Source code for direct.nn.conjgradnet.conjgradnet_engine

# Copyright 2025 AI for Oncology Research Group. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Dict, Optional, Tuple

import torch
from torch import nn

from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine




class ConjGradNetEngine(MRIModelEngine):
    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:`ConjGradNetEngine`.

        Parameters
        ----------
        cfg: BaseConfig
            Configuration file.
        model: nn.Module
            Model.
        device: str
            Device. Can be "cuda:{idx}" or "cpu".
        forward_operator: Callable, optional
            The forward operator. Default: None.
        backward_operator: Callable, 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,
        )



    def forward_function(self, data: Dict[str, Any]) -> Tuple[torch.Tensor, None]:
        output_image = self.model(
            masked_kspace=data["masked_kspace"],
            sampling_mask=data["sampling_mask"],
            sensitivity_map=data["sensitivity_map"],
        )  # shape (batch, height,  width)
        output_kspace = None
        return output_image, output_kspace