:books: Add and fix scripts

README.md

@@ -38,6 +38,6 @@ pip install torch-uncertainty
 
 ### Loading models
 
-The functions to load the models are available in `scripts`.
+The functions to load the models are available in `scripts`. The script corresponding to Tiny-ImageNet also contains a snippet to evaluate the accuracy of a downloaded model.
 
 **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/cifar-10-resnet18/std_loading.py

@@ -1,12 +1,13 @@
 from pathlib import Path
 
-from torch_uncertainty.models.resnet import resnet18
+from torch_uncertainty.models.resnet import resnet
 from safetensors.torch import load_file
 
 
 def load_model(version: int):
     """Load the model corresponding to the given version."""
-    model = resnet18(
+    model = resnet(
+        arch=18,
         num_classes=10,
         in_channels=3,
         style="cifar",

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

@@ -1,14 +1,15 @@
 from pathlib import Path
 from torch.nn import functional as F
 
-from torch_uncertainty.models.resnet import resnet20
+from torch_uncertainty.models.resnet import resnet
 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(
+    model = resnet(
+        arch=20,
         num_classes=10,
         in_channels=3,
         style="cifar",

scripts/tiny-imagenet-resnet18/std_loading_testing.py

@@ -0,0 +1,44 @@
+from pathlib import Path
+
+from torch_uncertainty.models.resnet import resnet
+from safetensors.torch import load_file
+
+
+def load_model(version: int):
+    """Load the model corresponding to the given version."""
+    model = resnet(
+        arch=18,
+        num_classes=200,
+        in_channels=3,
+        style="cifar",
+        conv_bias=False,
+    )
+    path = Path(
+        f"tiny-imagenet-resnet18/tiny-imagenet-resnet18-0-1023/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
+
+    from torch_uncertainty.datamodules.classification.tiny_imagenet import TinyImageNetDataModule
+from torchmetrics import Accuracy
+
+# Compute the accuracy using the first checkpoint
+acc = Accuracy("multiclass", num_classes=200)
+data_module = TinyImageNetDataModule(
+    root="data",
+    batch_size=32,
+)
+model = load_model(0)
+
+model.eval()
+data_module.setup("test")
+
+for batch in data_module.test_dataloader()[0]:
+    x, y = batch
+    y_hat = model(x)
+    acc.update(y_hat, y)
+print(f"Accuracy on the test set: {acc.compute():.3%}")