| from transformers import PretrainedConfig | |
| from typing import List | |
| class ChAdaViTConfig(PretrainedConfig): | |
| model_type = "chadavit" | |
| def __init__( | |
| self, | |
| img_size: List[int] = [224], | |
| in_chans: int = 1, | |
| embed_dim: int = 192, | |
| patch_size: int = 16, | |
| num_classes: int = 0, | |
| depth: int = 12, | |
| num_heads: int = 12, | |
| drop_rate: float = 0.0, | |
| drop_path_rate: float = 0.0, | |
| return_all_tokens: bool = True, | |
| max_number_channels: int = 10, | |
| **kwargs, | |
| ): | |
| self.img_size = img_size | |
| self.in_chans = in_chans | |
| self.embed_dim = embed_dim | |
| self.patch_size = patch_size | |
| self.num_classes = num_classes | |
| self.depth = depth | |
| self.num_heads = num_heads | |
| self.drop_rate = drop_rate | |
| self.drop_path_rate = drop_path_rate | |
| self.return_all_tokens = return_all_tokens | |
| self.max_number_channels = max_number_channels | |
| super().__init__(**kwargs) | |