| dependencies = ['torch'] | |
| from modeling_sagvit import SAGViTClassifier | |
| import torch | |
| def SAGViT(pretrained=False, **kwargs): | |
| """ | |
| SAG-ViT model endpoint. | |
| Args: | |
| pretrained (bool): If True, loads pretrained weights. | |
| **kwargs: Additional arguments for the model. | |
| Returns: | |
| model (nn.Module): The SAG-ViT model as proposed in the | |
| paper: SAG-ViT: A Scale-Aware, High-Fidelity Patching | |
| Approach with Graph Attention for Vision Transformers. | |
| https://doi.org/10.48550/arXiv.2411.09420 | |
| """ | |
| model = SAGViTClassifier(**kwargs) | |
| if pretrained: | |
| checkpoint = '' | |
| state_dict = torch.hub.load_state_dict_from_url(checkpoint, progress=True) | |
| model.load_state_dict(state_dict) | |
| return model | |