| 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%}") |