| from src.models.scGPT.model import TransformerModel |
| from src.models.perturbation.model import Model as FlowModel |
| from src.models.perturbation.model import TimedTransformer |
| from src.models.origin.model import model as OriginModel |
| import torch |
|
|
| def instantiate_model(model_type: str, **kwargs): |
|
|
| if model_type == 'origin': |
| if kwargs['fusion_method'] == 'differential_transformer': |
| layers = 8 |
| elif kwargs['fusion_method'] == 'differential_perceiver': |
| layers = 4 |
| else: |
| layers = 8 |
| d_model = kwargs.get('d_model', 512) |
| ntoken = kwargs.get('ntoken', 6000) |
| d_hid = int(4.0 * d_model) |
| return OriginModel( |
| ntoken=ntoken, |
| d_model=d_model, |
| d_hid=d_hid, |
| nlayers=layers, |
| fusion_method=kwargs['fusion_method'], |
| perturbation_function=kwargs['perturbation_function'], |
| mask_path=kwargs['mask_path'], |
| ) |
| else: |
| raise ValueError(f"Invalid model type: {model_type}") |
| |
| if __name__ == "__main__": |
| model = instantiate_model("punet128") |
| x = torch.randn(32, 128, 128) |
| t = torch.randn(32) |
| out = model( x,t) |
| print(out.shape) |
