(
self,
pretrained_model_name_or_path: Optional[str] = None,
ori_model: bool = True,
)
| 43 | |
| 44 | class MoGe(nn.Module): |
| 45 | def __init__( |
| 46 | self, |
| 47 | pretrained_model_name_or_path: Optional[str] = None, |
| 48 | ori_model: bool = True, |
| 49 | ): |
| 50 | super().__init__() |
| 51 | |
| 52 | if ori_model and pretrained_model_name_or_path is not None: |
| 53 | from models.moge.model.v1 import MoGeModel |
| 54 | self.model = MoGeModel.from_pretrained(pretrained_model_name_or_path) |
| 55 | else: |
| 56 | raise NotImplementedError |
| 57 | |
| 58 | def forward(self, image: torch.Tensor, num_tokens: int) -> Dict[str, torch.Tensor]: |
| 59 | return self.model(image, num_tokens) |
nothing calls this directly
no test coverage detected