Move encoders weights to HF-Hub

pyproject.toml

@@ -21,6 +21,7 @@ dependencies = [
     'numpy>=1.19.3',
     'pillow>=8',
     'pretrainedmodels>=0.7.1',
+    'safetensors>=0.3.1',
     'six>=1.5',
     'timm>=0.9',
     'torch>=1.8',

requirements/minimum.old

@@ -2,6 +2,7 @@ huggingface-hub==0.24.0
 numpy==1.19.3
 pillow==8.0.0
 pretrainedmodels==0.7.1
+safetensors==0.3.1
 six==1.5.0
 timm==0.9.0
 torch==1.9.0

requirements/required.txt

@@ -2,6 +2,7 @@ huggingface_hub==0.27.1
 numpy==2.2.1
 pillow==11.1.0
 pretrainedmodels==0.7.4
+safetensors==0.5.2
 six==1.17.0
 timm==1.0.13
 torch==2.5.1

segmentation_models_pytorch/encoders/__init__.py

@@ -1,8 +1,12 @@
+import json
 import timm
 import copy
 import warnings
 import functools
-import torch.utils.model_zoo as model_zoo
+from torch.utils.model_zoo import load_url
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
 
 from .resnet import resnet_encoders
 from .dpn import dpn_encoders
@@ -22,6 +26,7 @@
 from .timm_universal import TimmUniversalEncoder
 
 from ._preprocessing import preprocess_input
+from ._legacy_pretrained_settings import pretrained_settings
 
 __all__ = [
     "encoders",
@@ -101,15 +106,43 @@ def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **
     encoder = EncoderClass(**params)
 
     if weights is not None:
-        try:
-            settings = encoders[name]["pretrained_settings"][weights]
-        except KeyError:
+        if weights not in encoders[name]["pretrained_settings"]:
+            available_weights = list(encoders[name]["pretrained_settings"].keys())
             raise KeyError(
-                "Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format(
-                    weights, name, list(encoders[name]["pretrained_settings"].keys())
-                )
+                f"Wrong pretrained weights `{weights}` for encoder `{name}`. "
+                f"Available options are: {available_weights}"
+            )
+
+        settings = encoders[name]["pretrained_settings"][weights]
+        repo_id = settings["repo_id"]
+        revision = settings["revision"]
+
+        # First, try to load from  HF-Hub, but as far as I know not all countries have
+        # access to the Hub (e.g. China), so we try to load from the original url if
+        # the first attempt fails.
+        weights_path = None
+        try:
+            hf_hub_download(repo_id, filename="config.json", revision=revision)
+            weights_path = hf_hub_download(
+                repo_id, filename="model.safetensors", revision=revision
             )
-        encoder.load_state_dict(model_zoo.load_url(settings["url"]))
+        except Exception as e:
+            if name in pretrained_settings and weights in pretrained_settings[name]:
+                message = (
+                    f"Error loading {name} `{weights}` weights from Hugging Face Hub, "
+                    "trying loading from original url..."
+                )
+                warnings.warn(message, UserWarning)
+                url = pretrained_settings[name][weights]["url"]
+                state_dict = load_url(url, map_location="cpu")
+            else:
+                raise e
+
+        if weights_path is not None:
+            state_dict = load_file(weights_path, device="cpu")
+
+        # Load model weights
+        encoder.load_state_dict(state_dict)
 
     encoder.set_in_channels(in_channels, pretrained=weights is not None)
     if output_stride != 32:
@@ -136,7 +169,25 @@ def get_preprocessing_params(encoder_name, pretrained="imagenet"):
             raise ValueError(
                 "Available pretrained options {}".format(all_settings.keys())
             )
-        settings = all_settings[pretrained]
+
+        repo_id = all_settings[pretrained]["repo_id"]
+        revision = all_settings[pretrained]["revision"]
+
+        # Load config and model
+        try:
+            config_path = hf_hub_download(
+                repo_id, filename="config.json", revision=revision
+            )
+            with open(config_path, "r") as f:
+                settings = json.load(f)
+        except Exception as e:
+            if (
+                encoder_name in pretrained_settings
+                and pretrained in pretrained_settings[encoder_name]
+            ):
+                settings = pretrained_settings[encoder_name][pretrained]
+            else:
+                raise e
 
     formatted_settings = {}
     formatted_settings["input_space"] = settings.get("input_space", "RGB")

segmentation_models_pytorch/encoders/_efficientnet.py

@@ -13,7 +13,6 @@
 import math
 import collections
 from functools import partial
-from torch.utils import model_zoo
 
 
 class MBConvBlock(nn.Module):