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import numpy as np
from light_training.dataloading.dataset import get_train_val_test_loader_from_train
import torch
import torch.nn as nn
from monai.inferers import SlidingWindowInferer
from light_training.evaluation.metric import dice
from light_training.trainer import Trainer
from monai.utils import set_determinism
from light_training.utils.files_helper import save_new_model_and_delete_last
from monai.losses.dice import DiceLoss
set_determinism(123)
import os
import argparse
def func(m, epochs):
return np.exp(-10*(1- m / epochs)**2)
class BraTSTrainer(Trainer):
def __init__(
self,
env_type,
max_epochs,
batch_size,
device="cpu",
val_every=1,
num_gpus=1,
logdir="./logs/",
roi_size=(128, 128, 128),
augmentation=True,
train_process=18,
master_ip='localhost',
master_port=17750,
training_script="train.py",
):
super().__init__(
env_type,
max_epochs,
batch_size,
device,
val_every,
num_gpus,
logdir,
master_ip,
master_port,
training_script,
train_process=train_process,
)
self.window_infer = SlidingWindowInferer(roi_size=list(roi_size), sw_batch_size=1, overlap=0.5)
self.augmentation = augmentation
from model_segmamba.segmamba import SegMamba
self.model = SegMamba(in_chans=4,
out_chans=4,
depths=[2,2,2,2],
feat_size=[48, 96, 192, 384])
self.patch_size = list(roi_size)
self.best_mean_dice = 0.0
self.ce = nn.CrossEntropyLoss()
self.mse = nn.MSELoss()
self.train_process = train_process
self.model_save_path = os.path.join(logdir, "model")
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=1e-2, weight_decay=3e-5,
momentum=0.99, nesterov=True)
self.scheduler_type = "poly"
self.cross = nn.CrossEntropyLoss()
def training_step(self, batch):
image, label = self.get_input(batch)
pred = self.model(image)
loss = self.cross(pred, label)
self.log("training_loss", loss, step=self.global_step)
return loss
def convert_labels(self, labels):
## TC, WT and ET
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
return torch.cat(result, dim=1).float()
def get_input(self, batch):
image = batch["data"]
label = batch["seg"]
label = label[:, 0].long()
return image, label
def cal_metric(self, gt, pred, voxel_spacing=[1.0, 1.0, 1.0]):
if pred.sum() > 0 and gt.sum() > 0:
d = dice(pred, gt)
return np.array([d, 50])
elif gt.sum() == 0 and pred.sum() == 0:
return np.array([1.0, 50])
else:
return np.array([0.0, 50])
def validation_step(self, batch):
image, label = self.get_input(batch)
output = self.model(image)
output = output.argmax(dim=1)
output = output[:, None]
output = self.convert_labels(output)
label = label[:, None]
label = self.convert_labels(label)
output = output.cpu().numpy()
target = label.cpu().numpy()
dices = []
c = 3
for i in range(0, c):
pred_c = output[:, i]
target_c = target[:, i]
cal_dice, _ = self.cal_metric(target_c, pred_c)
dices.append(cal_dice)
return dices
def validation_end(self, val_outputs):
dices = val_outputs
tc, wt, et = dices[0].mean(), dices[1].mean(), dices[2].mean()
print(f"dices is {tc, wt, et}")
mean_dice = (tc + wt + et) / 3
self.log("tc", tc, step=self.epoch)
self.log("wt", wt, step=self.epoch)
self.log("et", et, step=self.epoch)
self.log("mean_dice", mean_dice, step=self.epoch)
if mean_dice > self.best_mean_dice:
self.best_mean_dice = mean_dice
save_new_model_and_delete_last(self.model,
os.path.join(self.model_save_path,
f"best_model_{mean_dice:.4f}.pt"),
delete_symbol="best_model")
save_new_model_and_delete_last(self.model,
os.path.join(self.model_save_path,
f"final_model_{mean_dice:.4f}.pt"),
delete_symbol="final_model")
if (self.epoch + 1) % 100 == 0:
torch.save(self.model.state_dict(), os.path.join(self.model_save_path, f"tmp_model_ep{self.epoch}_{mean_dice:.4f}.pt"))
print(f"mean_dice is {mean_dice}")
def _parse_csv_ints(s: str, n: int):
parts = [p.strip() for p in s.split(",") if p.strip()]
if len(parts) != n:
raise ValueError(f"expect {n} integers like '128,128,128', got: {s}")
return [int(x) for x in parts]
def _parse_augmentation(s: str):
s = str(s).strip().lower()
if s in {"true", "1", "yes", "y"}:
return True
if s in {"false", "0", "no", "n"}:
return False
# allow special modes used by Trainer.get_multi_processor_loader
# e.g. nomirror / onlymirror / onlyspatial
return s
def main():
parser = argparse.ArgumentParser(description="SegMamba BraTS2023 training.")
parser.add_argument("--data_dir", type=str, default="./data/fullres/train", help="Preprocessed data directory (contains *.npz).")
parser.add_argument("--logdir", type=str, default="./logs/segmamba", help="Log/checkpoint directory.")
parser.add_argument("--env", type=str, default="pytorch", choices=["pytorch", "DDP", "ddp"], help="Training environment.")
parser.add_argument("--max_epoch", type=int, default=1000)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--val_every", type=int, default=2)
parser.add_argument("--num_gpus", type=int, default=1)
parser.add_argument("--device", type=str, default="cuda:0", help="Device for single GPU; DDP will use LOCAL_RANK.")
parser.add_argument("--roi_size", type=str, default="128,128,128", help="Patch/ROI size, e.g. '128,128,128'.")
parser.add_argument("--augmentation", type=str, default="true", help="true/false/nomirror/onlymirror/onlyspatial")
parser.add_argument("--train_process", type=int, default=18, help="Number of augmentation worker processes (per rank).")
parser.add_argument("--master_port", type=int, default=17759)
# torchrun launcher will append this; ignore it here (Trainer will read it too)
parser.add_argument("--not_call_launch", action="store_true", help=argparse.SUPPRESS)
args, _ = parser.parse_known_args()
roi_size = _parse_csv_ints(args.roi_size, 3)
augmentation = _parse_augmentation(args.augmentation)
trainer = BraTSTrainer(
env_type=args.env,
max_epochs=args.max_epoch,
batch_size=args.batch_size,
device=args.device,
logdir=args.logdir,
val_every=args.val_every,
num_gpus=args.num_gpus,
master_port=args.master_port,
training_script=__file__,
roi_size=roi_size,
augmentation=augmentation,
train_process=args.train_process,
)
train_ds, val_ds, test_ds = get_train_val_test_loader_from_train(args.data_dir)
trainer.train(train_dataset=train_ds, val_dataset=val_ds)
if __name__ == "__main__":
main()
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