Add 3d unet engine

direct/nn/unet/unet_engine.py

@@ -299,3 +299,92 @@ def forward_function(self, data: dict[str, Any]) -> tuple[torch.Tensor, None]:
         output_kspace = None
 
         return output_image, output_kspace
+
+
+class Unet3dEngine(MRIModelEngine):
+    """Unet3d Model Engine.
+
+    Parameters
+    ----------
+    cfg: BaseConfig
+        Configuration file.
+    model: nn.Module
+        Model.
+    device: str
+        Device. Can be "cuda:{idx}" or "cpu".
+    forward_operator: Callable[[tuple[Any, ...]], torch.Tensor], optional
+        The forward operator. Default: None.
+    backward_operator: Callable[[tuple[Any, ...]], torch.Tensor], optional
+        The backward operator. Default: None.
+    mixed_precision: bool
+        Use mixed precision. Default: False.
+    **models: nn.Module
+        Additional models.
+    """
+
+    def __init__(
+        self,
+        cfg: BaseConfig,
+        model: nn.Module,
+        device: str,
+        forward_operator: Optional[Callable] = None,
+        backward_operator: Optional[Callable] = None,
+        mixed_precision: bool = False,
+        **models: nn.Module,
+    ):
+        """Inits :class:`Unet3dEngine`.
+
+        Parameters
+        ----------
+        cfg: BaseConfig
+            Configuration file.
+        model: nn.Module
+            Model.
+        device: str
+            Device. Can be "cuda:{idx}" or "cpu".
+        forward_operator: Callable[[tuple[Any, ...]], torch.Tensor], optional
+            The forward operator. Default: None.
+        backward_operator: Callable[[tuple[Any, ...]], torch.Tensor], optional
+            The backward operator. Default: None.
+        mixed_precision: bool
+            Use mixed precision. Default: False.
+        **models: nn.Module
+            Additional models.
+        """
+        super().__init__(
+            cfg,
+            model,
+            device,
+            forward_operator=forward_operator,
+            backward_operator=backward_operator,
+            mixed_precision=mixed_precision,
+            **models,
+        )
+
+        self._spatial_dims = (3, 4)
+
+    def forward_function(self, data: dict[str, Any]) -> tuple[torch.Tensor, None]:
+        """Forward function for :class:`Unet3dEngine`.
+
+        Parameters
+        ----------
+        data : dict[str, Any]
+            Input data dictionary containing the following keys: "masked_kspace" and "sensitivity_map"
+            if image initialization is "sense".
+
+        Returns
+        -------
+        tuple[torch.Tensor, None]
+            Prediction of image and None for k-space.
+        """
+
+        sensitity_map = (
+            data["sensitivity_map"] if self.cfg.model.image_initialization == "sense" else None  # type: ignore
+        )
+
+        output_image = self.model(masked_kspace=data["masked_kspace"], sensitivity_map=sensitity_map)
+        output_image = T.modulus(output_image)
+
+        output_kspace = None
+
+        return output_image, output_kspace