Add loading fct & Improve ReadMe

README.md

@@ -7,4 +7,20 @@ tags:
 pretty_name: Checkpoints
 ---
 
-The Checkpoints dataset as trained and used in [A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors](https://arxiv.org/abs/2310.08287) published at ICLR 2024.
+The Checkpoints dataset as trained and used in [A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors](https://arxiv.org/abs/2310.08287) published at ICLR 2024.
+
+## Usage
+
+To load or train models, start by downloading [TorchUncertainty](https://github.com/ENSTA-U2IS-AI/torch-uncertainty) - [Documentation](https://torch-uncertainty.github.io/).
+
+Install TorchUncertainty with pip:
+```bash
+
+pip install torch-uncertainty
+```
+
+Functions to load the models are available in `scripts`.
+
+**Any questions?** Please feel free to ask in the [GitHub Issues](https://github.com/ENSTA-U2IS-AI/torch-uncertainty/issues) or on our [Discord server](https://discord.gg/HMCawt5MJu)
+
+. 

scripts/resnet20-frn-silu-cifar10/std_loading.py

@@ -0,0 +1,26 @@
+from pathlib import Path
+from torch.nn import functional as F
+
+from torch_uncertainty.models.resnet import resnet20
+from torch_uncertainty.layers.filter_response_norm import FilterResponseNorm2d
+from safetensors.torch import load_file
+
+
+def load_model(version: int):
+    """Load the model corresponding to the given version."""
+    model = resnet20(
+        num_classes=10,
+        in_channels=3,
+        style="cifar",
+        activation_fn=F.silu,
+        normalization_layer=FilterResponseNorm2d,
+    )
+    path = Path(
+        f"resnet20-frn-silu-cifar10/resnet20-frn-silu-cifar10-0-1023/version_{version}.safetensors"
+    )
+    if not path.exists():
+        raise ValueError("File does not exist")
+
+    state_dict = load_file(path)
+    model.load_state_dict(state_dict=state_dict)
+    return model