretrained_cfg_value, is_model_pretrained
+    is_scriptable, is_exportable, set_scriptable, set_exportable, \
+    is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value

timm/data/config.py

@@ -13,64 +13,64 @@ def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, v
 
     # Resolve input/image size
     in_chans = 3
-    if 'chans' in args and args['chans'] is not None:
+    if args.get('chans', None) is not None:
         in_chans = args['chans']
 
     input_size = (in_chans, 224, 224)
-    if 'input_size' in args and args['input_size'] is not None:
+    if args.get('input_size', None) is not None:
         assert isinstance(args['input_size'], (tuple, list))
         assert len(args['input_size']) == 3
         input_size = tuple(args['input_size'])
         in_chans = input_size[0]  # input_size overrides in_chans
-    elif 'img_size' in args and args['img_size'] is not None:
+    elif args.get('img_size', None) is not None:
         assert isinstance(args['img_size'], int)
         input_size = (in_chans, args['img_size'], args['img_size'])
     else:
-        if use_test_size and 'test_input_size' in default_cfg:
+        if use_test_size and default_cfg.get('test_input_size', None) is not None:
             input_size = default_cfg['test_input_size']
-        elif 'input_size' in default_cfg:
+        elif default_cfg.get('input_size', None) is not None:
             input_size = default_cfg['input_size']
     new_config['input_size'] = input_size
 
     # resolve interpolation method
     new_config['interpolation'] = 'bicubic'
-    if 'interpolation' in args and args['interpolation']:
+    if args.get('interpolation', None):
         new_config['interpolation'] = args['interpolation']
-    elif 'interpolation' in default_cfg:
+    elif default_cfg.get('interpolation', None):
         new_config['interpolation'] = default_cfg['interpolation']
 
     # resolve dataset + model mean for normalization
     new_config['mean'] = IMAGENET_DEFAULT_MEAN
-    if 'mean' in args and args['mean'] is not None:
+    if args.get('mean', None) is not None:
         mean = tuple(args['mean'])
         if len(mean) == 1:
             mean = tuple(list(mean) * in_chans)
         else:
             assert len(mean) == in_chans
         new_config['mean'] = mean
-    elif 'mean' in default_cfg:
+    elif default_cfg.get('mean', None):
         new_config['mean'] = default_cfg['mean']
 
     # resolve dataset + model std deviation for normalization
     new_config['std'] = IMAGENET_DEFAULT_STD
-    if 'std' in args and args['std'] is not None:
+    if args.get('std', None) is not None:
         std = tuple(args['std'])
         if len(std) == 1:
             std = tuple(list(std) * in_chans)
         else:
             assert len(std) == in_chans
         new_config['std'] = std
-    elif 'std' in default_cfg:
+    elif default_cfg.get('std', None):
         new_config['std'] = default_cfg['std']
 
     # resolve default crop percentage
     crop_pct = DEFAULT_CROP_PCT
-    if 'crop_pct' in args and args['crop_pct'] is not None:
+    if args.get('crop_pct', None):
         crop_pct = args['crop_pct']
     else:
-        if use_test_size and 'test_crop_pct' in default_cfg:
+        if use_test_size and default_cfg.get('test_crop_pct', None):
             crop_pct = default_cfg['test_crop_pct']
-        elif 'crop_pct' in default_cfg:
+        elif default_cfg.get('crop_pct', None):
             crop_pct = default_cfg['crop_pct']
     new_config['crop_pct'] = crop_pct
 

timm/models/__init__.py

@@ -71,4 +71,4 @@ from .layers import convert_splitbn_model, convert_sync_batchnorm
 from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit
 from .layers import set_fast_norm
 from .registry import register_model, model_entrypoint, list_models, is_model, list_modules, is_model_in_modules,\
-    is_model_pretrained, get_pretrained_cfg, has_pretrained_cfg_key, is_pretrained_cfg_key, get_pretrained_cfg_value
+    is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value

timm/models/_pretrained.py

@@ -0,0 +1,102 @@
+from collections import deque, defaultdict
+from dataclasses import dataclass, field, replace
+from typing import Any, Deque, Dict, Tuple, Optional, Union
+
+
+@dataclass
+class PretrainedCfg:
+    """
+    """
+    # weight locations
+    url: str = ''
+    file: str = ''
+    hf_hub_id: str = ''
+    hf_hub_filename: str = ''
+
+    source: str = ''  # source of cfg / weight location used (url, file, hf-hub)
+    architecture: str = ''  # architecture variant can be set when not implicit
+    custom_load: bool = False  # use custom model specific model.load_pretrained() (ie for npz files)
+
+    # input / data config
+    input_size: Tuple[int, int, int] = (3, 224, 224)
+    test_input_size: Optional[Tuple[int, int, int]] = None
+    min_input_size: Optional[Tuple[int, int, int]] = None
+    fixed_input_size: bool = False
+    interpolation: str = 'bicubic'
+    crop_pct: float = 0.875
+    test_crop_pct: Optional[float] = None
+    crop_type: str = 'pct'
+    mean: Tuple[float, ...] = (0.485, 0.456, 0.406)
+    std: Tuple[float, ...] = (0.229, 0.224, 0.225)
+
+    # head config
+    num_classes: int = 1000
+    label_offset: int = 0
+
+    # model attributes that vary with above or required for pretrained adaptation
+    pool_size: Optional[Tuple[int, ...]] = None
+    test_pool_size: Optional[Tuple[int, ...]] = None
+    first_conv: str = ''
+    classifier: str = ''
+
+    license: str = ''
+    source_url: str = ''
+    paper: str = ''
+    notes: str = ''
+
+    @property
+    def has_weights(self):
+        return self.url.startswith('http') or self.file or self.hf_hub_id
+
+
+@dataclass
+class DefaultCfg:
+    tags: Deque[str] = field(default_factory=deque)  # priority queue of tags (first is default)
+    cfgs: Dict[str, PretrainedCfg] = field(default_factory=dict)  # pretrained cfgs by tag
+    is_pretrained: bool = False  # at least one of the configs has a pretrained source set
+
+    @property
+    def default(self):
+        return self.cfgs[self.tags[0]]
+
+    @property
+    def default_with_tag(self):
+        tag = self.tags[0]
+        return tag, self.cfgs[tag]
+
+
+def split_model_name_tag(model_name: str, no_tag=''):
+    model_name, *tag_list = model_name.split('.', 1)
+    tag = tag_list[0] if tag_list else no_tag
+    return model_name, tag
+
+
+def generate_defaults(cfgs: Dict[str, Union[Dict[str, Any], PretrainedCfg]]):
+    out = defaultdict(DefaultCfg)
+    default_set = set()  # no tag and tags ending with * are prioritized as default
+
+    for k, v in cfgs.items():
+        if isinstance(v, dict):
+            v = PretrainedCfg(**v)
+        has_weights = v.has_weights
+
+        model, tag = split_model_name_tag(k)
+        is_default_set = model in default_set
+        priority = not tag or (tag.endswith('*') and not is_default_set)
+        tag = tag.strip('*')
+
+        default_cfg = out[model]
+        if has_weights:
+            default_cfg.is_pretrained = True
+
+        if priority:
+            default_cfg.tags.appendleft(tag)
+            default_set.add(model)
+        elif has_weights and not default_set:
+            default_cfg.tags.appendleft(tag)
+        else:
+            default_cfg.tags.append(tag)
+
+        default_cfg.cfgs[tag] = v
+
+    return out

timm/models/factory.py

@@ -1,14 +1,18 @@
-from urllib.parse import urlsplit, urlunsplit
 import os
+from typing import Any, Dict, Optional, Union
+from urllib.parse import urlsplit
 
-from .registry import is_model, is_model_in_modules, model_entrypoint
+from ._pretrained import PretrainedCfg, split_model_name_tag
 from .helpers import load_checkpoint
-from .layers import set_layer_config
 from .hub import load_model_config_from_hf
+from .layers import set_layer_config
+from .registry import is_model, model_entrypoint
 
 
 def parse_model_name(model_name):
-    model_name = model_name.replace('hf_hub', 'hf-hub')  # NOTE for backwards compat, to deprecate hf_hub use
+    if model_name.startswith('hf_hub'):
+        # NOTE for backwards compat, deprecate hf_hub use
+        model_name = model_name.replace('hf_hub', 'hf-hub')
     parsed = urlsplit(model_name)
     assert parsed.scheme in ('', 'timm', 'hf-hub')
     if parsed.scheme == 'hf-hub':
@@ -20,6 +24,7 @@ def parse_model_name(model_name):
 
 
 def safe_model_name(model_name, remove_source=True):
+    # return a filename / path safe model name
     def make_safe(name):
         return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
     if remove_source:
@@ -28,20 +33,29 @@ def safe_model_name(model_name, remove_source=True):
 
 
 def create_model(
-        model_name,
-        pretrained=False,
-        pretrained_cfg=None,
-        checkpoint_path='',
-        scriptable=None,
-        exportable=None,
-        no_jit=None,
-        **kwargs):
+        model_name: str,
+        pretrained: bool = False,
+        pretrained_cfg: Optional[Union[str, Dict[str, Any], PretrainedCfg]] = None,
+        pretrained_cfg_overlay:  Optional[Dict[str, Any]] = None,
+        checkpoint_path: str = '',
+        scriptable: Optional[bool] = None,
+        exportable: Optional[bool] = None,
+        no_jit: Optional[bool] = None,
+        **kwargs,
+):
     """Create a model
 
+    Lookup model's entrypoint function and pass relevant args to create a new model.
+
+    **kwargs will be passed through entrypoint fn to timm.models.build_model_with_cfg()
+    and then the model class __init__(). kwargs values set to None are pruned before passing.
+
     Args:
         model_name (str): name of model to instantiate
         pretrained (bool): load pretrained ImageNet-1k weights if true
-        checkpoint_path (str): path of checkpoint to load after model is initialized
+        pretrained_cfg (Union[str, dict, PretrainedCfg]): pass in external pretrained_cfg for model
+        pretrained_cfg_overlay (dict): replace key-values in base pretrained_cfg with these
+        checkpoint_path (str): path of checkpoint to load _after_ the model is initialized
         scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
         exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
         no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
@@ -49,7 +63,7 @@ def create_model(
     Keyword Args:
         drop_rate (float): dropout rate for training (default: 0.0)
         global_pool (str): global pool type (default: 'avg')
-        **: other kwargs are model specific
+        **: other kwargs are consumed by builder or model __init__()
     """
     # Parameters that aren't supported by all models or are intended to only override model defaults if set
     # should default to None in command line args/cfg. Remove them if they are present and not set so that
@@ -58,17 +72,27 @@ def create_model(
 
     model_source, model_name = parse_model_name(model_name)
     if model_source == 'hf-hub':
-        # FIXME hf-hub source overrides any passed in pretrained_cfg, warn?
+        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)
+    else:
+        model_name, pretrained_tag = split_model_name_tag(model_name)
+        if not pretrained_cfg:
+            # a valid pretrained_cfg argument takes priority over tag in model name
+            pretrained_cfg = pretrained_tag
 
     if not is_model(model_name):
         raise RuntimeError('Unknown model (%s)' % model_name)
 
     create_fn = model_entrypoint(model_name)
     with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
-        model = create_fn(pretrained=pretrained, pretrained_cfg=pretrained_cfg, **kwargs)
+        model = create_fn(
+            pretrained=pretrained,
+            pretrained_cfg=pretrained_cfg,
+            pretrained_cfg_overlay=pretrained_cfg_overlay,
+            **kwargs,
+        )
 
     if checkpoint_path:
         load_checkpoint(model, checkpoint_path)

timm/models/helpers.py

@@ -3,6 +3,7 @@
 Hacked together by / Copyright 2020 Ross Wightman
 """
 import collections.abc
+import dataclasses
 import logging
 import math
 import os
@@ -17,6 +18,7 @@ import torch.nn as nn
 from torch.hub import load_state_dict_from_url
 from torch.utils.checkpoint import checkpoint
 
+from ._pretrained import PretrainedCfg
 from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
 from .fx_features import FeatureGraphNet
 from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
@@ -194,7 +196,11 @@ def load_custom_pretrained(
         load_fn: An external standalone fn that loads weights into provided model, otherwise a fn named
             'laod_pretrained' on the model will be called if it exists
     """
-    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None) or {}
+    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
+    if not pretrained_cfg:
+        _logger.warning("Invalid pretrained config, cannot load weights.")
+        return
+
     load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
     if not load_from:
         _logger.warning("No pretrained weights exist for this model. Using random initialization.")
@@ -202,7 +208,11 @@ def load_custom_pretrained(
     if load_from == 'hf-hub':  # FIXME
         _logger.warning("Hugging Face hub not currently supported for custom load pretrained models.")
     elif load_from == 'url':
-        pretrained_loc = download_cached_file(pretrained_loc, check_hash=_CHECK_HASH, progress=_DOWNLOAD_PROGRESS)
+        pretrained_loc = download_cached_file(
+            pretrained_loc,
+            check_hash=_CHECK_HASH,
+            progress=_DOWNLOAD_PROGRESS
+        )
 
     if load_fn is not None:
         load_fn(model, pretrained_loc)
@@ -250,13 +260,17 @@ def load_pretrained(
     Args:
         model (nn.Module) : PyTorch model module
         pretrained_cfg (Optional[Dict]): configuration for pretrained weights / target dataset
-        num_classes (int): num_classes for model
-        in_chans (int): in_chans for model
+        num_classes (int): num_classes for target model
+        in_chans (int): in_chans for target model
         filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
         strict (bool): strict load of checkpoint
 
     """
-    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None) or {}
+    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
+    if not pretrained_cfg:
+        _logger.warning("Invalid pretrained config, cannot load weights.")
+        return
+
     load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
     if load_from == 'file':
         _logger.info(f'Loading pretrained weights from file ({pretrained_loc})')
@@ -264,7 +278,11 @@ def load_pretrained(
     elif load_from == 'url':
         _logger.info(f'Loading pretrained weights from url ({pretrained_loc})')
         state_dict = load_state_dict_from_url(
-            pretrained_loc, map_location='cpu', progress=_DOWNLOAD_PROGRESS, check_hash=_CHECK_HASH)
+            pretrained_loc,
+            map_location='cpu',
+            progress=_DOWNLOAD_PROGRESS,
+            check_hash=_CHECK_HASH,
+        )
     elif load_from == 'hf-hub':
         _logger.info(f'Loading pretrained weights from Hugging Face hub ({pretrained_loc})')
         if isinstance(pretrained_loc, (list, tuple)):
@@ -428,40 +446,20 @@ def adapt_model_from_file(parent_module, model_variant):
 def pretrained_cfg_for_features(pretrained_cfg):
     pretrained_cfg = deepcopy(pretrained_cfg)
     # remove default pretrained cfg fields that don't have much relevance for feature backbone
-    to_remove = ('num_classes', 'crop_pct', 'classifier', 'global_pool')  # add default final pool size?
+    to_remove = ('num_classes', 'classifier', 'global_pool')  # add default final pool size?
     for tr in to_remove:
         pretrained_cfg.pop(tr, None)
     return pretrained_cfg
 
 
-def set_default_kwargs(kwargs, names, pretrained_cfg):
-    for n in names:
-        # for legacy reasons, model __init__args uses img_size + in_chans as separate args while
-        # pretrained_cfg has one input_size=(C, H ,W) entry
-        if n == 'img_size':
-            input_size = pretrained_cfg.get('input_size', None)
-            if input_size is not None:
-                assert len(input_size) == 3
-                kwargs.setdefault(n, input_size[-2:])
-        elif n == 'in_chans':
-            input_size = pretrained_cfg.get('input_size', None)
-            if input_size is not None:
-                assert len(input_size) == 3
-                kwargs.setdefault(n, input_size[0])
-        else:
-            default_val = pretrained_cfg.get(n, None)
-            if default_val is not None:
-                kwargs.setdefault(n, pretrained_cfg[n])
-
-
-def filter_kwargs(kwargs, names):
+def _filter_kwargs(kwargs, names):
     if not kwargs or not names:
         return
     for n in names:
         kwargs.pop(n, None)
 
 
-def update_pretrained_cfg_and_kwargs(pretrained_cfg, kwargs, kwargs_filter):
+def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
     """ Update the default_cfg and kwargs before passing to model
 
     Args:
@@ -474,31 +472,61 @@ def update_pretrained_cfg_and_kwargs(pretrained_cfg, kwargs, kwargs_filter):
     if pretrained_cfg.get('fixed_input_size', False):
         # if fixed_input_size exists and is True, model takes an img_size arg that fixes its input size
         default_kwarg_names += ('img_size',)
-    set_default_kwargs(kwargs, names=default_kwarg_names, pretrained_cfg=pretrained_cfg)
+
+    for n in default_kwarg_names:
+        # for legacy reasons, model __init__args uses img_size + in_chans as separate args while
+        # pretrained_cfg has one input_size=(C, H ,W) entry
+        if n == 'img_size':
+            input_size = pretrained_cfg.get('input_size', None)
+            if input_size is not None:
+                assert len(input_size) == 3
+                kwargs.setdefault(n, input_size[-2:])
+        elif n == 'in_chans':
+            input_size = pretrained_cfg.get('input_size', None)
+            if input_size is not None:
+                assert len(input_size) == 3
+                kwargs.setdefault(n, input_size[0])
+        else:
+            default_val = pretrained_cfg.get(n, None)
+            if default_val is not None:
+                kwargs.setdefault(n, pretrained_cfg[n])
+
     # Filter keyword args for task specific model variants (some 'features only' models, etc.)
-    filter_kwargs(kwargs, names=kwargs_filter)
+    _filter_kwargs(kwargs, names=kwargs_filter)
 
 
-def resolve_pretrained_cfg(variant: str, pretrained_cfg=None):
-    if pretrained_cfg and isinstance(pretrained_cfg, dict):
-        # highest priority, pretrained_cfg available and passed as arg
-        return deepcopy(pretrained_cfg)
+def resolve_pretrained_cfg(
+        variant: str,
+        pretrained_cfg=None,
+        pretrained_cfg_overlay=None,
+) -> PretrainedCfg:
+    model_with_tag = variant
+    pretrained_tag = None
+    if pretrained_cfg:
+        if isinstance(pretrained_cfg, dict):
+            # pretrained_cfg dict passed as arg, validate by converting to PretrainedCfg
+            pretrained_cfg = PretrainedCfg(**pretrained_cfg)
+        elif isinstance(pretrained_cfg, str):
+            pretrained_tag = pretrained_cfg
+            pretrained_cfg = None
+
     # fallback to looking up pretrained cfg in model registry by variant identifier
-    pretrained_cfg = get_pretrained_cfg(variant)
+    if not pretrained_cfg:
+        if pretrained_tag:
+            model_with_tag = '.'.join([variant, pretrained_tag])
+        pretrained_cfg = get_pretrained_cfg(model_with_tag)
+
     if not pretrained_cfg:
         _logger.warning(
-            f"No pretrained configuration specified for {variant} model. Using a default."
+            f"No pretrained configuration specified for {model_with_tag} model. Using a default."
             f" Please add a config to the model pretrained_cfg registry or pass explicitly.")
-        pretrained_cfg = dict(
-            url='',
-            num_classes=1000,
-            input_size=(3, 224, 224),
-            pool_size=None,
-            crop_pct=.9,
-            interpolation='bicubic',
-            first_conv='',
-            classifier='',
-        )
+        pretrained_cfg = PretrainedCfg()  # instance with defaults
+
+    pretrained_cfg_overlay = pretrained_cfg_overlay or {}
+    if not pretrained_cfg.architecture:
+        pretrained_cfg_overlay.setdefault('architecture', variant)
+    pretrained_cfg = dataclasses.replace(pretrained_cfg, **pretrained_cfg_overlay)
+
     return pretrained_cfg
 
 
@@ -507,13 +535,14 @@ def build_model_with_cfg(
         variant: str,
         pretrained: bool,
         pretrained_cfg: Optional[Dict] = None,
+        pretrained_cfg_overlay: Optional[Dict] = None,
         model_cfg: Optional[Any] = None,
         feature_cfg: Optional[Dict] = None,
         pretrained_strict: bool = True,
         pretrained_filter_fn: Optional[Callable] = None,
-        pretrained_custom_load: bool = False,
         kwargs_filter: Optional[Tuple[str]] = None,
-        **kwargs):
+        **kwargs,
+):
     """ Build model with specified default_cfg and optional model_cfg
 
     This helper fn aids in the construction of a model including:
@@ -531,7 +560,6 @@ def build_model_with_cfg(
         feature_cfg (Optional[Dict]: feature extraction adapter config
         pretrained_strict (bool): load pretrained weights strictly
         pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights
-        pretrained_custom_load (bool): use custom load fn, to load numpy or other non PyTorch weights
         kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model
         **kwargs: model args passed through to model __init__
     """
@@ -540,9 +568,16 @@ def build_model_with_cfg(
     feature_cfg = feature_cfg or {}
 
     # resolve and update model pretrained config and model kwargs
-    pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=pretrained_cfg)
-    update_pretrained_cfg_and_kwargs(pretrained_cfg, kwargs, kwargs_filter)
-    pretrained_cfg.setdefault('architecture', variant)
+    pretrained_cfg = resolve_pretrained_cfg(
+        variant,
+        pretrained_cfg=pretrained_cfg,
+        pretrained_cfg_overlay=pretrained_cfg_overlay
+    )
+
+    # FIXME converting back to dict, PretrainedCfg use should be propagated further, but not into model
+    pretrained_cfg = dataclasses.asdict(pretrained_cfg)
+
+    _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter)
 
     # Setup for feature extraction wrapper done at end of this fn
     if kwargs.pop('features_only', False):
@@ -551,8 +586,11 @@ def build_model_with_cfg(
         if 'out_indices' in kwargs:
             feature_cfg['out_indices'] = kwargs.pop('out_indices')
 
-    # Build the model
-    model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
+    # Instantiate the model
+    if model_cfg is None:
+        model = model_cls(**kwargs)
+    else:
+        model = model_cls(cfg=model_cfg, **kwargs)
     model.pretrained_cfg = pretrained_cfg
     model.default_cfg = model.pretrained_cfg  # alias for backwards compat
     
@@ -562,9 +600,11 @@ 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:
-        if pretrained_custom_load:
-            # FIXME improve custom load trigger
-            load_custom_pretrained(model, pretrained_cfg=pretrained_cfg)
+        if pretrained_cfg.get('custom_load', False):
+            load_custom_pretrained(
+                model,
+                pretrained_cfg=pretrained_cfg,
+            )
         else:
             load_pretrained(
                 model,
@@ -572,7 +612,8 @@ def build_model_with_cfg(
                 num_classes=num_classes_pretrained,
                 in_chans=kwargs.get('in_chans', 3),
                 filter_fn=pretrained_filter_fn,
-                strict=pretrained_strict)
+                strict=pretrained_strict,
+            )
 
     # Wrap the model in a feature extraction module if enabled
     if features:

timm/models/inception_v3.py

@@ -27,24 +27,23 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     # original PyTorch weights, ported from Tensorflow but modified
     'inception_v3': _cfg(
-        url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
-        has_aux=True),  # checkpoint has aux logit layer weights
+        # NOTE checkpoint has aux logit layer weights
+        url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'),
     # my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
     'tf_inception_v3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
-        num_classes=1000, has_aux=False, label_offset=1),
+        num_classes=1000, label_offset=1),
     # my port of Tensorflow adversarially trained Inception V3 from
     # http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
     'adv_inception_v3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
-        num_classes=1000, has_aux=False, label_offset=1),
+        num_classes=1000, label_offset=1),
     # from gluon pretrained models, best performing in terms of accuracy/loss metrics
     # https://gluon-cv.mxnet.io/model_zoo/classification.html
     'gluon_inception_v3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
         mean=IMAGENET_DEFAULT_MEAN,  # also works well with inception defaults
         std=IMAGENET_DEFAULT_STD,  # also works well with inception defaults
-        has_aux=False,
     )
 }
 
@@ -433,10 +432,10 @@ def _create_inception_v3(variant, pretrained=False, **kwargs):
     if aux_logits:
         assert not kwargs.pop('features_only', False)
         model_cls = InceptionV3Aux
-        load_strict = pretrained_cfg['has_aux']
+        load_strict = variant == 'inception_v3'
     else:
         model_cls = InceptionV3
-        load_strict = not pretrained_cfg['has_aux']
+        load_strict = variant != 'inception_v3'
 
     return build_model_with_cfg(
         model_cls, variant, pretrained,

timm/models/registry.py

@@ -2,20 +2,30 @@
 Hacked together by / Copyright 2020 Ross Wightman
 """
 
-import sys
-import re
 import fnmatch
-from collections import defaultdict
+import re
+import sys
+from collections import defaultdict, deque
 from copy import deepcopy
+from typing import Optional, Tuple
 
-__all__ = ['list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
-           'is_pretrained_cfg_key', 'has_pretrained_cfg_key', 'get_pretrained_cfg_value', 'is_model_pretrained']
+from ._pretrained import PretrainedCfg, DefaultCfg, split_model_name_tag
+
+__all__ = [
+    'list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
+    'get_pretrained_cfg_value', 'is_model_pretrained', 'get_arch_name']
 
 _module_to_models = defaultdict(set)  # dict of sets to check membership of model in module
 _model_to_module = {}  # mapping of model names to module names
-_model_entrypoints = {}  # mapping of model names to entrypoint fns
+_model_entrypoints = {}  # mapping of model names to architecture entrypoint fns
 _model_has_pretrained = set()  # set of model names that have pretrained weight url present
-_model_pretrained_cfgs = dict()  # central repo for model default_cfgs
+_model_default_cfgs = dict()  # central repo for model arch -> default cfg objects
+_model_pretrained_cfgs = dict()  # central repo for model arch + tag -> pretrained cfgs
+_model_with_tags = defaultdict(list)  # shortcut to map each model arch to all model + tag names
+
+
+def get_arch_name(model_name: str) -> Tuple[str, Optional[str]]:
+    return split_model_name_tag(model_name)[0]
 
 
 def register_model(fn):
@@ -35,19 +45,37 @@ def register_model(fn):
     _model_entrypoints[model_name] = fn
     _model_to_module[model_name] = module_name
     _module_to_models[module_name].add(model_name)
-    has_valid_pretrained = False  # check if model has a pretrained url to allow filtering on this
     if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
         # this will catch all models that have entrypoint matching cfg key, but miss any aliasing
         # entrypoints or non-matching combos
         cfg = mod.default_cfgs[model_name]
-        has_valid_pretrained = (
-            ('url' in cfg and 'http' in cfg['url']) or
-            ('file' in cfg and cfg['file']) or
-            ('hf_hub_id' in cfg and cfg['hf_hub_id'])
-        )
-        _model_pretrained_cfgs[model_name] = mod.default_cfgs[model_name]
-    if has_valid_pretrained:
+        if not isinstance(cfg, DefaultCfg):
+            # new style default cfg dataclass w/ multiple entries per model-arch
+            assert isinstance(cfg, dict)
+            # old style cfg dict per model-arch
+            cfg = PretrainedCfg(**cfg)
+            cfg = DefaultCfg(tags=deque(['']), cfgs={'': cfg})
+
+        for tag_idx, tag in enumerate(cfg.tags):
+            is_default = tag_idx == 0
+            pretrained_cfg = cfg.cfgs[tag]
+            if is_default:
+                _model_pretrained_cfgs[model_name] = pretrained_cfg
+                if pretrained_cfg.has_weights:
+                    # add tagless entry if it's default and has weights
                     _model_has_pretrained.add(model_name)
+            if tag:
+                model_name_tag = '.'.join([model_name, tag])
+                _model_pretrained_cfgs[model_name_tag] = pretrained_cfg
+                if pretrained_cfg.has_weights:
+                    # add model w/ tag if tag is valid
+                    _model_has_pretrained.add(model_name_tag)
+                _model_with_tags[model_name].append(model_name_tag)
+            else:
+                _model_with_tags[model_name].append(model_name)  # has empty tag (to slowly remove these instances)
+
+        _model_default_cfgs[model_name] = cfg
+
     return fn
 
 
@@ -55,24 +83,39 @@ def _natural_key(string_):
     return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
 
 
-def list_models(filter='', module='', pretrained=False, exclude_filters='', name_matches_cfg=False):
+def list_models(
+        filter: str = '',
+        module: str = '',
+        pretrained=False,
+        exclude_filters: str = '',
+        name_matches_cfg: bool = False,
+        include_tags: Optional[bool] = None,
+):
     """ Return list of available model names, sorted alphabetically
 
     Args:
         filter (str) - Wildcard filter string that works with fnmatch
-        module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
-        pretrained (bool) - Include only models with pretrained weights if True
+        module (str) - Limit model selection to a specific submodule (ie 'vision_transformer')
+        pretrained (bool) - Include only models with valid pretrained weights if True
         exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
         name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
-
+        include_tags (Optional[boo]) - Include pretrained tags in model names (model.tag). If None, defaults
+            set to True when pretrained=True else False (default: None)
     Example:
         model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
         model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
     """
+    if include_tags is None:
+        # FIXME should this be default behaviour? or default to include_tags=True?
+        include_tags = pretrained
+
     if module:
         all_models = list(_module_to_models[module])
     else:
         all_models = _model_entrypoints.keys()
+
+    # FIXME wildcard filter tag as well as model arch name
+
     if filter:
         models = []
         include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
@@ -82,6 +125,7 @@ def list_models(filter='', module='', pretrained=False, exclude_filters='', name
                 models = set(models).union(include_models)
     else:
         models = all_models
+
     if exclude_filters:
         if not isinstance(exclude_filters, (tuple, list)):
             exclude_filters = [exclude_filters]
@@ -89,23 +133,35 @@ def list_models(filter='', module='', pretrained=False, exclude_filters='', name
             exclude_models = fnmatch.filter(models, xf)  # exclude these models
             if len(exclude_models):
                 models = set(models).difference(exclude_models)
+
+    if include_tags:
+        # expand model names to include names w/ pretrained tags
+        models_with_tags = []
+        for m in models:
+            models_with_tags.extend(_model_with_tags[m])
+        models = models_with_tags
+
     if pretrained:
         models = _model_has_pretrained.intersection(models)
+
     if name_matches_cfg:
         models = set(_model_pretrained_cfgs).intersection(models)
+
     return list(sorted(models, key=_natural_key))
 
 
 def is_model(model_name):
     """ Check if a model name exists
     """
-    return model_name in _model_entrypoints
+    arch_name = get_arch_name(model_name)
+    return arch_name in _model_entrypoints
 
 
 def model_entrypoint(model_name):
     """Fetch a model entrypoint for specified model name
     """
-    return _model_entrypoints[model_name]
+    arch_name = get_arch_name(model_name)
+    return _model_entrypoints[arch_name]
 
 
 def list_modules():
@@ -121,8 +177,9 @@ def is_model_in_modules(model_name, module_names):
         model_name (str) - name of model to check
         module_names (tuple, list, set) - names of modules to search in
     """
+    arch_name = get_arch_name(model_name)
     assert isinstance(module_names, (tuple, list, set))
-    return any(model_name in _module_to_models[n] for n in module_names)
+    return any(arch_name in _module_to_models[n] for n in module_names)
 
 
 def is_model_pretrained(model_name):
@@ -132,28 +189,12 @@ def is_model_pretrained(model_name):
 def get_pretrained_cfg(model_name):
     if model_name in _model_pretrained_cfgs:
         return deepcopy(_model_pretrained_cfgs[model_name])
-    return {}
-
-
-def has_pretrained_cfg_key(model_name, cfg_key):
-    """ Query model default_cfgs for existence of a specific key.
-    """
-    if model_name in _model_pretrained_cfgs and cfg_key in _model_pretrained_cfgs[model_name]:
-        return True
-    return False
-
-
-def is_pretrained_cfg_key(model_name, cfg_key):
-    """ Return truthy value for specified model default_cfg key, False if does not exist.
-    """
-    if model_name in _model_pretrained_cfgs and _model_pretrained_cfgs[model_name].get(cfg_key, False):
-        return True
-    return False
+    raise RuntimeError(f'No pretrained config exists for model {model_name}.')
 
 
 def get_pretrained_cfg_value(model_name, cfg_key):
-    """ Get a specific model default_cfg value by key. None if it doesn't exist.
+    """ Get a specific model default_cfg value by key. None if key doesn't exist.
     """
     if model_name in _model_pretrained_cfgs:
-        return _model_pretrained_cfgs[model_name].get(cfg_key, None)
-    return None
+        return getattr(_model_pretrained_cfgs[model_name], cfg_key, None)
+    raise RuntimeError(f'No pretrained config exist for model {model_name}.')

timm/models/regnet.py

@@ -76,6 +76,9 @@ model_cfgs = dict(
     regnety_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
     regnety_160=RegNetCfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
     regnety_320=RegNetCfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
+    regnety_640=RegNetCfg(w0=352, wa=147.48, wm=2.4, group_size=328, depth=20, se_ratio=0.25),
+    regnety_1280=RegNetCfg(w0=456, wa=160.83, wm=2.52, group_size=264, depth=27, se_ratio=0.25),
+    regnety_2560=RegNetCfg(w0=640, wa=124.47, wm=2.04, group_size=848, depth=27, se_ratio=0.25),
 
     # Experimental
     regnety_040s_gn=RegNetCfg(
@@ -150,7 +153,12 @@ default_cfgs = dict(
     regnety_160=_cfg(
         url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth',  # from Facebook DeiT GitHub repository
         crop_pct=1.0, test_input_size=(3, 288, 288)),
-    regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
+    regnety_320=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'
+    ),
+    regnety_640=_cfg(url=''),
+    regnety_1280=_cfg(url=''),
+    regnety_2560=_cfg(url=''),
 
     regnety_040s_gn=_cfg(url=''),
     regnetv_040=_cfg(
@@ -508,6 +516,34 @@ def _init_weights(module, name='', zero_init_last=False):
 
 def _filter_fn(state_dict):
     """ convert patch embedding weight from manual patchify + linear proj to conv"""
+    if 'classy_state_dict' in state_dict:
+        import re
+        state_dict = state_dict['classy_state_dict']['base_model']['model']
+        out = {}
+        for k, v in state_dict['trunk'].items():
+            k = k.replace('_feature_blocks.conv1.stem.0', 'stem.conv')
+            k = k.replace('_feature_blocks.conv1.stem.1', 'stem.bn')
+            k = re.sub(
+                r'^_feature_blocks.res\d.block(\d)-(\d+)',
+                lambda x: f's{int(x.group(1))}.b{int(x.group(2)) + 1}', k)
+            k = re.sub(r's(\d)\.b(\d+)\.bn', r's\1.b\2.downsample.bn', k)
+            k = k.replace('proj', 'downsample.conv')
+            k = k.replace('f.a.0', 'conv1.conv')
+            k = k.replace('f.a.1', 'conv1.bn')
+            k = k.replace('f.b.0', 'conv2.conv')
+            k = k.replace('f.b.1', 'conv2.bn')
+            k = k.replace('f.c', 'conv3.conv')
+            k = k.replace('f.final_bn', 'conv3.bn')
+            k = k.replace('f.se.excitation.0', 'se.fc1')
+            k = k.replace('f.se.excitation.2', 'se.fc2')
+            out[k] = v
+        for k, v in state_dict['heads'].items():
+            if 'projection_head' in k or 'prototypes' in k:
+                continue
+            k = k.replace('0.clf.0', 'head.fc')
+            out[k] = v
+        return out
+
     if 'model' in state_dict:
         # For DeiT trained regnety_160 pretraiend model
         state_dict = state_dict['model']
@@ -666,6 +702,24 @@ def regnety_320(pretrained=False, **kwargs):
     return _create_regnet('regnety_320', pretrained, **kwargs)
 
 
+@register_model
+def regnety_640(pretrained=False, **kwargs):
+    """RegNetY-64GF"""
+    return _create_regnet('regnety_640', pretrained, **kwargs)
+
+
+@register_model
+def regnety_1280(pretrained=False, **kwargs):
+    """RegNetY-128GF"""
+    return _create_regnet('regnety_1280', pretrained, **kwargs)
+
+
+@register_model
+def regnety_2560(pretrained=False, **kwargs):
+    """RegNetY-256GF"""
+    return _create_regnet('regnety_2560', pretrained, **kwargs)
+
+
 @register_model
 def regnety_040s_gn(pretrained=False, **kwargs):
     """RegNetY-4.0GF w/ GroupNorm """

timm/models/resnetv2.py

@@ -57,52 +57,52 @@ default_cfgs = {
     # pretrained on imagenet21k, finetuned on imagenet1k
     'resnetv2_50x1_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz',
-        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
+        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
     'resnetv2_50x3_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz',
-        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
+        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
     'resnetv2_101x1_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz',
-        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
+        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
     'resnetv2_101x3_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz',
-        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
+        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
     'resnetv2_152x2_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz',
-        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
+        input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
     'resnetv2_152x4_bitm': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz',
-        input_size=(3, 480, 480), pool_size=(15, 15), crop_pct=1.0),  # only one at 480x480?
+        input_size=(3, 480, 480), pool_size=(15, 15), crop_pct=1.0, custom_load=True),  # only one at 480x480?
 
     # trained on imagenet-21k
     'resnetv2_50x1_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R50x1.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
     'resnetv2_50x3_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R50x3.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
     'resnetv2_101x1_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R101x1.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
     'resnetv2_101x3_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R101x3.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
     'resnetv2_152x2_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R152x2.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
     'resnetv2_152x4_bitm_in21k': _cfg(
         url='https://storage.googleapis.com/bit_models/BiT-M-R152x4.npz',
-        num_classes=21843),
+        num_classes=21843, custom_load=True),
 
     'resnetv2_50x1_bit_distilled': _cfg(
         url='https://storage.googleapis.com/bit_models/distill/R50x1_224.npz',
-        interpolation='bicubic'),
+        interpolation='bicubic', custom_load=True),
     'resnetv2_152x2_bit_teacher': _cfg(
         url='https://storage.googleapis.com/bit_models/distill/R152x2_T_224.npz',
-        interpolation='bicubic'),
+        interpolation='bicubic', custom_load=True),
     'resnetv2_152x2_bit_teacher_384': _cfg(
         url='https://storage.googleapis.com/bit_models/distill/R152x2_T_384.npz',
-        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic'),
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic', custom_load=True),
 
     'resnetv2_50': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetv2_50_a1h-000cdf49.pth',
@@ -507,8 +507,8 @@ def _create_resnetv2(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         ResNetV2, variant, pretrained,
         feature_cfg=feature_cfg,
-        pretrained_custom_load='_bit' in variant,
-        **kwargs)
+        **kwargs,
+    )
 
 
 def _create_resnetv2_bit(variant, pretrained=False, **kwargs):

timm/models/vision_transformer.py

@@ -34,6 +34,7 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCE
     OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
 from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply, adapt_input_conv, checkpoint_seq
 from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
+from ._pretrained import generate_defaults
 from .registry import register_model
 
 _logger = logging.getLogger(__name__)
@@ -50,59 +51,50 @@ def _cfg(url='', **kwargs):
     }
 
 
-default_cfgs = {
+default_cfgs = generate_defaults({
     # patch models (weights from official Google JAX impl)
-    'vit_tiny_patch16_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
-    'vit_tiny_patch16_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_small_patch32_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
-    'vit_small_patch32_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_small_patch16_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
-    'vit_small_patch16_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_base_patch32_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
-    'vit_base_patch32_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_base_patch16_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
-    'vit_base_patch16_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_base_patch8_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
-    'vit_large_patch32_224': _cfg(
-        url='',  # no official model weights for this combo, only for in21k
-        ),
-    'vit_large_patch32_384': _cfg(
+    'vit_tiny_patch16_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True),
+    'vit_tiny_patch16_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_small_patch32_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True),
+    'vit_small_patch32_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_small_patch16_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True),
+    'vit_small_patch16_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_base_patch32_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True),
+    'vit_base_patch32_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_base_patch16_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
+        custom_load=True),
+    'vit_base_patch16_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_base_patch8_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
+        custom_load=True),
+    'vit_large_patch32_384.in21ft1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
         input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_large_patch16_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
-    'vit_large_patch16_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_large_patch16_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
+        custom_load=True),
+    'vit_large_patch16_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
+        custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
 
     'vit_large_patch14_224': _cfg(url=''),
     'vit_huge_patch14_224': _cfg(url=''),
@@ -111,92 +103,128 @@ default_cfgs = {
 
 
     # patch models, imagenet21k (weights from official Google JAX impl)
-    'vit_tiny_patch16_224_in21k': _cfg(
+    'vit_tiny_patch16_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_small_patch32_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_small_patch32_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_small_patch16_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_small_patch16_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_base_patch32_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_base_patch32_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_base_patch16_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_base_patch16_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_base_patch8_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_base_patch8_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
-        num_classes=21843),
-    'vit_large_patch32_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_large_patch32_224.in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
         num_classes=21843),
-    'vit_large_patch16_224_in21k': _cfg(
+    'vit_large_patch16_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
-        num_classes=21843),
-    'vit_huge_patch14_224_in21k': _cfg(
+        custom_load=True, num_classes=21843),
+    'vit_huge_patch14_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
         hf_hub_id='timm/vit_huge_patch14_224_in21k',
-        num_classes=21843),
+        custom_load=True, num_classes=21843),
 
     # SAM trained models (https://arxiv.org/abs/2106.01548)
-    'vit_base_patch32_224_sam': _cfg(
-        url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
-    'vit_base_patch16_224_sam': _cfg(
-        url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
+    'vit_base_patch32_224.sam': _cfg(
+        url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz', custom_load=True),
+    'vit_base_patch16_224.sam': _cfg(
+        url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz', custom_load=True),
 
     # DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
-    'vit_small_patch16_224_dino': _cfg(
+    'vit_small_patch16_224.dino': _cfg(
         url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_small_patch8_224_dino': _cfg(
+    'vit_small_patch8_224.dino': _cfg(
         url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_base_patch16_224_dino': _cfg(
+    'vit_base_patch16_224.dino': _cfg(
         url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_base_patch8_224_dino': _cfg(
+    'vit_base_patch8_224.dino': _cfg(
         url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
 
 
     # ViT ImageNet-21K-P pretraining by MILL
-    'vit_base_patch16_224_miil_in21k': _cfg(
+    'vit_base_patch16_224_miil.in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
         mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221),
-    'vit_base_patch16_224_miil': _cfg(
+    'vit_base_patch16_224_miil.in21ft1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.pth',
         mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'),
 
+    # custom timm variants
     'vit_base_patch16_rpn_224': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'),
-
-    # experimental (may be removed)
-    'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
-    'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
-    'vit_small_patch16_36x1_224': _cfg(url=''),
-    'vit_small_patch16_18x2_224': _cfg(url=''),
-    'vit_base_patch16_18x2_224': _cfg(url=''),
-
-    'vit_base_patch32_224_clip_laion2b': _cfg(
+    'vit_medium_patch16_gap_240.in12k': _cfg(
+        url='',
+        input_size=(3, 240, 240), crop_pct=0.95, num_classes=11821),
+    'vit_medium_patch16_gap_256.in12ft1k': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
+    'vit_medium_patch16_gap_384.in12ft1k': _cfg(url='', input_size=(3, 384, 384), crop_pct=0.95),
+
+    # CLIP pretrained image tower and related fine-tuned weights
+    'vit_base_patch32_224_clip.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
-    'vit_large_patch14_224_clip_laion2b': _cfg(
+    'vit_large_patch14_224_clip.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=768),
-    'vit_huge_patch14_224_clip_laion2b': _cfg(
+    'vit_huge_patch14_224_clip.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),
-    'vit_giant_patch14_224_clip_laion2b': _cfg(
+    'vit_giant_patch14_224_clip.laion2b': _cfg(
         hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),
 
-}
+    'vit_base_patch32_224_clip.laion2b_ft_in1k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
+    'vit_large_patch14_224_clip.laion2b_ft_in1k': _cfg(
+        hf_hub_id='',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
+    'vit_huge_patch14_224_clip.laion2b_ft_in1k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
+
+    'vit_base_patch32_224_clip.laion2b_ft_in12k_in1k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
+    'vit_large_patch14_224_clip.laion2b_ft_in12k_in1k': _cfg(
+        hf_hub_id='',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
+    'vit_huge_patch14_224_clip.laion2b_ft_in12k_in1k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
+
+    'vit_base_patch32_224_clip.laion2b_ft_in12k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
+    'vit_large_patch14_224_clip.laion2b_ft_in12k': _cfg(
+        hf_hub_id='',
+        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=11821),
+    'vit_huge_patch14_224_clip.laion2b_ft_in12k': _cfg(
+        hf_hub_id='',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
+
+    # experimental (may be removed)
+    'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
+    'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
+    'vit_small_patch16_36x1_224': _cfg(url=''),
+    'vit_small_patch16_18x2_224': _cfg(url=''),
+    'vit_base_patch16_18x2_224': _cfg(url=''),
+})
 
 
 class Attention(nn.Module):
@@ -782,14 +810,11 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs):
     if kwargs.get('features_only', None):
         raise RuntimeError('features_only not implemented for Vision Transformer models.')
 
-    pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
-    model = build_model_with_cfg(
+    return build_model_with_cfg(
         VisionTransformer, variant, pretrained,
-        pretrained_cfg=pretrained_cfg,
         pretrained_filter_fn=checkpoint_filter_fn,
-        pretrained_custom_load='npz' in pretrained_cfg['url'],
-        **kwargs)
-    return model
+        **kwargs,
+    )
 
 
 @register_model
@@ -831,7 +856,6 @@ def vit_small_patch32_384(pretrained=False, **kwargs):
 @register_model
 def vit_small_patch16_224(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
-    NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
     """
     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
@@ -841,13 +865,21 @@ def vit_small_patch16_224(pretrained=False, **kwargs):
 @register_model
 def vit_small_patch16_384(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
-    NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
     """
     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
+@register_model
+def vit_small_patch8_224(pretrained=False, **kwargs):
+    """ ViT-Small (ViT-S/8)
+    """
+    model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_base_patch32_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
@@ -974,175 +1006,90 @@ def vit_gigantic_patch14_224(pretrained=False, **kwargs):
 
 
 @register_model
-def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Tiny (Vit-Ti/16).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
-    """
-    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
-    model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Small (ViT-S/16)
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
-    """
-    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
-    model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Small (ViT-S/16)
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
+def vit_base_patch16_224_miil(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
-    model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
+def vit_base_patch32_224_clip(pretrained=False, **kwargs):
+    """ ViT-B/32
+    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
     """
-    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer('vit_base_patch32_224_clip', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
+def vit_medium_patch16_gap_240(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-M/16) w/o class token, w/ avg-pool @ 240x240
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+    model = _create_vision_transformer('vit_medium_patch16_gap_240', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch8_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Base model (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
+def vit_medium_patch16_gap_256(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-M/16) w/o class token, w/ avg-pool @ 256x256
     """
-    model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch8_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+    model = _create_vision_transformer('vit_medium_patch16_gap_256', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
+def vit_medium_patch16_gap_384(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-M/16) w/o class token, w/ avg-pool @ 384x384
     """
-    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
-    model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
+        global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs)
+    model = _create_vision_transformer('vit_medium_patch16_gap_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
-    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
+def vit_large_patch14_224_clip(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/14)
+    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
-    model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer('vit_large_patch14_224_clip', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
+def vit_huge_patch14_224_clip(pretrained=False, **kwargs):
     """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
-    """
-    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
-    model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch16_224_sam(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
-    """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch32_224_sam(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
-    """
-    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_patch16_224_dino(pretrained=False, **kwargs):
-    """ ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
-    """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
-    model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_patch8_224_dino(pretrained=False, **kwargs):
-    """ ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
-    """
-    model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
-    model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch16_224_dino(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
-    """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch8_224_dino(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
-    """
-    model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
-    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
+    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer('vit_huge_patch14_224_clip', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch16_224_miil(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
-    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
+def vit_giant_patch14_224_clip(pretrained=False, **kwargs):
+    """ ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16,
+        pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
+    model = _create_vision_transformer('vit_giant_patch14_224_clip', pretrained=pretrained, **model_kwargs)
     return model
 
 
@@ -1211,46 +1158,3 @@ def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_base_patch32_224_clip_laion2b(pretrained=False, **kwargs):
-    """ ViT-B/32
-    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
-    """
-    model_kwargs = dict(
-        patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
-    model = _create_vision_transformer('vit_base_patch32_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_large_patch14_224_clip_laion2b(pretrained=False, **kwargs):
-    """ ViT-Large model (ViT-L/14)
-    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
-    """
-    model_kwargs = dict(
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
-    model = _create_vision_transformer('vit_large_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_huge_patch14_224_clip_laion2b(pretrained=False, **kwargs):
-    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
-    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
-    """
-    model_kwargs = dict(
-        patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
-    model = _create_vision_transformer('vit_huge_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_giant_patch14_224_clip_laion2b(pretrained=False, **kwargs):
-    """ ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
-    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
-    """
-    model_kwargs = dict(
-        patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16,
-        pre_norm=True, norm_layer=nn.LayerNorm, **kwargs)
-    model = _create_vision_transformer('vit_giant_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
-    return model

timm/models/vision_transformer_hybrid.py

@@ -20,6 +20,7 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from ._pretrained import generate_defaults
 from .layers import StdConv2dSame, StdConv2d, to_2tuple
 from .resnet import resnet26d, resnet50d
 from .resnetv2 import ResNetV2, create_resnetv2_stem
@@ -38,52 +39,49 @@ def _cfg(url='', **kwargs):
     }
 
 
-default_cfgs = {
+default_cfgs = generate_defaults({
     # hybrid in-1k models (weights from official JAX impl where they exist)
-    'vit_tiny_r_s16_p8_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+    'vit_tiny_r_s16_p8_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True,
         first_conv='patch_embed.backbone.conv'),
-    'vit_tiny_r_s16_p8_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_small_r26_s32_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+    'vit_tiny_r_s16_p8_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
+    'vit_small_r26_s32_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz',
+        custom_load=True,
     ),
-    'vit_small_r26_s32_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0),
+    'vit_small_r26_s32_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
+        input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
     'vit_base_r26_s32_224': _cfg(),
     'vit_base_r50_s16_224': _cfg(),
-    'vit_base_r50_s16_384': _cfg(
+    'vit_base_r50_s16_384.in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
         input_size=(3, 384, 384), crop_pct=1.0),
-    'vit_large_r50_s32_224': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'
+    'vit_large_r50_s32_224.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
+        custom_load=True,
     ),
-    'vit_large_r50_s32_384': _cfg(
-        url='https://storage.googleapis.com/vit_models/augreg/'
-            'R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
-        input_size=(3, 384, 384), crop_pct=1.0
+    'vit_large_r50_s32_384.in21ft1k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
+        input_size=(3, 384, 384), crop_pct=1.0, custom_load=True,
     ),
 
     # hybrid in-21k models (weights from official Google JAX impl where they exist)
-    'vit_tiny_r_s16_p8_224_in21k': _cfg(
+    'vit_tiny_r_s16_p8_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv'),
-    'vit_small_r26_s32_224_in21k': _cfg(
+        num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv', custom_load=True),
+    'vit_small_r26_s32_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz',
-        num_classes=21843, crop_pct=0.9),
-    'vit_base_r50_s16_224_in21k': _cfg(
+        num_classes=21843, crop_pct=0.9, custom_load=True),
+    'vit_base_r50_s16_224.in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
         num_classes=21843, crop_pct=0.9),
-    'vit_large_r50_s32_224_in21k': _cfg(
+    'vit_large_r50_s32_224.in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz',
-        num_classes=21843, crop_pct=0.9),
+        num_classes=21843, crop_pct=0.9, custom_load=True),
 
     # hybrid models (using timm resnet backbones)
     'vit_small_resnet26d_224': _cfg(
@@ -94,7 +92,7 @@ default_cfgs = {
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
     'vit_base_resnet50d_224': _cfg(
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
-}
+})
 
 
 class HybridEmbed(nn.Module):
@@ -248,12 +246,6 @@ def vit_base_r50_s16_384(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_base_resnet50_384(pretrained=False, **kwargs):
-    # DEPRECATED this is forwarding to model def above for backwards compatibility
-    return vit_base_r50_s16_384(pretrained=pretrained, **kwargs)
-
-
 @register_model
 def vit_large_r50_s32_224(pretrained=False, **kwargs):
     """ R50+ViT-L/S32 hybrid.
@@ -276,57 +268,6 @@ def vit_large_r50_s32_384(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_tiny_r_s16_p8_224_in21k(pretrained=False, **kwargs):
-    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid.  ImageNet-21k.
-    """
-    backbone = _resnetv2(layers=(), **kwargs)
-    model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
-    model = _create_vision_transformer_hybrid(
-        'vit_tiny_r_s16_p8_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_r26_s32_224_in21k(pretrained=False, **kwargs):
-    """ R26+ViT-S/S32 hybrid. ImageNet-21k.
-    """
-    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
-    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
-    model = _create_vision_transformer_hybrid(
-        'vit_small_r26_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
-    """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    """
-    backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
-    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
-    model = _create_vision_transformer_hybrid(
-        'vit_base_r50_s16_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
-    # DEPRECATED this is forwarding to model def above for backwards compatibility
-    return vit_base_r50_s16_224_in21k(pretrained=pretrained, **kwargs)
-
-
-@register_model
-def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs):
-    """ R50+ViT-L/S32 hybrid. ImageNet-21k.
-    """
-    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
-    model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
-    model = _create_vision_transformer_hybrid(
-        'vit_large_r50_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
-    return model
-
-
 @register_model
 def vit_small_resnet26d_224(pretrained=False, **kwargs):
     """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.