Merge pull request #1649 from leoentersthevoid/0.6.x

fixed import in python 3.11.0

.github/workflows/tests.yml

@@ -2,9 +2,9 @@ name: Python tests
 
 on:
   push:
-    branches: [ master ]
+    branches: [ 0.6.x ]
   pull_request:
-    branches: [ master ]
+    branches: [ 0.6.x ]
 
 env:
   OMP_NUM_THREADS: 2
@@ -15,10 +15,10 @@ jobs:
     name: Run tests on ${{ matrix.os }} with Python ${{ matrix.python }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macOS-latest]
-        python: ['3.9']
-        torch: ['1.10.0']
-        torchvision: ['0.11.1']
+        os: [ubuntu-latest]
+        python: ['3.10']
+        torch: ['1.13.1']
+        torchvision: ['0.14.1']
     runs-on: ${{ matrix.os }}
 
     steps:
@@ -30,7 +30,7 @@ jobs:
     - name: Install testing dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install pytest pytest-timeout pytest-xdist expecttest
+        pip install pytest pytest-timeout pytest-xdist pytest-forked expecttest
     - name: Install torch on mac
       if: startsWith(matrix.os, 'macOS')
       run: pip install --no-cache-dir torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}

timm/models/hub.py

@@ -3,10 +3,12 @@ import logging
 import os
 from functools import partial
 from pathlib import Path
-from typing import Union
+from tempfile import TemporaryDirectory
+from typing import Optional, Union
 
 import torch
 from torch.hub import HASH_REGEX, download_url_to_file, urlparse
+
 try:
     from torch.hub import get_dir
 except ImportError:
@@ -15,7 +17,10 @@ except ImportError:
 from timm import __version__
 
 try:
-    from huggingface_hub import HfApi, HfFolder, Repository, hf_hub_download, hf_hub_url
+    from huggingface_hub import (create_repo, get_hf_file_metadata,
+                                 hf_hub_download, hf_hub_url,
+                                 repo_type_and_id_from_hf_id, upload_folder)
+    from huggingface_hub.utils import EntryNotFoundError
     hf_hub_download = partial(hf_hub_download, library_name="timm", library_version=__version__)
     _has_hf_hub = True
 except ImportError:
@@ -121,53 +126,45 @@ def save_for_hf(model, save_directory, model_config=None):
 
 def push_to_hf_hub(
     model,
-    local_dir,
-    repo_namespace_or_url=None,
-    commit_message='Add model',
-    use_auth_token=True,
-    git_email=None,
-    git_user=None,
-    revision=None,
-    model_config=None,
+    repo_id: str,
+    commit_message: str ='Add model',
+    token: Optional[str] = None,
+    revision: Optional[str] = None,
+    private: bool = False,
+    create_pr: bool = False,
+    model_config: Optional[dict] = None,
 ):
-    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."
-            )
-
-        repo_owner = HfApi().whoami(token)['name']
-        repo_name = Path(local_dir).name
-
-    repo_url = f'https://huggingface.co/{repo_owner}/{repo_name}'
-
-    repo = Repository(
-        local_dir,
-        clone_from=repo_url,
-        use_auth_token=use_auth_token,
-        git_user=git_user,
-        git_email=git_email,
-        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):
+    # Create repo if doesn't exist yet
+    repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
+
+    # Infer complete repo_id from repo_url
+    # Can be different from the input `repo_id` if repo_owner was implicit
+    _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
+    repo_id = f"{repo_owner}/{repo_name}"
+
+    # Check if README file already exist in repo
+    try:
+        get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
+        has_readme = True
+    except EntryNotFoundError:
+        has_readme = False
+
+    # Dump model and push to Hub
+    with TemporaryDirectory() as tmpdir:
         # Save model weights and config.
-        save_for_hf(model, repo.local_dir, model_config=model_config)
+        save_for_hf(model, tmpdir, model_config=model_config)
 
-        # Save a model card if it doesn't exist.
-        readme_path = Path(repo.local_dir) / 'README.md'
-        if not readme_path.exists():
+        # Add readme if does not exist
+        if not has_readme:
+            readme_path = Path(tmpdir) / "README.md"
+            readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_id}'
             readme_path.write_text(readme_text)
 
-    return repo.git_remote_url()
+        # Upload model and return
+        return upload_folder(
+            repo_id=repo_id,
+            folder_path=tmpdir,
+            revision=revision,
+            create_pr=create_pr,
+            commit_message=commit_message,
+        )

timm/models/layers/norm.py

@@ -50,7 +50,7 @@ class LayerNorm(nn.LayerNorm):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         if self._fast_norm:
-           x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+            x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
         else:
             x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
         return x
@@ -65,7 +65,7 @@ class LayerNorm2d(nn.LayerNorm):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = x.permute(0, 2, 3, 1)
         if self._fast_norm:
-           x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+            x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
         else:
             x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
         x = x.permute(0, 3, 1, 2)

timm/models/maxxvit.py

@@ -39,7 +39,7 @@ Hacked together by / Copyright 2022, Ross Wightman
 
 import math
 from collections import OrderedDict
-from dataclasses import dataclass, replace
+from dataclasses import dataclass, replace, field
 from functools import partial
 from typing import Callable, Optional, Union, Tuple, List
 
@@ -229,8 +229,8 @@ class MaxxVitCfg:
     block_type: Tuple[Union[str, Tuple[str, ...]], ...] = ('C', 'C', 'T', 'T')
     stem_width: Union[int, Tuple[int, int]] = 64
     stem_bias: bool = True
-    conv_cfg: MaxxVitConvCfg = MaxxVitConvCfg()
-    transformer_cfg: MaxxVitTransformerCfg = MaxxVitTransformerCfg()
+    conv_cfg: MaxxVitConvCfg = field(default_factory=MaxxVitConvCfg)
+    transformer_cfg: MaxxVitTransformerCfg = field(default_factory=MaxxVitTransformerCfg)
     weight_init: str = 'vit_eff'
 
 
@@ -1910,4 +1910,5 @@ def maxvit_large_224(pretrained=False, **kwargs):
 
 @register_model
 def maxvit_xlarge_224(pretrained=False, **kwargs):
-    return _create_maxxvit('maxvit_xlarge_224', pretrained=pretrained, **kwargs)
+    return _create_maxxvit('maxvit_xlarge_224', pretrained=pretrained, **kwargs)
+