Update efficientnet.py and convnext.py to multi-weight, add ImageNet-12k pretrained EfficientNet-B5 and ConvNeXt-Nano.

timm/models/_builder.py

@@ -1,5 +1,6 @@
 import dataclasses
 import logging
+import os
 from copy import deepcopy
 from typing import Optional, Dict, Callable, Any, Tuple
 
@@ -9,7 +10,7 @@ from torch.hub import load_state_dict_from_url
 from timm.models._features import FeatureListNet, FeatureHookNet
 from timm.models._features_fx import FeatureGraphNet
 from timm.models._helpers import load_state_dict
-from timm.models._hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
+from timm.models._hub import has_hf_hub, download_cached_file, check_cached_file, load_state_dict_from_hf
 from timm.models._manipulate import adapt_input_conv
 from timm.models._pretrained import PretrainedCfg
 from timm.models._prune import adapt_model_from_file
@@ -32,6 +33,7 @@ def _resolve_pretrained_source(pretrained_cfg):
     pretrained_url = pretrained_cfg.get('url', None)
     pretrained_file = pretrained_cfg.get('file', None)
     hf_hub_id = pretrained_cfg.get('hf_hub_id', None)
+
     # resolve where to load pretrained weights from
     load_from = ''
     pretrained_loc = ''
@@ -43,15 +45,20 @@ def _resolve_pretrained_source(pretrained_cfg):
     else:
         # default source == timm or unspecified
         if pretrained_file:
+            # file load override is the highest priority if set
             load_from = 'file'
             pretrained_loc = pretrained_file
-        elif pretrained_url:
+        else:
-            load_from = 'url'
+            # next, HF hub is prioritized unless a valid cached version of weights exists already
-            pretrained_loc = pretrained_url
+            cached_url_valid = check_cached_file(pretrained_url) if pretrained_url else False
-        elif hf_hub_id and has_hf_hub(necessary=True):
+            if hf_hub_id and has_hf_hub(necessary=True) and not cached_url_valid:
-            # hf-hub available as alternate weight source in default_cfg
+                # hf-hub available as alternate weight source in default_cfg
-            load_from = 'hf-hub'
+                load_from = 'hf-hub'
-            pretrained_loc = hf_hub_id
+                pretrained_loc = hf_hub_id
+            elif pretrained_url:
+                load_from = 'url'
+                pretrained_loc = pretrained_url
+
     if load_from == 'hf-hub' and pretrained_cfg.get('hf_hub_filename', None):
         # if a filename override is set, return tuple for location w/ (hub_id, filename)
         pretrained_loc = pretrained_loc, pretrained_cfg['hf_hub_filename']
@@ -105,7 +112,7 @@ def load_custom_pretrained(
         pretrained_loc = download_cached_file(
             pretrained_loc,
             check_hash=_CHECK_HASH,
-            progress=_DOWNLOAD_PROGRESS
+            progress=_DOWNLOAD_PROGRESS,
         )
 
     if load_fn is not None:

timm/models/_hub.py

@@ -1,3 +1,4 @@
+import hashlib
 import json
 import logging
 import os
@@ -67,6 +68,26 @@ def download_cached_file(url, check_hash=True, progress=False):
     return cached_file
 
 
+def check_cached_file(url, check_hash=True):
+    if isinstance(url, (list, tuple)):
+        url, filename = url
+    else:
+        parts = urlparse(url)
+        filename = os.path.basename(parts.path)
+    cached_file = os.path.join(get_cache_dir(), filename)
+    if os.path.exists(cached_file):
+        if check_hash:
+            r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]
+            hash_prefix = r.group(1) if r else None
+            if hash_prefix:
+                with open(cached_file, 'rb') as f:
+                    hd = hashlib.sha256(f.read()).hexdigest()
+                    if hd[:len(hash_prefix)] != hash_prefix:
+                        return False
+        return True
+    return False
+
+
 def has_hf_hub(necessary=False):
     if not _has_hf_hub and necessary:
         # if no HF Hub module installed, and it is necessary to continue, raise error
@@ -145,7 +166,9 @@ def save_for_hf(model, save_directory, model_config=None):
     hf_config['architecture'] = pretrained_cfg.pop('architecture')
     hf_config['num_classes'] = model_config.get('num_classes', model.num_classes)
     hf_config['num_features'] = model_config.get('num_features', model.num_features)
-    hf_config['global_pool'] = model_config.get('global_pool', getattr(model, 'global_pool', None))
+    global_pool_type = model_config.get('global_pool', getattr(model, 'global_pool', None))
+    if isinstance(global_pool_type, str) and global_pool_type:
+        hf_config['global_pool'] = global_pool_type
 
     if 'label' in model_config:
         _logger.warning(

timm/models/_pretrained.py

@@ -19,6 +19,7 @@ class PretrainedCfg:
 
     source: Optional[str] = None  # source of cfg / weight location used (url, file, hf-hub)
     architecture: Optional[str] = None  # architecture variant can be set when not implicit
+    tag: Optional[str] = None  # pretrained tag of source
     custom_load: bool = False  # use custom model specific model.load_pretrained() (ie for npz files)
 
     # input / data config

timm/models/_registry.py

@@ -7,6 +7,7 @@ import re
 import sys
 from collections import defaultdict, deque
 from copy import deepcopy
+from dataclasses import replace
 from typing import List, Optional, Union, Tuple
 
 from ._pretrained import PretrainedCfg, DefaultCfg, split_model_name_tag
@@ -20,7 +21,7 @@ _model_to_module = {}  # mapping of model names to module names
 _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_default_cfgs = dict()  # central repo for model arch -> default cfg objects
-_model_pretrained_cfgs = dict()  # central repo for model arch + tag -> pretrained cfgs
+_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
 
 
@@ -48,24 +49,31 @@ def register_model(fn):
     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]
+        default_cfg = mod.default_cfgs[model_name]
-        if not isinstance(cfg, DefaultCfg):
+        if not isinstance(default_cfg, DefaultCfg):
             # new style default cfg dataclass w/ multiple entries per model-arch
-            assert isinstance(cfg, dict)
+            assert isinstance(default_cfg, dict)
             # old style cfg dict per model-arch
-            cfg = PretrainedCfg(**cfg)
+            pretrained_cfg = PretrainedCfg(**default_cfg)
-            cfg = DefaultCfg(tags=deque(['']), cfgs={'': cfg})
+            default_cfg = DefaultCfg(tags=deque(['']), cfgs={'': pretrained_cfg})
 
-        for tag_idx, tag in enumerate(cfg.tags):
+        for tag_idx, tag in enumerate(default_cfg.tags):
             is_default = tag_idx == 0
-            pretrained_cfg = cfg.cfgs[tag]
+            pretrained_cfg = default_cfg.cfgs[tag]
+            model_name_tag = '.'.join([model_name, tag]) if tag else model_name
+            replace_items = dict(architecture=model_name, tag=tag if tag else None)
+            if pretrained_cfg.hf_hub_id and pretrained_cfg.hf_hub_id == 'timm/':
+                # auto-complete hub name w/ architecture.tag
+                replace_items['hf_hub_id'] = pretrained_cfg.hf_hub_id + model_name_tag
+            pretrained_cfg = replace(pretrained_cfg, **replace_items)
+
             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
@@ -74,7 +82,7 @@ def register_model(fn):
             else:
                 _model_with_tags[model_name].append(model_name)  # has empty tag (to slowly remove these instances)
 
-        _model_default_cfgs[model_name] = cfg
+        _model_default_cfgs[model_name] = default_cfg
 
     return fn
 

timm/models/convnext.py

@@ -361,7 +361,6 @@ def _create_convnext(variant, pretrained=False, **kwargs):
     return model
 
 
-
 def _cfg(url='', **kwargs):
     return {
         'url': url,
@@ -375,90 +374,130 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = generate_default_cfgs({
     # timm specific variants
-    'convnext_atto.timm_in1k': _cfg(
+    'convnext_atto.d2_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_d2-01bb0f51.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
-    'convnext_atto_ols.timm_in1k': _cfg(
+    'convnext_atto_ols.a2_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_ols_a2-78d1c8f3.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
-    'convnext_femto.timm_in1k': _cfg(
+    'convnext_femto.d1_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_d1-d71d5b4c.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
-    'convnext_femto_ols.timm_in1k': _cfg(
+    'convnext_femto_ols.d1_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_ols_d1-246bf2ed.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
-    'convnext_pico.timm_in1k': _cfg(
+    'convnext_pico.d1_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_d1-10ad7f0d.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
-    'convnext_pico_ols.timm_in1k': _cfg(
+    'convnext_pico_ols.d1_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_ols_d1-611f0ca7.pth',
+        hf_hub_id='timm/',
         crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
-    'convnext_nano.timm_in1k': _cfg(
+    'convnext_nano.in12k_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
+    'convnext_nano.d1h_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_d1h-7eb4bdea.pth',
+        hf_hub_id='timm/',
         crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
-    'convnext_nano_ols.timm_in1k': _cfg(
+    'convnext_nano_ols.d1h_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_ols_d1h-ae424a9a.pth',
+        hf_hub_id='timm/',
         crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
-    'convnext_tiny_hnf.timm_in1k': _cfg(
+    'convnext_tiny_hnf.a2h_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
+        hf_hub_id='timm/',
         crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
 
+    'convnext_nano.in12k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, num_classes=11821),
+
     'convnext_tiny.fb_in1k': _cfg(
         url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_small.fb_in1k': _cfg(
         url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_base.fb_in1k': _cfg(
         url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_large.fb_in1k': _cfg(
         url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_xlarge.untrained': _cfg(),
 
     'convnext_tiny.fb_in22k_ft_in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_small.fb_in22k_ft_in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_base.fb_in22k_ft_in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_large.fb_in22k_ft_in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
     'convnext_xlarge.fb_in22k_ft_in1k': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth',
+        hf_hub_id='timm/',
         test_input_size=(3, 288, 288), test_crop_pct=1.0),
 
     'convnext_tiny.fb_in22k_ft_in1k_384': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth',
+        hf_hub_id='timm/',
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
-    'convnext_small..fb_in22k_ft_in1k_384': _cfg(
+    'convnext_small.fb_in22k_ft_in1k_384': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth',
+        hf_hub_id='timm/',
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
     'convnext_base.fb_in22k_ft_in1k_384': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth',
+        hf_hub_id='timm/',
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
     'convnext_large.fb_in22k_ft_in1k_384': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth',
+        hf_hub_id='timm/',
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
     'convnext_xlarge.fb_in22k_ft_in1k_384': _cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth',
+        hf_hub_id='timm/',
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
 
-    'convnext_tiny_in22k.fb_in22k': _cfg(
+    'convnext_tiny.fb_in22k': _cfg(
-        url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", num_classes=21841),
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",
-    'convnext_small_in22k.fb_in22k': _cfg(
+        hf_hub_id='timm/',
-        url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", num_classes=21841),
+        num_classes=21841),
-    'convnext_base_in22k.fb_in22k': _cfg(
+    'convnext_small.fb_in22k': _cfg(
-        url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",
-    'convnext_large_in22k.fb_in22k': _cfg(
+        hf_hub_id='timm/',
-        url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
+        num_classes=21841),
-    'convnext_xlarge_in22k.fb_in22k': _cfg(
+    'convnext_base.fb_in22k': _cfg(
-        url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
+        hf_hub_id='timm/',
+        num_classes=21841),
+    'convnext_large.fb_in22k': _cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
+        hf_hub_id='timm/',
+        num_classes=21841),
+    'convnext_xlarge.fb_in22k': _cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
+        hf_hub_id='timm/',
+        num_classes=21841),
 })
 
 

timm/models/vision_transformer.py

@@ -711,10 +711,10 @@ default_cfgs = generate_default_cfgs({
 
 
     # patch models, imagenet21k (weights from official Google JAX impl)
-    'vit_large_patch32_224.v1_in21k': _cfg(
+    'vit_large_patch32_224.orig_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_huge_patch14_224.v1_in21k': _cfg(
+    'vit_huge_patch14_224.orig_in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
         hf_hub_id='timm/vit_huge_patch14_224_in21k',
         custom_load=True, num_classes=21843),