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"""
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
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