From e72c98997360a5ee8f271615ba02f8122b45fde0 Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Wed, 1 Sep 2021 18:14:28 -0600
Subject: [PATCH 01/19] :sparkles: add ability to push to hf hub

---
 timm/models/hub.py | 120 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 118 insertions(+), 2 deletions(-)

diff --git a/timm/models/hub.py b/timm/models/hub.py
index 9a9b5530..9e0ac18a 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -1,6 +1,7 @@
 import json
 import logging
 import os
+from pathlib import Path
 from functools import partial
 from typing import Union, Optional
 
@@ -13,8 +14,7 @@ except ImportError:
 
 from timm import __version__
 try:
-    from huggingface_hub import hf_hub_url
-    from huggingface_hub import cached_download
+    from huggingface_hub import cached_download, hf_hub_url, HfFolder, HfApi, Repository
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
 except ImportError:
     hf_hub_url = None
@@ -94,3 +94,119 @@ def load_state_dict_from_hf(model_id: str):
     cached_file = _download_from_hf(model_id, 'pytorch_model.bin')
     state_dict = torch.load(cached_file, map_location='cpu')
     return state_dict
+
+
+def save_pretrained_for_hf(model, save_directory, **config_kwargs):
+    assert has_hf_hub(True)
+    save_directory = Path(save_directory)
+    save_directory.mkdir(exist_ok=True, parents=True)
+
+    weights_path = save_directory / 'pytorch_model.bin'
+    torch.save(model.state_dict(), weights_path)
+
+    config_path = save_directory / 'config.json'
+    config = model.default_cfg
+    config.update(config_kwargs)
+
+    with config_path.open('w') as f:
+        json.dump(config, f, indent=4)
+
+
+def push_to_hf_hub(
+    model,
+    repo_path_or_name: Optional[str] = None,
+    repo_url: Optional[str] = None,
+    commit_message: Optional[str] = "Add model",
+    organization: Optional[str] = None,
+    private: Optional[bool] = None,
+    api_endpoint: Optional[str] = None,
+    use_auth_token: Optional[Union[bool, str]] = None,
+    git_user: Optional[str] = None,
+    git_email: Optional[str] = None,
+    config: Optional[dict] = None,
+):
+    """
+    Upload model checkpoint and config to the 🤗 Model Hub while synchronizing a local clone of the repo in
+    :obj:`repo_path_or_name`.
+
+    Parameters:
+        repo_path_or_name (:obj:`str`, `optional`):
+            Can either be a repository name for your model or tokenizer in the Hub or a path to a local folder (in
+            which case the repository will have the name of that local folder). If not specified, will default to
+            the name given by :obj:`repo_url` and a local directory with that name will be created.
+        repo_url (:obj:`str`, `optional`):
+            Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
+            repository will be created in your namespace (unless you specify an :obj:`organization`) with
+            :obj:`repo_name`.
+        commit_message (:obj:`str`, `optional`):
+            Message to commit while pushing. Will default to :obj:`"add config"`, :obj:`"add tokenizer"` or
+            :obj:`"add model"` depending on the type of the class.
+        organization (:obj:`str`, `optional`):
+            Organization in which you want to push your model or tokenizer (you must be a member of this
+            organization).
+        private (:obj:`bool`, `optional`):
+            Whether or not the repository created should be private (requires a paying subscription).
+        api_endpoint (:obj:`str`, `optional`):
+            The API endpoint to use when pushing the model to the hub.
+        use_auth_token (:obj:`bool` or :obj:`str`, `optional`):
+            The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
+            generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). Will default to
+            :obj:`True` if :obj:`repo_url` is not specified.
+        git_user (``str``, `optional`):
+            will override the ``git config user.name`` for committing and pushing files to the hub.
+        git_email (``str``, `optional`):
+            will override the ``git config user.email`` for committing and pushing files to the hub.
+        config (:obj:`dict`, `optional`):
+            Configuration object to be saved alongside the model weights.
+
+
+    Returns:
+        The url of the commit of your model in the given repository.
+    """
+    assert has_hf_hub(True)
+    if repo_path_or_name is None and repo_url is None:
+        raise ValueError(
+            "You need to specify a `repo_path_or_name` or a `repo_url`."
+        )
+
+    if use_auth_token is None and repo_url is None:
+        token = HfFolder.get_token()
+        if token is None:
+            raise ValueError(
+                "You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and "
+                "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
+                "token as the `use_auth_token` argument."
+            )
+    elif isinstance(use_auth_token, str):
+        token = use_auth_token
+    else:
+        token = None
+
+    if repo_path_or_name is None:
+        repo_path_or_name = repo_url.split("/")[-1]
+
+    # If no URL is passed and there's no path to a directory containing files, create a repo
+    if repo_url is None and not os.path.exists(repo_path_or_name):
+        repo_name = Path(repo_path_or_name).name
+        repo_url = HfApi(endpoint=api_endpoint).create_repo(
+            token,
+            repo_name,
+            organization=organization,
+            private=private,
+            repo_type=None,
+            exist_ok=True,
+        )
+
+    repo = Repository(
+        repo_path_or_name,
+        clone_from=repo_url,
+        use_auth_token=use_auth_token,
+        git_user=git_user,
+        git_email=git_email,
+    )
+    repo.git_pull(rebase=True)
+
+    save_config = model.default_cfg
+    save_config.update(config or {})
+    with repo.commit(commit_message):
+        save_pretrained_for_hf(model, repo.local_dir, **save_config)

From 28d2841acfad0dbdd720b9335f361bcc80cde1a8 Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Wed, 1 Sep 2021 18:15:08 -0600
Subject: [PATCH 02/19] :lipstick: apply isort

---
 timm/models/hub.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/timm/models/hub.py b/timm/models/hub.py
index 9e0ac18a..f5b041a6 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -1,20 +1,24 @@
 import json
 import logging
 import os
-from pathlib import Path
 from functools import partial
-from typing import Union, Optional
+from pathlib import Path
+from typing import Optional, Union
 
 import torch
-from torch.hub import load_state_dict_from_url, download_url_to_file, urlparse, HASH_REGEX
+from torch.hub import (HASH_REGEX, download_url_to_file,
+                       load_state_dict_from_url, urlparse)
+
 try:
     from torch.hub import get_dir
 except ImportError:
     from torch.hub import _get_torch_home as get_dir
 
 from timm import __version__
+
 try:
-    from huggingface_hub import cached_download, hf_hub_url, HfFolder, HfApi, Repository
+    from huggingface_hub import (HfApi, HfFolder, Repository, cached_download,
+                                 hf_hub_url)
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
 except ImportError:
     hf_hub_url = None

From abf9d51bc3d3b2ca69f569b9f226a3cdf4ec1d10 Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Tue, 7 Sep 2021 18:39:26 -0600
Subject: [PATCH 03/19] :construction: wip

---
 timm/models/hub.py | 118 +++++++++++++--------------------------------
 1 file changed, 33 insertions(+), 85 deletions(-)

diff --git a/timm/models/hub.py b/timm/models/hub.py
index f5b041a6..4cdca7f0 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -3,11 +3,10 @@ import logging
 import os
 from functools import partial
 from pathlib import Path
-from typing import Optional, Union
+from typing import Union
 
 import torch
-from torch.hub import (HASH_REGEX, download_url_to_file,
-                       load_state_dict_from_url, urlparse)
+from torch.hub import HASH_REGEX, download_url_to_file, urlparse, load_state_dict_from_url
 
 try:
     from torch.hub import get_dir
@@ -17,8 +16,7 @@ except ImportError:
 from timm import __version__
 
 try:
-    from huggingface_hub import (HfApi, HfFolder, Repository, cached_download,
-                                 hf_hub_url)
+    from huggingface_hub import HfApi, HfFolder, Repository, cached_download, hf_hub_url
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
 except ImportError:
     hf_hub_url = None
@@ -110,107 +108,57 @@ def save_pretrained_for_hf(model, save_directory, **config_kwargs):
 
     config_path = save_directory / 'config.json'
     config = model.default_cfg
+    config['num_classes'] = config_kwargs.pop('num_classes', model.num_classes)
+    config['num_features'] = config_kwargs.pop('num_features', model.num_features)
+    config['labels'] = config_kwargs.pop('labels', [f"LABEL_{i}" for i in range(config['num_classes'])])
     config.update(config_kwargs)
 
     with config_path.open('w') as f:
-        json.dump(config, f, indent=4)
+        json.dump(config, f, indent=2)
 
 
 def push_to_hf_hub(
     model,
-    repo_path_or_name: Optional[str] = None,
-    repo_url: Optional[str] = None,
-    commit_message: Optional[str] = "Add model",
-    organization: Optional[str] = None,
-    private: Optional[bool] = None,
-    api_endpoint: Optional[str] = None,
-    use_auth_token: Optional[Union[bool, str]] = None,
-    git_user: Optional[str] = None,
-    git_email: Optional[str] = None,
-    config: Optional[dict] = None,
+    local_dir,
+    repo_namespace_or_url=None,
+    commit_message='Add model',
+    use_auth_token=True,
+    git_email=None,
+    git_user=None,
+    revision=None,
+    **config_kwargs
 ):
-    """
-    Upload model checkpoint and config to the 🤗 Model Hub while synchronizing a local clone of the repo in
-    :obj:`repo_path_or_name`.
-
-    Parameters:
-        repo_path_or_name (:obj:`str`, `optional`):
-            Can either be a repository name for your model or tokenizer in the Hub or a path to a local folder (in
-            which case the repository will have the name of that local folder). If not specified, will default to
-            the name given by :obj:`repo_url` and a local directory with that name will be created.
-        repo_url (:obj:`str`, `optional`):
-            Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
-            repository will be created in your namespace (unless you specify an :obj:`organization`) with
-            :obj:`repo_name`.
-        commit_message (:obj:`str`, `optional`):
-            Message to commit while pushing. Will default to :obj:`"add config"`, :obj:`"add tokenizer"` or
-            :obj:`"add model"` depending on the type of the class.
-        organization (:obj:`str`, `optional`):
-            Organization in which you want to push your model or tokenizer (you must be a member of this
-            organization).
-        private (:obj:`bool`, `optional`):
-            Whether or not the repository created should be private (requires a paying subscription).
-        api_endpoint (:obj:`str`, `optional`):
-            The API endpoint to use when pushing the model to the hub.
-        use_auth_token (:obj:`bool` or :obj:`str`, `optional`):
-            The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-            generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). Will default to
-            :obj:`True` if :obj:`repo_url` is not specified.
-        git_user (``str``, `optional`):
-            will override the ``git config user.name`` for committing and pushing files to the hub.
-        git_email (``str``, `optional`):
-            will override the ``git config user.email`` for committing and pushing files to the hub.
-        config (:obj:`dict`, `optional`):
-            Configuration object to be saved alongside the model weights.
-
-
-    Returns:
-        The url of the commit of your model in the given repository.
-    """
-    assert has_hf_hub(True)
-    if repo_path_or_name is None and repo_url is None:
-        raise ValueError(
-            "You need to specify a `repo_path_or_name` or a `repo_url`."
-        )
 
-    if use_auth_token is None and repo_url is None:
-        token = HfFolder.get_token()
+    if repo_namespace_or_url:
+        repo_owner, repo_name = repo_namespace_or_url.rstrip('/').split('/')[-2:]
+    else:
+        if isinstance(use_auth_token, str):
+            token = use_auth_token
+        else:
+            token = HfFolder.get_token()
+
         if token is None:
             raise ValueError(
                 "You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and "
                 "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
                 "token as the `use_auth_token` argument."
             )
-    elif isinstance(use_auth_token, str):
-        token = use_auth_token
-    else:
-        token = None
-
-    if repo_path_or_name is None:
-        repo_path_or_name = repo_url.split("/")[-1]
-
-    # If no URL is passed and there's no path to a directory containing files, create a repo
-    if repo_url is None and not os.path.exists(repo_path_or_name):
-        repo_name = Path(repo_path_or_name).name
-        repo_url = HfApi(endpoint=api_endpoint).create_repo(
-            token,
-            repo_name,
-            organization=organization,
-            private=private,
-            repo_type=None,
-            exist_ok=True,
-        )
+
+        repo_owner = HfApi().whoami(token)['name']
+        repo_name = Path(local_dir).name
+
+    repo_url = f'https://huggingface.co/{repo_owner}/{repo_name}'
 
     repo = Repository(
-        repo_path_or_name,
+        local_dir,
         clone_from=repo_url,
         use_auth_token=use_auth_token,
         git_user=git_user,
         git_email=git_email,
+        revision=revision,
     )
-    repo.git_pull(rebase=True)
 
-    save_config = model.default_cfg
-    save_config.update(config or {})
     with repo.commit(commit_message):
-        save_pretrained_for_hf(model, repo.local_dir, **save_config)
+        save_pretrained_for_hf(model, repo.local_dir, **config_kwargs)
+
+    return repo.git_remote_url()

From 2b6ade24b3479a641f43aec7bded20bfbb19603e Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Mon, 13 Sep 2021 23:31:28 -0400
Subject: [PATCH 04/19] :art: write model card to enable inference

---
 timm/models/hub.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/timm/models/hub.py b/timm/models/hub.py
index 4cdca7f0..184e9b85 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -159,6 +159,13 @@ def push_to_hf_hub(
     )
 
     with repo.commit(commit_message):
+        # Save model weights and config
         save_pretrained_for_hf(model, repo.local_dir, **config_kwargs)
 
+        # Save a model card if it doesn't exist, enabling inference.
+        readme_path = Path(repo.local_dir) / 'README.md'
+        readme_txt = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
+        if not readme_path.exists():
+            readme_path.write_text(readme_txt)
+
     return repo.git_remote_url()

From e65a2cba3d6d7bea6b95c4ed482872f4e2355e33 Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Tue, 14 Sep 2021 01:07:04 -0400
Subject: [PATCH 05/19] :art: cleanup and add a couple comments

---
 timm/models/hub.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/timm/models/hub.py b/timm/models/hub.py
index 184e9b85..31593f88 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -7,14 +7,12 @@ from typing import Union
 
 import torch
 from torch.hub import HASH_REGEX, download_url_to_file, urlparse, load_state_dict_from_url
-
 try:
     from torch.hub import get_dir
 except ImportError:
     from torch.hub import _get_torch_home as get_dir
 
 from timm import __version__
-
 try:
     from huggingface_hub import HfApi, HfFolder, Repository, cached_download, hf_hub_url
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
@@ -158,14 +156,15 @@ def push_to_hf_hub(
         revision=revision,
     )
 
+    # Prepare a default model card that includes the necessary tags to enable inference.
+    readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
     with repo.commit(commit_message):
-        # Save model weights and config
+        # Save model weights and config.
         save_pretrained_for_hf(model, repo.local_dir, **config_kwargs)
 
-        # Save a model card if it doesn't exist, enabling inference.
+        # Save a model card if it doesn't exist.
         readme_path = Path(repo.local_dir) / 'README.md'
-        readme_txt = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
         if not readme_path.exists():
-            readme_path.write_text(readme_txt)
+            readme_path.write_text(readme_text)
 
     return repo.git_remote_url()

From adcb74f87f09cb9db75b62a8690b53ae1552dda5 Mon Sep 17 00:00:00 2001
From: nateraw <nxr9266@g.rit.edu>
Date: Tue, 14 Sep 2021 01:11:40 -0400
Subject: [PATCH 06/19] :art: Import load_state_dict_from_url directly

---
 timm/models/helpers.py | 4 ++--
 timm/models/hub.py     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/timm/models/helpers.py b/timm/models/helpers.py
index 662a7a48..281f2412 100644
--- a/timm/models/helpers.py
+++ b/timm/models/helpers.py
@@ -11,10 +11,10 @@ from typing import Any, Callable, Optional, Tuple
 
 import torch
 import torch.nn as nn
-
+from torch.hub import load_state_dict_from_url
 
 from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
-from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf, load_state_dict_from_url
+from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
 from .layers import Conv2dSame, Linear
 
 
diff --git a/timm/models/hub.py b/timm/models/hub.py
index 31593f88..a436aff6 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -6,7 +6,7 @@ from pathlib import Path
 from typing import Union
 
 import torch
-from torch.hub import HASH_REGEX, download_url_to_file, urlparse, load_state_dict_from_url
+from torch.hub import HASH_REGEX, download_url_to_file, urlparse
 try:
     from torch.hub import get_dir
 except ImportError:

From f0507f6da6c3eb93960e1d712e8a40b81fd8d8fb Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 09:37:36 -0800
Subject: [PATCH 07/19] Fix k_decay default arg != 1.0 in poly scheduler

---
 timm/scheduler/poly_lr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/timm/scheduler/poly_lr.py b/timm/scheduler/poly_lr.py
index 0c1e63b7..9c351be6 100644
--- a/timm/scheduler/poly_lr.py
+++ b/timm/scheduler/poly_lr.py
@@ -37,7 +37,7 @@ class PolyLRScheduler(Scheduler):
                  noise_pct=0.67,
                  noise_std=1.0,
                  noise_seed=42,
-                 k_decay=.5,
+                 k_decay=1.0,
                  initialize=True) -> None:
         super().__init__(
             optimizer, param_group_field="lr",

From 1e51c2d02e77373e4e4248f5d826f519379ebdff Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 09:46:43 -0800
Subject: [PATCH 08/19] More FX test tweaks

---
 tests/test_models.py | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index 68939a14..c3642eb9 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -348,6 +348,7 @@ if 'GITHUB_ACTIONS' in os.environ:
         'vgg*',
         'vit_large*',
         'xcit_large*',
+        'mixer_l*',
     ]
 
 
@@ -368,15 +369,16 @@ def test_model_forward_fx(model_name, batch_size):
     input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
     if max(input_size) > MAX_FWD_FX_SIZE:
         pytest.skip("Fixed input size model > limit.")
-    inputs = torch.randn((batch_size, *input_size))
-    outputs = model(inputs)
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
+    with torch.no_grad():
+        inputs = torch.randn((batch_size, *input_size))
+        outputs = model(inputs)
+        if isinstance(outputs, tuple):
+            outputs = torch.cat(outputs)
 
-    model = _create_fx_model(model)
-    fx_outputs = tuple(model(inputs).values())
-    if isinstance(fx_outputs, tuple):
-        fx_outputs = torch.cat(fx_outputs)
+        model = _create_fx_model(model)
+        fx_outputs = tuple(model(inputs).values())
+        if isinstance(fx_outputs, tuple):
+            fx_outputs = torch.cat(fx_outputs)
 
     assert torch.all(fx_outputs == outputs)
     assert outputs.shape[0] == batch_size
@@ -440,9 +442,10 @@ def test_model_forward_fx_torchscript(model_name, batch_size):
     model.eval()
 
     model = torch.jit.script(_create_fx_model(model))
-    outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
+    with torch.no_grad():
+        outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
+        if isinstance(outputs, tuple):
+            outputs = torch.cat(outputs)
 
     assert outputs.shape[0] == batch_size
     assert not torch.isnan(outputs).any(), 'Output included NaNs'

From ce76a810c2f6f8719c2a3bd1325b24da78928848 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 11:48:40 -0800
Subject: [PATCH 09/19] New FX test strategy, filter based on param count

---
 tests/test_models.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index c3642eb9..9fb826c5 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -399,8 +399,10 @@ def test_model_backward_fx(model_name, batch_size):
         pytest.skip("Fixed input size model > limit.")
 
     model = create_model(model_name, pretrained=False, num_classes=42)
-    num_params = sum([x.numel() for x in model.parameters()])
     model.train()
+    num_params = sum([x.numel() for x in model.parameters()])
+    if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
+        pytest.skip("Skipping FX backward test on model with more than 100M params.")
 
     model = _create_fx_model(model, train=True)
     outputs = tuple(model(torch.randn((batch_size, *input_size))).values())

From 79bf4f163f956252a84b6098fa06a81f0f11deff Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 11:49:05 -0800
Subject: [PATCH 10/19] Add mention of ResNet50 weights w/ RSB recipe
 ingredients and lower train res for better @ 224 results (but worse res
 scaling beyond). Not changing default.

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e398ac1b..e300cc5b 100644
--- a/README.md
+++ b/README.md
@@ -23,10 +23,11 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
-### Nov 19, 2021
+### Nov 22, 2021
 * A number of updated weights anew new model defs
   * `eca_halonext26ts` - 79.5 @ 256
   * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
+  * `resnet50` - 80.68 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
   * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288
   * `sebotnet33ts_256` (new) - 81.2 @ 224
   * `lamhalobotnet50ts_256` - 81.5 @ 256

From 9bb4c80d2a61812f4ca2a0d665f6147978feed39 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 12:08:46 -0800
Subject: [PATCH 11/19] Update README.md, missed recent regnetz results

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e300cc5b..619cffb4 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,7 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 * A number of updated weights anew new model defs
   * `eca_halonext26ts` - 79.5 @ 256
   * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
-  * `resnet50` - 80.68 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
+  * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
   * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288
   * `sebotnet33ts_256` (new) - 81.2 @ 224
   * `lamhalobotnet50ts_256` - 81.5 @ 256
@@ -36,6 +36,8 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
   * `resnet101` - 82.0 @ 224, 82.8 @ 288
   * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288
   * `resnet152` - 82.8 @ 224, 83.5 @ 288
+  * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320
+  * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320
 * `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris)
 * Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare)
   * models updated for tracing compatibility (almost full support with some distlled transformer exceptions)

From 878bee1d5e3bbc607424e2ef3284bbf7fd219c97 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 22 Nov 2021 14:00:27 -0800
Subject: [PATCH 12/19] Add patch8 vit model to FX exclusion filter

---
 tests/test_models.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index 9fb826c5..c2b151c3 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -339,16 +339,17 @@ EXCLUDE_FX_FILTERS = []
 if 'GITHUB_ACTIONS' in os.environ:
     EXCLUDE_FX_FILTERS += [
         'beit_large*',
-        'swin_large*',
+        'mixer_l*',
+        '*nfnet_f2*',
         '*resnext101_32x32d',
         'resnetv2_152x2*',
-        '*nfnet_f2*',
         'resmlp_big*',
         'resnetrs270',
+        'swin_large*',
         'vgg*',
         'vit_large*',
+        'vit_base_patch8*',
         'xcit_large*',
-        'mixer_l*',
     ]
 
 

From b18c9e323b472dbc01e2334d7c1e98e4d0ef4871 Mon Sep 17 00:00:00 2001
From: Nathan Raw <nxr9266@g.rit.edu>
Date: Mon, 22 Nov 2021 23:43:44 -0500
Subject: [PATCH 13/19] Update helpers.py

---
 timm/models/helpers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/timm/models/helpers.py b/timm/models/helpers.py
index 4e339e66..fd128252 100644
--- a/timm/models/helpers.py
+++ b/timm/models/helpers.py
@@ -15,7 +15,7 @@ from torch.hub import load_state_dict_from_url
 
 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, load_state_dict_from_url
+from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
 from .layers import Conv2dSame, Linear
 
 

From 147e1059a832aaf1bfbe7d530152cba13adf0514 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Nov 2021 14:32:32 -0800
Subject: [PATCH 14/19] Remove FX backward test from GitHub actions runs for
 now.

---
 tests/test_models.py | 60 +++++++++++++++++++++++---------------------
 1 file changed, 32 insertions(+), 28 deletions(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index c2b151c3..18162431 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -386,37 +386,41 @@ def test_model_forward_fx(model_name, batch_size):
     assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 
-@pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models(
-    exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
-@pytest.mark.parametrize('batch_size', [2])
-def test_model_backward_fx(model_name, batch_size):
-    """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
-    if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
-
-    input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
-    if max(input_size) > MAX_BWD_FX_SIZE:
-        pytest.skip("Fixed input size model > limit.")
+if 'GITHUB_ACTIONS' not in os.environ:
+    # FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process
 
-    model = create_model(model_name, pretrained=False, num_classes=42)
-    model.train()
-    num_params = sum([x.numel() for x in model.parameters()])
-    if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
-        pytest.skip("Skipping FX backward test on model with more than 100M params.")
+    @pytest.mark.timeout(120)
+    @pytest.mark.parametrize('model_name', list_models(
+        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
+    @pytest.mark.parametrize('batch_size', [2])
+    def test_model_backward_fx(model_name, batch_size):
+        """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
+        if not has_fx_feature_extraction:
+            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+        input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
+        if max(input_size) > MAX_BWD_FX_SIZE:
+            pytest.skip("Fixed input size model > limit.")
+
+        model = create_model(model_name, pretrained=False, num_classes=42)
+        model.train()
+        num_params = sum([x.numel() for x in model.parameters()])
+        if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
+            pytest.skip("Skipping FX backward test on model with more than 100M params.")
+
+        model = _create_fx_model(model, train=True)
+        outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
+        if isinstance(outputs, tuple):
+            outputs = torch.cat(outputs)
+        outputs.mean().backward()
+        for n, x in model.named_parameters():
+            assert x.grad is not None, f'No gradient for {n}'
+        num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
 
-    model = _create_fx_model(model, train=True)
-    outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
-    outputs.mean().backward()
-    for n, x in model.named_parameters():
-        assert x.grad is not None, f'No gradient for {n}'
-    num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
+        assert outputs.shape[-1] == 42
+        assert num_params == num_grad, 'Some parameters are missing gradients'
+        assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
-    assert outputs.shape[-1] == 42
-    assert num_params == num_grad, 'Some parameters are missing gradients'
-    assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
 EXCLUDE_FX_JIT_FILTERS = [

From d633a014e6362ae354f0aa0227c61033233dc260 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Nov 2021 16:54:01 -0800
Subject: [PATCH 15/19] Post merge cleanup. Fix potential security issue
 passing kwargs directly through to serialized web data.

---
 timm/models/helpers.py |  8 ++++----
 timm/models/hub.py     | 27 ++++++++++++++-------------
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/timm/models/helpers.py b/timm/models/helpers.py
index fd128252..16ce64d0 100644
--- a/timm/models/helpers.py
+++ b/timm/models/helpers.py
@@ -184,12 +184,12 @@ def load_pretrained(model, default_cfg=None, num_classes=1000, in_chans=3, filte
     if not pretrained_url and not hf_hub_id:
         _logger.warning("No pretrained weights exist for this model. Using random initialization.")
         return
-    if hf_hub_id and has_hf_hub(necessary=not pretrained_url):
-        _logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})')
-        state_dict = load_state_dict_from_hf(hf_hub_id)
-    else:
+    if pretrained_url:
         _logger.info(f'Loading pretrained weights from url ({pretrained_url})')
         state_dict = load_state_dict_from_url(pretrained_url, progress=progress, map_location='cpu')
+    elif hf_hub_id and has_hf_hub(necessary=True):
+        _logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})')
+        state_dict = load_state_dict_from_hf(hf_hub_id)
     if filter_fn is not None:
         # for backwards compat with filter fn that take one arg, try one first, the two
         try:
diff --git a/timm/models/hub.py b/timm/models/hub.py
index a436aff6..65e7ba9a 100644
--- a/timm/models/hub.py
+++ b/timm/models/hub.py
@@ -16,9 +16,10 @@ from timm import __version__
 try:
     from huggingface_hub import HfApi, HfFolder, Repository, cached_download, hf_hub_url
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
+    _has_hf_hub = True
 except ImportError:
-    hf_hub_url = None
     cached_download = None
+    _has_hf_hub = False
 
 _logger = logging.getLogger(__name__)
 
@@ -53,11 +54,11 @@ def download_cached_file(url, check_hash=True, progress=False):
 
 
 def has_hf_hub(necessary=False):
-    if hf_hub_url is None and necessary:
+    if not _has_hf_hub and necessary:
         # if no HF Hub module installed and it is necessary to continue, raise error
         raise RuntimeError(
             'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
-    return hf_hub_url is not None
+    return _has_hf_hub
 
 
 def hf_split(hf_id):
@@ -96,8 +97,9 @@ def load_state_dict_from_hf(model_id: str):
     return state_dict
 
 
-def save_pretrained_for_hf(model, save_directory, **config_kwargs):
+def save_for_hf(model, save_directory, model_config=None):
     assert has_hf_hub(True)
+    model_config = model_config or {}
     save_directory = Path(save_directory)
     save_directory.mkdir(exist_ok=True, parents=True)
 
@@ -105,14 +107,14 @@ def save_pretrained_for_hf(model, save_directory, **config_kwargs):
     torch.save(model.state_dict(), weights_path)
 
     config_path = save_directory / 'config.json'
-    config = model.default_cfg
-    config['num_classes'] = config_kwargs.pop('num_classes', model.num_classes)
-    config['num_features'] = config_kwargs.pop('num_features', model.num_features)
-    config['labels'] = config_kwargs.pop('labels', [f"LABEL_{i}" for i in range(config['num_classes'])])
-    config.update(config_kwargs)
+    hf_config = model.default_cfg
+    hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes)
+    hf_config['num_features'] = model_config.pop('num_features', model.num_features)
+    hf_config['labels'] = model_config.pop('labels', [f"LABEL_{i}" for i in range(hf_config['num_classes'])])
+    hf_config.update(model_config)
 
     with config_path.open('w') as f:
-        json.dump(config, f, indent=2)
+        json.dump(hf_config, f, indent=2)
 
 
 def push_to_hf_hub(
@@ -124,9 +126,8 @@ def push_to_hf_hub(
     git_email=None,
     git_user=None,
     revision=None,
-    **config_kwargs
+    model_config=None,
 ):
-
     if repo_namespace_or_url:
         repo_owner, repo_name = repo_namespace_or_url.rstrip('/').split('/')[-2:]
     else:
@@ -160,7 +161,7 @@ def push_to_hf_hub(
     readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
     with repo.commit(commit_message):
         # Save model weights and config.
-        save_pretrained_for_hf(model, repo.local_dir, **config_kwargs)
+        save_for_hf(model, repo.local_dir, model_config=model_config)
 
         # Save a model card if it doesn't exist.
         readme_path = Path(repo.local_dir) / 'README.md'

From f83b0b01e3a28f0408431e8e88acda641d22f299 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Nov 2021 22:24:58 -0800
Subject: [PATCH 16/19] Would like to pass GitHub tests again disabling both FX
 feature extract backward and torchscript tests

---
 tests/test_models.py | 56 ++++++++++++++++++++++----------------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index 18162431..4f80612f 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -422,37 +422,37 @@ if 'GITHUB_ACTIONS' not in os.environ:
         assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 
-# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
-EXCLUDE_FX_JIT_FILTERS = [
-    'deit_*_distilled_patch16_224',
-    'levit*',
-    'pit_*_distilled_224',
-] + EXCLUDE_FX_FILTERS
+    # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
+    EXCLUDE_FX_JIT_FILTERS = [
+        'deit_*_distilled_patch16_224',
+        'levit*',
+        'pit_*_distilled_224',
+    ] + EXCLUDE_FX_FILTERS
 
 
-@pytest.mark.timeout(120)
-@pytest.mark.parametrize(
-    'model_name', list_models(
-        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
-@pytest.mark.parametrize('batch_size', [1])
-def test_model_forward_fx_torchscript(model_name, batch_size):
-    """Symbolically trace each model, script it, and run single forward pass"""
-    if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+    @pytest.mark.timeout(120)
+    @pytest.mark.parametrize(
+        'model_name', list_models(
+            exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
+    @pytest.mark.parametrize('batch_size', [1])
+    def test_model_forward_fx_torchscript(model_name, batch_size):
+        """Symbolically trace each model, script it, and run single forward pass"""
+        if not has_fx_feature_extraction:
+            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
 
-    input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
-    if max(input_size) > MAX_JIT_SIZE:
-        pytest.skip("Fixed input size model > limit.")
+        input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
+        if max(input_size) > MAX_JIT_SIZE:
+            pytest.skip("Fixed input size model > limit.")
 
-    with set_scriptable(True):
-        model = create_model(model_name, pretrained=False)
-    model.eval()
+        with set_scriptable(True):
+            model = create_model(model_name, pretrained=False)
+        model.eval()
 
-    model = torch.jit.script(_create_fx_model(model))
-    with torch.no_grad():
-        outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
-        if isinstance(outputs, tuple):
-            outputs = torch.cat(outputs)
+        model = torch.jit.script(_create_fx_model(model))
+        with torch.no_grad():
+            outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
+            if isinstance(outputs, tuple):
+                outputs = torch.cat(outputs)
 
-    assert outputs.shape[0] == batch_size
-    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+        assert outputs.shape[0] == batch_size
+        assert not torch.isnan(outputs).any(), 'Output included NaNs'

From 85c5ff26d741b2de29d990e0637b06014ec8ad15 Mon Sep 17 00:00:00 2001
From: Martins Bruveris <martins.bruveris@onfido.com>
Date: Wed, 24 Nov 2021 15:02:46 +0200
Subject: [PATCH 17/19] Added DINO pretrained ResMLP models.

---
 timm/models/mlp_mixer.py | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/timm/models/mlp_mixer.py b/timm/models/mlp_mixer.py
index f128b9c9..727b655b 100644
--- a/timm/models/mlp_mixer.py
+++ b/timm/models/mlp_mixer.py
@@ -128,6 +128,13 @@ default_cfgs = dict(
         url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
 
+    resmlp_12_224_dino=_cfg(
+        url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    resmlp_24_224_dino=_cfg(
+        url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+
     gmlp_ti16_224=_cfg(),
     gmlp_s16_224=_cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
@@ -589,6 +596,33 @@ def resmlp_big_24_224_in22ft1k(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def resmlp_12_224_dino(pretrained=False, **kwargs):
+    """ ResMLP-12
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+
+    Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_12_224_dino', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def resmlp_24_224_dino(pretrained=False, **kwargs):
+    """ ResMLP-24
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+
+    Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
+        block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_24_224_dino', pretrained=pretrained, **model_args)
+    return model
+
+
 @register_model
 def gmlp_ti16_224(pretrained=False, **kwargs):
     """ gMLP-Tiny

From 480c676ffad49092e1a76a58554b20bf7513e75e Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Wed, 24 Nov 2021 09:24:47 -0800
Subject: [PATCH 18/19] Fix FX breaking assert in evonorm

---
 timm/models/layers/evo_norm.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/timm/models/layers/evo_norm.py b/timm/models/layers/evo_norm.py
index 8c08e49f..6ef0c881 100644
--- a/timm/models/layers/evo_norm.py
+++ b/timm/models/layers/evo_norm.py
@@ -34,18 +34,17 @@ class EvoNormBatch2d(nn.Module):
             nn.init.ones_(self.v)
 
     def forward(self, x):
-        assert x.dim() == 4, 'expected 4D input'
+        _assert(x.dim() == 4, 'expected 4D input')
         x_type = x.dtype
-        running_var = self.running_var.view(1, -1, 1, 1)
-        if self.training:
-            var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
-            n = x.numel() / x.shape[1]
-            running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
-            self.running_var.copy_(running_var.view(self.running_var.shape))
-        else:
-            var = running_var
-
         if self.v is not None:
+            running_var = self.running_var.view(1, -1, 1, 1)
+            if self.training:
+                var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
+                n = x.numel() / x.shape[1]
+                running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
+                self.running_var.copy_(running_var.view(self.running_var.shape))
+            else:
+                var = running_var
             v = self.v.to(dtype=x_type).reshape(1, -1, 1, 1)
             d = x * v + (x.var(dim=(2, 3), unbiased=False, keepdim=True) + self.eps).sqrt().to(dtype=x_type)
             d = d.max((var + self.eps).sqrt().to(dtype=x_type))

From f7d210d759beb00a3d0834a3ce2d93f6e17f3d38 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Wed, 24 Nov 2021 13:21:03 -0800
Subject: [PATCH 19/19] Remove evonorm models from FX tests

---
 tests/test_models.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_models.py b/tests/test_models.py
index 4f80612f..80b1101f 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -350,6 +350,7 @@ if 'GITHUB_ACTIONS' in os.environ:
         'vit_large*',
         'vit_base_patch8*',
         'xcit_large*',
+        '*evob', '*evos', # remove experimental evonorm models, seem to cause issues with dtype manipulation
     ]