models/lightning/varnet_module.py

"""
Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""

from argparse import ArgumentParser

import torch

import fastmri
from fastmri import transforms
from ..varnet import VarNet
import wandb

from .mri_module import MriModule


class VarNetModule(MriModule):
    """
    VarNet training module.

    This can be used to train variational networks from the paper:

    A. Sriram et al. End-to-end variational networks for accelerated MRI
    reconstruction. In International Conference on Medical Image Computing and
    Computer-Assisted Intervention, 2020.

    which was inspired by the earlier paper:

    K. Hammernik et al. Learning a variational network for reconstruction of
    accelerated MRI data. Magnetic Resonance inMedicine, 79(6):3055–3071, 2018.
    """

    def __init__(
        self,
        num_cascades: int = 12,
        pools: int = 4,
        chans: int = 18,
        sens_pools: int = 4,
        sens_chans: int = 8,
        lr: float = 0.0003,
        lr_step_size: int = 40,
        lr_gamma: float = 0.1,
        weight_decay: float = 0.0,
        **kwargs,
    ):
        """
        Parameters
        ----------
        num_cascades : int
            Number of cascades (i.e., layers) for the variational network.
        pools : int
            Number of downsampling and upsampling layers for the cascade U-Net.
        chans : int
            Number of channels for the cascade U-Net.
        sens_pools : int
            Number of downsampling and upsampling layers for the sensitivity map U-Net.
        sens_chans : int
            Number of channels for the sensitivity map U-Net.
        lr : float
            Learning rate.
        lr_step_size : int
            Learning rate step size.
        lr_gamma : float
            Learning rate gamma decay.
        weight_decay : float
            Parameter for penalizing weights norm.
        num_sense_lines : int, optional
            Number of low-frequency lines to use for sensitivity map computation.
            Must be even or `None`. Default `None` will automatically compute the number
            from masks. Default behavior may cause some slices to use more low-frequency
            lines than others, when used in conjunction with e.g. the EquispacedMaskFunc
            defaults. To prevent this, either set `num_sense_lines`, or set
            `skip_low_freqs` and `skip_around_low_freqs` to `True` in the EquispacedMaskFunc.
            Note that setting this value may lead to undesired behavior when training on
            multiple accelerations simultaneously.
        """
        super().__init__(**kwargs)
        self.save_hyperparameters()

        self.num_cascades = num_cascades
        self.pools = pools
        self.chans = chans
        self.sens_pools = sens_pools
        self.sens_chans = sens_chans
        self.lr = lr
        self.lr_step_size = lr_step_size
        self.lr_gamma = lr_gamma
        self.weight_decay = weight_decay

        self.varnet = VarNet(
            num_cascades=self.num_cascades,
            sens_chans=self.sens_chans,
            sens_pools=self.sens_pools,
            chans=self.chans,
            pools=self.pools,
        )

        self.criterion = fastmri.SSIMLoss()
        self.num_params = sum(p.numel() for p in self.parameters())

    def forward(self, masked_kspace, mask, num_low_frequencies):
        return self.varnet(masked_kspace, mask, num_low_frequencies)

    def training_step(self, batch, batch_idx):
        output = self.forward(
            batch.masked_kspace, batch.mask, batch.num_low_frequencies
        )

        target, output = transforms.center_crop_to_smallest(batch.target, output)
        loss = self.criterion(
            output.unsqueeze(1), target.unsqueeze(1), data_range=batch.max_value
        )

        self.log("train_loss", loss, on_step=True, on_epoch=True)
        self.log("epoch", int(self.current_epoch), on_step=True, on_epoch=True)

        return loss

    def validation_step(self, batch, batch_idx, dataloader_idx=0):
        dataloaders = self.trainer.val_dataloaders
        slug = list(dataloaders.keys())[dataloader_idx]

        # breakpoint()
        output = self.forward(
            batch.masked_kspace, batch.mask, batch.num_low_frequencies
        )

        target, output = transforms.center_crop_to_smallest(batch.target, output)

        loss = self.criterion(
            output.unsqueeze(1),
            target.unsqueeze(1),
            data_range=batch.max_value,
        )

        return {
            "slug": slug,
            "fname": batch.fname,
            "slice_num": batch.slice_num,
            "max_value": batch.max_value,
            "output": output,
            "target": target,
            "val_loss": loss,
        }

    def configure_optimizers(self):
        optim = torch.optim.Adam(
            self.parameters(), lr=self.lr, weight_decay=self.weight_decay
        )
        scheduler = torch.optim.lr_scheduler.StepLR(
            optim, self.lr_step_size, self.lr_gamma
        )

        return [optim], [scheduler]

    @staticmethod
    def add_model_specific_args(parent_parser):  # pragma: no-cover
        """
        Define parameters that only apply to this model
        """
        parser = ArgumentParser(parents=[parent_parser], add_help=False)
        parser = MriModule.add_model_specific_args(parser)

        # network params
        parser.add_argument(
            "--num_cascades",
            default=12,
            type=int,
            help="Number of VarNet cascades",
        )
        parser.add_argument(
            "--pools",
            default=4,
            type=int,
            help="Number of U-Net pooling layers in VarNet blocks",
        )
        parser.add_argument(
            "--chans",
            default=18,
            type=int,
            help="Number of channels for U-Net in VarNet blocks",
        )
        parser.add_argument(
            "--sens_pools",
            default=4,
            type=int,
            help=(
                "Number of pooling layers for sense map estimation U-Net in" " VarNet"
            ),
        )
        parser.add_argument(
            "--sens_chans",
            default=8,
            type=float,
            help="Number of channels for sense map estimation U-Net in VarNet",
        )

        # training params (opt)
        parser.add_argument(
            "--lr", default=0.0003, type=float, help="Adam learning rate"
        )
        parser.add_argument(
            "--lr_step_size",
            default=40,
            type=int,
            help="Epoch at which to decrease step size",
        )
        parser.add_argument(
            "--lr_gamma",
            default=0.1,
            type=float,
            help="Extent to which step size should be decreased",
        )
        parser.add_argument(
            "--weight_decay",
            default=0.0,
            type=float,
            help="Strength of weight decay regularization",
        )

        return parser