Make safetensor import option for now. Improve avg/clean checkpoints ext handling a bit (more consistent).

avg_checkpoints.py

@@ -17,10 +17,14 @@ import os
 import glob
 import hashlib
 from timm.models import load_state_dict
+try:
     import safetensors.torch
+    _has_safetensors = True
+except ImportError:
+    _has_safetensors = False
 
-DEFAULT_OUTPUT = "./average.pth"
+DEFAULT_OUTPUT = "./averaged.pth"
-DEFAULT_SAFE_OUTPUT = "./average.safetensors"
+DEFAULT_SAFE_OUTPUT = "./averaged.safetensors"
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
 parser.add_argument('--input', default='', type=str, metavar='PATH',
@@ -38,6 +42,7 @@ parser.add_argument('-n', type=int, default=10, metavar='N',
 parser.add_argument('--safetensors', action='store_true',
                     help='Save weights using safetensors instead of the default torch way (pickle).')
 
+
 def checkpoint_metric(checkpoint_path):
     if not checkpoint_path or not os.path.isfile(checkpoint_path):
         return {}
@@ -63,14 +68,20 @@ def main():
     if args.safetensors and args.output == DEFAULT_OUTPUT:
         # Default path changes if using safetensors
         args.output = DEFAULT_SAFE_OUTPUT
-    if args.safetensors and not args.output.endswith(".safetensors"):
+
+    output, output_ext = os.path.splitext(args.output)
+    if not output_ext:
+        output_ext = ('.safetensors' if args.safetensors else '.pth')
+    output = output + output_ext
+
+    if args.safetensors and not output_ext == ".safetensors":
         print(
             "Warning: saving weights as safetensors but output file extension is not "
             f"set to '.safetensors': {args.output}"
         )
 
-    if os.path.exists(args.output):
+    if os.path.exists(output):
-        print("Error: Output filename ({}) already exists.".format(args.output))
+        print("Error: Output filename ({}) already exists.".format(output))
         exit(1)
 
     pattern = args.input
@@ -87,22 +98,27 @@ def main():
                 checkpoint_metrics.append((metric, c))
         checkpoint_metrics = list(sorted(checkpoint_metrics))
         checkpoint_metrics = checkpoint_metrics[-args.n:]
+        if checkpoint_metrics:
             print("Selected checkpoints:")
             [print(m, c) for m, c in checkpoint_metrics]
         avg_checkpoints = [c for m, c in checkpoint_metrics]
     else:
         avg_checkpoints = checkpoints
+        if avg_checkpoints:
             print("Selected checkpoints:")
             [print(c) for c in checkpoints]
 
+    if not avg_checkpoints:
+        print('Error: No checkpoints found to average.')
+        exit(1)
+
     avg_state_dict = {}
     avg_counts = {}
     for c in avg_checkpoints:
         new_state_dict = load_state_dict(c, args.use_ema)
         if not new_state_dict:
-            print("Error: Checkpoint ({}) doesn't exist".format(args.checkpoint))
+            print(f"Error: Checkpoint ({c}) doesn't exist")
             continue
-
         for k, v in new_state_dict.items():
             if k not in avg_state_dict:
                 avg_state_dict[k] = v.clone().to(dtype=torch.float64)
@@ -122,16 +138,14 @@ def main():
         final_state_dict[k] = v.to(dtype=torch.float32)
 
     if args.safetensors:
-        safetensors.torch.save_file(final_state_dict, args.output)
+        assert _has_safetensors, "`pip install safetensors` to use .safetensors"
+        safetensors.torch.save_file(final_state_dict, output)
     else:
-        try:
+        torch.save(final_state_dict, output)
-            torch.save(final_state_dict, args.output, _use_new_zipfile_serialization=False)
-        except:
-            torch.save(final_state_dict, args.output)
 
-    with open(args.output, 'rb') as f:
+    with open(output, 'rb') as f:
         sha_hash = hashlib.sha256(f.read()).hexdigest()
-    print("=> Saved state_dict to '{}, SHA256: {}'".format(args.output, sha_hash))
+    print(f"=> Saved state_dict to '{output}, SHA256: {sha_hash}'")
 
 
 if __name__ == '__main__':

clean_checkpoint.py

@@ -11,9 +11,14 @@ import torch
 import argparse
 import os
 import hashlib
-import safetensors.torch
 import shutil
+import tempfile
 from timm.models import load_state_dict
+try:
+    import safetensors.torch
+    _has_safetensors = True
+except ImportError:
+    _has_safetensors = False
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
 parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
@@ -22,13 +27,13 @@ parser.add_argument('--output', default='', type=str, metavar='PATH',
                     help='output path')
 parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
                     help='use ema version of weights if present')
+parser.add_argument('--no-hash', dest='no_hash', action='store_true',
+                    help='no hash in output filename')
 parser.add_argument('--clean-aux-bn', dest='clean_aux_bn', action='store_true',
                     help='remove auxiliary batch norm layers (from SplitBN training) from checkpoint')
 parser.add_argument('--safetensors', action='store_true',
                     help='Save weights using safetensors instead of the default torch way (pickle).')
 
-_TEMP_NAME = './_checkpoint.pth'
-
 
 def main():
     args = parser.parse_args()
@@ -37,10 +42,24 @@ def main():
         print("Error: Output filename ({}) already exists.".format(args.output))
         exit(1)
 
-    clean_checkpoint(args.checkpoint, args.output, not args.no_use_ema, args.clean_aux_bn, safe_serialization=args.safetensors)
+    clean_checkpoint(
+        args.checkpoint,
+        args.output,
+        not args.no_use_ema,
+        args.no_hash,
+        args.clean_aux_bn,
+        safe_serialization=args.safetensors,
+    )
 
 
-def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False, safe_serialization: bool=False):
+def clean_checkpoint(
+        checkpoint,
+        output,
+        use_ema=True,
+        no_hash=False,
+        clean_aux_bn=False,
+        safe_serialization: bool=False,
+):
     # Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
     if checkpoint and os.path.isfile(checkpoint):
         print("=> Loading checkpoint '{}'".format(checkpoint))
@@ -55,25 +74,36 @@ def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False, sa
             new_state_dict[name] = v
         print("=> Loaded state_dict from '{}'".format(checkpoint))
 
+        ext = ''
+        if output:
+            checkpoint_root, checkpoint_base = os.path.split(output)
+            checkpoint_base, ext = os.path.splitext(checkpoint_base)
+        else:
+            checkpoint_root = ''
+            checkpoint_base = os.path.split(checkpoint)[1]
+            checkpoint_base = os.path.splitext(checkpoint_base)[0]
+
+        temp_filename = '__' + checkpoint_base
         if safe_serialization:
-            safetensors.torch.save_file(new_state_dict, _TEMP_NAME)
+            assert _has_safetensors, "`pip install safetensors` to use .safetensors"
+            safetensors.torch.save_file(new_state_dict, temp_filename)
         else:
-            try:
+            torch.save(new_state_dict, temp_filename)
-                torch.save(new_state_dict, _TEMP_NAME, _use_new_zipfile_serialization=False)
-            except:
-                torch.save(new_state_dict, _TEMP_NAME)
 
-        with open(_TEMP_NAME, 'rb') as f:
+        with open(temp_filename, 'rb') as f:
             sha_hash = hashlib.sha256(f.read()).hexdigest()
 
-        if output:
+        if ext:
-            checkpoint_root, checkpoint_base = os.path.split(output)
+            final_ext = ext
-            checkpoint_base = os.path.splitext(checkpoint_base)[0]
         else:
-            checkpoint_root = ''
+            final_ext = ('.safetensors' if safe_serialization else '.pth')
-            checkpoint_base = os.path.splitext(checkpoint)[0]
+
-        final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + ('.safetensors' if safe_serialization else '.pth')
+        if no_hash:
-        shutil.move(_TEMP_NAME, os.path.join(checkpoint_root, final_filename))
+            final_filename = checkpoint_base + final_ext
+        else:
+            final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + final_ext
+
+        shutil.move(temp_filename, os.path.join(checkpoint_root, final_filename))
         print("=> Saved state_dict to '{}, SHA256: {}'".format(final_filename, sha_hash))
         return final_filename
     else:

timm/models/_helpers.py

@@ -7,7 +7,11 @@ import os
 from collections import OrderedDict
 
 import torch
+try:
     import safetensors.torch
+    _has_safetensors = True
+except ImportError:
+    _has_safetensors = False
 
 import timm.models._builder
 
@@ -29,6 +33,7 @@ def load_state_dict(checkpoint_path, use_ema=True):
     if checkpoint_path and os.path.isfile(checkpoint_path):
         # Check if safetensors or not and load weights accordingly
         if str(checkpoint_path).endswith(".safetensors"):
+            assert _has_safetensors, "`pip install safetensors` to use .safetensors"
             checkpoint = safetensors.torch.load_file(checkpoint_path, device='cpu')
         else:
             checkpoint = torch.load(checkpoint_path, map_location='cpu')

timm/models/_hub.py

@@ -7,15 +7,21 @@ from functools import partial
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Iterable, Optional, Union
+
 import torch
 from torch.hub import HASH_REGEX, download_url_to_file, urlparse
-import safetensors.torch
 
 try:
     from torch.hub import get_dir
 except ImportError:
     from torch.hub import _get_torch_home as get_dir
 
+try:
+    import safetensors.torch
+    _has_safetensors = True
+except ImportError:
+    _has_safetensors = False
+
 if sys.version_info >= (3, 8):
     from typing import Literal
 else:
@@ -45,6 +51,7 @@ __all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'l
 HF_WEIGHTS_NAME = "pytorch_model.bin"  # default pytorch pkl
 HF_SAFE_WEIGHTS_NAME = "model.safetensors"  # safetensors version
 
+
 def get_cache_dir(child_dir=''):
     """
     Returns the location of the directory where models are cached (and creates it if necessary).
@@ -164,21 +171,28 @@ def load_state_dict_from_hf(model_id: str, filename: str = HF_WEIGHTS_NAME):
     hf_model_id, hf_revision = hf_split(model_id)
 
     # Look for .safetensors alternatives and load from it if it exists
+    if _has_safetensors:
         for safe_filename in _get_safe_alternatives(filename):
             try:
                 cached_safe_file = hf_hub_download(repo_id=hf_model_id, filename=safe_filename, revision=hf_revision)
-            _logger.info(f"[{model_id}] Safe alternative available for '{filename}' (as '{safe_filename}'). Loading weights using safetensors.")
+                _logger.info(
+                    f"[{model_id}] Safe alternative available for '{filename}' "
+                    f"(as '{safe_filename}'). Loading weights using safetensors.")
                 return safetensors.torch.load_file(cached_safe_file, device="cpu")
             except EntryNotFoundError:
                 pass
 
     # Otherwise, load using pytorch.load
     cached_file = hf_hub_download(hf_model_id, filename=filename, revision=hf_revision)
-    _logger.info(f"[{model_id}] Safe alternative not found for '{filename}'. Loading weights using default pytorch.")
+    _logger.debug(f"[{model_id}] Safe alternative not found for '{filename}'. Loading weights using default pytorch.")
     return torch.load(cached_file, map_location='cpu')
 
 
-def save_config_for_hf(model, config_path: str, model_config: Optional[dict] = None):
+def save_config_for_hf(
+        model,
+        config_path: str,
+        model_config: Optional[dict] = None
+):
     model_config = model_config or {}
     hf_config = {}
     pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
@@ -220,7 +234,7 @@ def save_for_hf(
         model,
         save_directory: str,
         model_config: Optional[dict] = None,
-        safe_serialization: Union[bool, Literal["both"]] = False
+        safe_serialization: Union[bool, Literal["both"]] = False,
 ):
     assert has_hf_hub(True)
     save_directory = Path(save_directory)
@@ -229,6 +243,7 @@ def save_for_hf(
     # Save model weights, either safely (using safetensors), or using legacy pytorch approach or both.
     tensors = model.state_dict()
     if safe_serialization is True or safe_serialization == "both":
+        assert _has_safetensors, "`pip install safetensors` to use .safetensors"
         safetensors.torch.save_file(tensors, save_directory / HF_SAFE_WEIGHTS_NAME)
     if safe_serialization is False or safe_serialization == "both":
         torch.save(tensors, save_directory / HF_WEIGHTS_NAME)
@@ -247,7 +262,7 @@ def push_to_hf_hub(
         create_pr: bool = False,
         model_config: Optional[dict] = None,
         model_card: Optional[dict] = None,
-    safe_serialization: Union[bool, Literal["both"]] = False
+        safe_serialization: Union[bool, Literal["both"]] = False,
 ):
     """
     Arguments:
@@ -341,6 +356,7 @@ def generate_readme(model_card: dict, model_name: str):
             readme_text += f"```bibtex\n{c}\n```\n"
     return readme_text
 
+
 def _get_safe_alternatives(filename: str) -> Iterable[str]:
     """Returns potential safetensors alternatives for a given filename.
 
@@ -350,5 +366,5 @@ def _get_safe_alternatives(filename: str) -> Iterable[str]:
     """
     if filename == HF_WEIGHTS_NAME:
         yield HF_SAFE_WEIGHTS_NAME
-    if filename.endswith(".bin"):
+    if filename != HF_WEIGHTS_NAME and filename.endswith(".bin"):
-        yield filename[:-4] + ".safetensors"
+        return filename[:-4] + ".safetensors"