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train_deepreg.py
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train_deepreg.py
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import os
import numpy as np
import torch
import gc
import time
import ml_collections
os.environ['VXM_BACKEND'] = 'pytorch'
import voxelmorph as vxm
from data_utils import load_data_task03
from voxelmorph.torch.losses import NCC, Grad
from monai.losses import DiceLoss, MultiScaleLoss
from losses import DTMSELoss, MINDSSCLoss
from normalized_gradient_field import NormalizedGradientField3d
from monai.metrics import HausdorffDistanceMetric, SurfaceDistanceMetric, DiceMetric
from monai.networks import one_hot
from monai.data import DataLoader
from metrics import SDlogJac
from deepregnet import RegNet
from log_utils import LogWriter
from utils import run_epoch
from my_argparse import regnet_argparse
from torchinfo import summary
import polyaxon_helper
from monai.utils import first
# os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
torch.backends.cudnn.deterministric = True
torch.backends.cudnn.benchmark = True
arg = regnet_argparse(config_files="deepregnet.ini")
out_path = polyaxon_helper.get_outputs_path()
model_dir = os.path.join(out_path, arg.model_dir)
os.makedirs(model_dir, exist_ok=True)
log_dir = os.path.join(out_path, arg.log_dir)
logWriter = LogWriter(log_dir)
dataset = load_data_task03(arg.data_dir, cache_rate=0.05, num_workers=4)
train_dataset = dataset[:-20]
val_dataset = dataset[-20:]
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
train_size = len(train_loader)
val_size = len(val_loader)
def define_model(arg):
config = ml_collections.ConfigDict()
config.spatial_dims = len(arg.inshape)
config.in_channels = 2
config.out_channels = 3
config.num_channel_initial = arg.num_channel_initial
config.extract_levels = arg.extract_levels
config.out_activation = None
config.out_kernel_initializer = "zeros"
config.pooling = True
config.concat_skip = False
if not arg.use_last_ckpt:
model = RegNet(inshape=arg.inshape,
in_channels=config.in_channels,
num_channel_initial=config.num_channel_initial,
extract_levels=config.extract_levels,
out_kernel_initializer=config.out_kernel_initializer,
out_activation=config.out_activation,
out_channels=config.out_channels,
pooling=config.pooling,
concat_skip=config.concat_skip,
int_steps=arg.int_steps,
int_downsize=arg.int_downsize,
bidir=arg.bidir)
else:
model = RegNet.load(arg.load_model, arg.device)
model = model.to(arg.device)
with torch.no_grad():
summary(model, [(1, 1, *arg.inshape), (1, 1, *arg.inshape),
(1, 1, *arg.inshape), (1, 1, *arg.inshape),
(1, 1, *arg.inshape), (1, 1, *arg.inshape)])
torch.cuda.empty_cache()
gc.collect()
print(torch.cuda.get_device_name(0), torch.cuda.device_count())
return model, config
model, config = define_model(arg)
if arg.use_last_ckpt:
start_epoch = arg.start_epoch
# lr = arg.lr * (arg.decay_rate ** (start_epoch - 1))
lr = arg.lr
model.bidir = arg.bidir
else:
start_epoch = 0
lr = arg.lr
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=arg.decay_rate)
loss_func = [NCC().loss,
MINDSSCLoss(radius=2,
dilation=2),
MultiScaleLoss(DiceLoss(include_background=False, to_onehot_y=False),
scales=[0]),
Grad('l2', loss_mult=arg.int_downsize).loss,
DTMSELoss(alpha=2),
NormalizedGradientField3d(eps_src=1e-3, eps_tar=1e-3, mm_spacing=1)]
loss_weights = [arg.ncc_loss_weight, arg.mind_loss_weight, arg.dice_loss_weight,
arg.grad_loss_weight, arg.dtmse_loss_weight, arg.ngf_loss_weight]
metric_func = [SDlogJac(),
HausdorffDistanceMetric(percentile=95),
SurfaceDistanceMetric(),
DiceMetric(include_background=False)]
args_dict = vars(arg)
args_dict.update(dict(config))
args_dict['train_size'] = train_size
args_dict['val_size'] = val_size
logWriter.log_configuration(args_dict)
# logWriter.log_configuration(bidir=arg.bidir, int_steps=arg.int_steps, int_downsize=arg.int_downsize,
# num_channel_initial=arg.num_channel_initial, extract_levels=arg.extract_levels,
# out_kernel_initializer=config.out_kernel_initializer, pooling=config.pooling,
# concat_skip=config.concat_skip,
# lr=arg.lr, decay_rate=arg.decay_rate,
# ncc_loss_weight=arg.ncc_loss_weight,
# mind_loss_weight=arg.mind_loss_weight,
# dice_loss_weight=arg.dice_loss_weight,
# grad_loss_weight=arg.grad_loss_weight,
# dtmse_loss_weight=arg.dtmse_loss_weight,
# ngf_loss_weight=arg.ngf_loss_weight,
# flipping=arg.flipping)
best_dice_loss = 1
for epoch in range(start_epoch, arg.max_epochs):
if epoch % arg.val_interval == 0 or epoch == start_epoch:
model.eval()
phase = 'val'
with torch.no_grad():
val_dice_loss = run_epoch(model, val_loader, optimizer,
loss_func, loss_weights, metric_func,
arg.bidir, arg.flipping, logWriter,
arg.device, phase, epoch)
if (arg.max_epochs - epoch) < arg.eval_best_epoch and val_dice_loss < best_dice_loss:
best_dice_loss = val_dice_loss
model.save(os.path.join(model_dir, 'best.pt'))
print(f"save best model at {epoch} epoch")
model.train()
phase = 'train'
run_epoch(model, train_loader, optimizer,
loss_func, loss_weights, metric_func,
arg.bidir, arg.flipping, logWriter,
arg.device, phase, epoch)
lr_scheduler.step()
if epoch % arg.save_interval == 0:
model.save(os.path.join(model_dir, f'{epoch:04d}.pt'))
model.save(os.path.join(model_dir, f'{arg.max_epochs:04d}.pt'))