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main_unet_bn.py
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import os
import warnings
warnings.filterwarnings("ignore")
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (8192, rlimit[1]))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
torch.cuda.empty_cache()
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
torch.multiprocessing.set_sharing_strategy('file_system')
from pytorch3d.renderer import (
VolumeRenderer,
NDCMultinomialRaysampler,
)
from pytorch3d.renderer.implicit import (
HarmonicEmbedding
)
from pytorch3d.renderer.cameras import (
CamerasBase,
FoVPerspectiveCameras,
look_at_view_transform
)
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning import Trainer, LightningModule
from argparse import ArgumentParser
from typing import Optional, Sequence
from monai.networks.nets import Discriminator
from monai.networks.nets.flexible_unet import encoder_feature_channel
from monai.networks.layers.factories import Act, Norm, split_args
from datamodule import UnpairedDataModule
from unet.inverse_renderer import UnetFrontToBackInverseRenderer
from dvr.renderer import DirectVolumeFrontToBackRenderer
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
nn.init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
nn.init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
nn.init.normal_(m.weight.data, 1.0, init_gain)
nn.init.constant_(m.bias.data, 0.0)
# print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function <init_func>
class UnetLightningModule(LightningModule):
def __init__(self, hparams, **kwargs):
super().__init__()
self.logsdir = hparams.logsdir
self.lr = hparams.lr
self.shape = hparams.shape
self.alpha = hparams.alpha
self.gamma = hparams.gamma
self.weight_decay = hparams.weight_decay
self.batch_size = hparams.batch_size
self.devices = hparams.devices
self.n_pts_per_ray = hparams.n_pts_per_ray
self.save_hyperparameters()
self.fwd_renderer = DirectVolumeFrontToBackRenderer(
image_width=self.shape,
image_height=self.shape,
n_pts_per_ray=512,
min_depth=2.0,
max_depth=6.0
)
self.inv_renderer = UnetFrontToBackInverseRenderer(
shape=self.shape,
in_channels=3,
out_channels=1,
dropout=0.4,
)
# init_weights(self.inv_renderer, init_type='xavier', init_gain=0.02)
self.loss_smoothl1 = nn.SmoothL1Loss(reduction="mean", beta=0.02)
def forward(self, figures, elev, azim):
return self.inv_renderer(torch.cat([figures,
elev.view(-1, 1, 1, 1).repeat(1, 1, self.shape, self.shape),
azim.view(-1, 1, 1, 1).repeat(1, 1, self.shape, self.shape)], dim=1))
def _common_step(self, batch, batch_idx, optimizer_idx, stage: Optional[str] = 'evaluation'):
_device = batch["image3d"].device
image3d = batch["image3d"]
image2d = batch["image2d"]
# Construct the locked camera
dist_locked = 4.0 * torch.ones(self.batch_size, device=_device)
elev_locked = torch.zeros(self.batch_size, device=_device)
azim_locked = torch.zeros(self.batch_size, device=_device)
R_locked, T_locked = look_at_view_transform(
dist=dist_locked,
elev=elev_locked, # * 0,
azim=azim_locked, # * 0
)
camera_locked = FoVPerspectiveCameras(R=R_locked, T=T_locked, fov=45, aspect_ratio=1).to(_device)
# Construct the random camera
dist_random = 4.0 * torch.ones(self.batch_size, device=_device)
elev_random = torch.clamp(
torch.randn(self.batch_size, device=_device),
min=-0.5, max=0.5) # -0.5 0.5 -> -45 45 ; -1 1 -> -90 90
azim_random = torch.rand(self.batch_size, device=_device) # 0 1 -> 0 360
R_random, T_random = look_at_view_transform(
dist=dist_random,
elev=elev_random * 90,
azim=azim_random * 360
)
camera_random = FoVPerspectiveCameras(R=R_random, T=T_random, fov=45, aspect_ratio=1).to(_device)
# CT pathway
src_volume_ct_locked = image3d
est_figure_ct_locked = self.fwd_renderer.forward(image3d=src_volume_ct_locked, opacity=None, cameras=camera_locked)
est_figure_ct_random = self.fwd_renderer.forward(image3d=src_volume_ct_locked, opacity=None, cameras=camera_random)
# XR pathway
src_figure_xr_hidden = image2d
est_volume_ct_random, est_volume_xr_locked = \
torch.split(
self.forward(
torch.cat([est_figure_ct_random, src_figure_xr_hidden]),
torch.cat([elev_random, elev_locked]),
torch.cat([azim_random, azim_locked]),
),
self.batch_size
)
est_figure_xr_locked_locked = self.fwd_renderer.forward(image3d=est_volume_xr_locked, opacity=None, cameras=camera_locked)
est_figure_xr_locked_random = self.fwd_renderer.forward(image3d=est_volume_xr_locked, opacity=None, cameras=camera_random)
est_volume_ct_locked, rec_volume_xr_random = \
torch.split(
self.forward(
torch.cat([est_figure_ct_locked, est_figure_xr_locked_random]),
torch.cat([elev_locked, elev_random]),
torch.cat([azim_locked, azim_random]),
),
self.batch_size
)
rec_figure_ct_locked_locked = self.fwd_renderer.forward(image3d=est_volume_ct_locked, opacity=None, cameras=camera_locked)
rec_figure_ct_locked_random = self.fwd_renderer.forward(image3d=est_volume_ct_locked, opacity=None, cameras=camera_random)
rec_figure_ct_random_locked = self.fwd_renderer.forward(image3d=est_volume_ct_random, opacity=None, cameras=camera_locked)
rec_figure_ct_random_random = self.fwd_renderer.forward(image3d=est_volume_ct_random, opacity=None, cameras=camera_random)
rec_figure_xr_random_locked = self.fwd_renderer.forward(image3d=rec_volume_xr_random, opacity=None, cameras=camera_locked)
# rec_figure_xr_random_random = self.fwd_renderer.forward(image3d=rec_volume_xr_random, opacity=None, cameras=camera_random)
# Compute the loss
im3d_loss = self.loss_smoothl1(src_volume_ct_locked, est_volume_ct_locked) \
+ self.loss_smoothl1(src_volume_ct_locked, est_volume_ct_random)
im2d_loss = self.loss_smoothl1(est_figure_ct_locked, rec_figure_ct_locked_locked) \
+ self.loss_smoothl1(est_figure_ct_random, rec_figure_ct_locked_random) \
+ self.loss_smoothl1(est_figure_ct_locked, rec_figure_ct_random_locked) \
+ self.loss_smoothl1(est_figure_ct_random, rec_figure_ct_random_random) \
+ self.loss_smoothl1(src_figure_xr_hidden, est_figure_xr_locked_locked) \
+ self.loss_smoothl1(src_figure_xr_hidden, rec_figure_xr_random_locked)
self.log(f'{stage}_im2d_loss', im2d_loss, on_step=(stage == 'train'), prog_bar=True, logger=True, sync_dist=True, batch_size=self.batch_size)
self.log(f'{stage}_im3d_loss', im3d_loss, on_step=(stage == 'train'), prog_bar=True, logger=True, sync_dist=True, batch_size=self.batch_size)
loss = self.alpha*im3d_loss + self.gamma*im2d_loss
if batch_idx == 0:
viz2d = torch.cat([
torch.cat([src_volume_ct_locked[..., self.shape//2, :],
est_figure_ct_locked,
est_figure_ct_random,
rec_figure_ct_locked_locked,
rec_figure_ct_locked_random,
], dim=-2).transpose(2, 3),
torch.cat([est_volume_ct_locked[..., self.shape//2, :],
src_figure_xr_hidden,
est_volume_xr_locked[..., self.shape//2, :],
est_figure_xr_locked_locked,
rec_figure_xr_random_locked,
], dim=-2).transpose(2, 3)
], dim=-2)
grid = torchvision.utils.make_grid(viz2d, normalize=False, scale_each=False, nrow=1, padding=0)
tensorboard = self.logger.experiment
tensorboard.add_image(f'{stage}_samples', grid.clamp(0., 1.), self.current_epoch*self.batch_size + batch_idx)
info = {f'loss': loss}
return info
def training_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='train')
def validation_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='validation')
def test_step(self, batch, batch_idx):
return self._common_step(batch, batch_idx, optimizer_idx=0, stage='test')
def _common_epoch_end(self, outputs, stage: Optional[str] = 'common'):
loss = torch.stack([x[f'loss'] for x in outputs]).mean()
self.log(f'{stage}_loss_epoch', loss, on_step=False, prog_bar=True, logger=True, sync_dist=True)
def train_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='train')
def validation_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='validation')
def test_epoch_end(self, outputs):
return self._common_epoch_end(outputs, stage='test')
def configure_optimizers(self):
optimizer = torch.optim.RAdam(self.inv_renderer.parameters(), lr=self.lr, weight_decay=self.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 200], gamma=0.1)
return [optimizer], [scheduler]
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--conda_env", type=str, default="Unet")
parser.add_argument("--notification_email", type=str, default="[email protected]")
# Model arguments
parser.add_argument("--n_pts_per_ray", type=int, default=512, help="Sampling points per ray")
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--shape", type=int, default=256, help="spatial size of the tensor")
parser.add_argument("--epochs", type=int, default=301, help="number of epochs")
parser.add_argument("--train_samples", type=int, default=1000, help="training samples")
parser.add_argument("--val_samples", type=int, default=400, help="validation samples")
parser.add_argument("--test_samples", type=int, default=400, help="test samples")
parser.add_argument("--alpha", type=float, default=3., help="im3d loss")
parser.add_argument("--gamma", type=float, default=1., help="im2d loss")
parser.add_argument("--weight_decay", type=float, default=1e-4, help="Weight decay")
parser.add_argument("--lr", type=float, default=2e-4, help="adam: learning rate")
parser.add_argument("--ckpt", type=str, default=None, help="path to checkpoint")
parser.add_argument("--logsdir", type=str, default='logsfrecaling', help="logging directory")
parser.add_argument("--datadir", type=str, default='data', help="data directory")
parser.add_argument("--filter", type=str, default='sobel', help="None, sobel, laplacian, canny")
parser = Trainer.add_argparse_args(parser)
# Collect the hyper parameters
hparams = parser.parse_args()
# Seed the application
seed_everything(42)
# Callback
checkpoint_callback = ModelCheckpoint(
dirpath=hparams.logsdir,
filename='{epoch:02d}-{validation_loss_epoch:.2f}',
save_top_k=-1,
save_last=True,
every_n_epochs=5,
)
lr_callback = LearningRateMonitor(logging_interval='step')
# Logger
tensorboard_logger = TensorBoardLogger(save_dir=hparams.logsdir, log_graph=True)
# Init model with callbacks
trainer = Trainer.from_argparse_args(
hparams,
max_epochs=hparams.epochs,
logger=[tensorboard_logger],
callbacks=[
lr_callback,
checkpoint_callback,
],
accumulate_grad_batches=4,
# strategy="ddp_sharded", #"horovod", #"deepspeed", #"ddp_sharded",
strategy="fsdp", # "fsdp", #"ddp_sharded", #"horovod", #"deepspeed", #"ddp_sharded",
precision=16, # if hparams.use_amp else 32,
# stochastic_weight_avg=True,
# deterministic=False,
# profiler="simple",
)
# Create data module
train_image3d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/NSCLC/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-0'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-1'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-2'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-3'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-4'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Imagenglab/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/val/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/train/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/val/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/train/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/test/images/'),
]
train_label3d_folders = [
]
train_image2d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/JSRT/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/ChinaSet/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Montgomery/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/train/images/'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/test/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62020/20200501/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62021/20211101/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/train/images/'),
# # os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/test/images/'),
]
train_label2d_folders = [
]
val_image3d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/NSCLC/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-0'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-1'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-2'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-3'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MOSMED/processed/train/images/CT-4'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Imagenglab/processed/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/train/images'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/MELA2022/raw/val/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/train/images'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/AMOS2022/raw/val/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/train/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/val/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2019/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/Verse2020/raw/test/rawdata/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/train/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/UWSpine/processed/test/images/'),
]
val_image2d_folders = [
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/JSRT/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/ChinaSet/processed/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/Montgomery/processed/images/'),
# os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/train/images/'),
os.path.join(hparams.datadir, 'ChestXRLungSegmentation/VinDr/v1/processed/test/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62020/20200501/raw/images'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/T62021/20211101/raw/images'),
# # os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/train/images/'),
# os.path.join(hparams.datadir, 'SpineXRVertSegmentation/VinDr/v1/processed/test/images/'),
]
test_image3d_folders = val_image3d_folders
test_image2d_folders = val_image2d_folders
datamodule = UnpairedDataModule(
train_image3d_folders=train_image3d_folders,
train_image2d_folders=train_image2d_folders,
val_image3d_folders=val_image3d_folders,
val_image2d_folders=val_image2d_folders,
test_image3d_folders=test_image3d_folders,
test_image2d_folders=test_image2d_folders,
train_samples=hparams.train_samples,
val_samples=hparams.val_samples,
test_samples=hparams.test_samples,
batch_size=hparams.batch_size,
img_shape=hparams.shape,
vol_shape=hparams.shape
)
datamodule.setup()
####### Test camera mu and bandwidth ########
# test_random_uniform_cameras(hparams, datamodule)
#############################################
model = UnetLightningModule(
hparams=hparams
)
# model = model.load_from_checkpoint(hparams.ckpt, strict=False) if hparams.ckpt is not None else model
trainer.fit(
model,
datamodule,
ckpt_path=hparams.ckpt if hparams.ckpt is not None else None, # "some/path/to/my_checkpoint.ckpt"
)
# test
# serve