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import os
import pytorch_lightning as pl
import matplotlib.pyplot as plt
import csv
import torch
from monai.transforms import AsDiscrete, Activations, Compose, EnsureType
from models.SegTranVAE.SegTranVAE import SegTransVAE
from loss.loss import Loss_VAE, DiceScore
from monai.losses import DiceLoss
import pytorch_lightning as pl
from monai.inferers import sliding_window_inference
class BRATS(pl.LightningModule):
def __init__(self,train_loader,val_loader,test_loader, use_VAE = True, lr = 1e-4 ):
super().__init__()
self.train_loader = train_loader
self.val_loader = val_loader
self.test_loader = test_loader
self.use_vae = use_VAE
self.lr = lr
self.model = SegTransVAE((128, 128, 128), 8, 4, 3, 768, 8, 4, 3072, in_channels_vae=128, use_VAE = use_VAE)
self.loss_vae = Loss_VAE()
self.dice_loss = DiceLoss(to_onehot_y=False, sigmoid=True, squared_pred=True)
self.post_trans_images = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold_values=True),
]
)
self.best_val_dice = 0
self.training_step_outputs = []
self.val_step_loss = []
self.val_step_dice = []
self.val_step_dice_tc = []
self.val_step_dice_wt = []
self.val_step_dice_et = []
self.test_step_loss = []
self.test_step_dice = []
self.test_step_dice_tc = []
self.test_step_dice_wt = []
self.test_step_dice_et = []
def forward(self, x, is_validation = True):
return self.model(x, is_validation)
def training_step(self, batch, batch_index):
inputs, labels = (batch['image'], batch['label'])
if not self.use_vae:
outputs = self.forward(inputs, is_validation=False)
loss = self.dice_loss(outputs, labels)
else:
outputs, recon_batch, mu, sigma = self.forward(inputs, is_validation=False)
vae_loss = self.loss_vae(recon_batch, inputs, mu, sigma)
dice_loss = self.dice_loss(outputs, labels)
loss = dice_loss + 1/(4 * 128 * 128 * 128) * vae_loss
self.training_step_outputs.append(loss)
self.log('train/vae_loss', vae_loss)
self.log('train/dice_loss', dice_loss)
if batch_index == 10:
tensorboard = self.logger.experiment
fig, ax = plt.subplots(nrows=1, ncols=6, figsize=(10, 5))
ax[0].imshow(inputs.detach().cpu()[0][0][:, :, 80], cmap='gray')
ax[0].set_title("Input")
ax[1].imshow(recon_batch.detach().cpu().float()[0][0][:,:, 80], cmap='gray')
ax[1].set_title("Reconstruction")
ax[2].imshow(labels.detach().cpu().float()[0][0][:,:, 80], cmap='gray')
ax[2].set_title("Labels TC")
ax[3].imshow(outputs.sigmoid().detach().cpu().float()[0][0][:,:, 80], cmap='gray')
ax[3].set_title("TC")
ax[4].imshow(labels.detach().cpu().float()[0][2][:,:, 80], cmap='gray')
ax[4].set_title("Labels ET")
ax[5].imshow(outputs.sigmoid().detach().cpu().float()[0][2][:,:, 80], cmap='gray')
ax[5].set_title("ET")
tensorboard.add_figure('train_visualize', fig, self.current_epoch)
self.log('train/loss', loss)
return loss
def on_train_epoch_end(self):
## F1 Macro all epoch saving outputs and target per batch
# free up the memory
# --> HERE STEP 3 <--
epoch_average = torch.stack(self.training_step_outputs).mean()
self.log("training_epoch_average", epoch_average)
self.training_step_outputs.clear() # free memory
def validation_step(self, batch, batch_index):
inputs, labels = (batch['image'], batch['label'])
roi_size = (128, 128, 128)
sw_batch_size = 1
outputs = sliding_window_inference(
inputs, roi_size, sw_batch_size, self.model, overlap = 0.5)
loss = self.dice_loss(outputs, labels)
val_outputs = self.post_trans_images(outputs)
metric_tc = DiceScore(y_pred=val_outputs[:, 0:1], y=labels[:, 0:1], include_background = True)
metric_wt = DiceScore(y_pred=val_outputs[:, 1:2], y=labels[:, 1:2], include_background = True)
metric_et = DiceScore(y_pred=val_outputs[:, 2:3], y=labels[:, 2:3], include_background = True)
mean_val_dice = (metric_tc + metric_wt + metric_et)/3
self.val_step_loss.append(loss)
self.val_step_dice.append(mean_val_dice)
self.val_step_dice_tc.append(metric_tc)
self.val_step_dice_wt.append(metric_wt)
self.val_step_dice_et.append(metric_et)
return {'val_loss': loss, 'val_mean_dice': mean_val_dice, 'val_dice_tc': metric_tc,
'val_dice_wt': metric_wt, 'val_dice_et': metric_et}
def on_validation_epoch_end(self):
loss = torch.stack(self.val_step_loss).mean()
mean_val_dice = torch.stack(self.val_step_dice).mean()
metric_tc = torch.stack(self.val_step_dice_tc).mean()
metric_wt = torch.stack(self.val_step_dice_wt).mean()
metric_et = torch.stack(self.val_step_dice_et).mean()
self.log('val/Loss', loss)
self.log('val/MeanDiceScore', mean_val_dice)
self.log('val/DiceTC', metric_tc)
self.log('val/DiceWT', metric_wt)
self.log('val/DiceET', metric_et)
os.makedirs(self.logger.log_dir, exist_ok=True)
if self.current_epoch == 0:
with open('{}/metric_log.csv'.format(self.logger.log_dir), 'w') as f:
writer = csv.writer(f)
writer.writerow(['Epoch', 'Mean Dice Score', 'Dice TC', 'Dice WT', 'Dice ET'])
with open('{}/metric_log.csv'.format(self.logger.log_dir), 'a') as f:
writer = csv.writer(f)
writer.writerow([self.current_epoch, mean_val_dice.item(), metric_tc.item(), metric_wt.item(), metric_et.item()])
if mean_val_dice > self.best_val_dice:
self.best_val_dice = mean_val_dice
self.best_val_epoch = self.current_epoch
print(
f"\n Current epoch: {self.current_epoch} Current mean dice: {mean_val_dice:.4f}"
f" tc: {metric_tc:.4f} wt: {metric_wt:.4f} et: {metric_et:.4f}"
f"\n Best mean dice: {self.best_val_dice}"
f" at epoch: {self.best_val_epoch}"
)
self.val_step_loss.clear()
self.val_step_dice.clear()
self.val_step_dice_tc.clear()
self.val_step_dice_wt.clear()
self.val_step_dice_et.clear()
return {'val_MeanDiceScore': mean_val_dice}
def test_step(self, batch, batch_index):
inputs, labels = (batch['image'], batch['label'])
roi_size = (128, 128, 128)
sw_batch_size = 1
test_outputs = sliding_window_inference(
inputs, roi_size, sw_batch_size, self.forward, overlap = 0.5)
loss = self.dice_loss(test_outputs, labels)
test_outputs = self.post_trans_images(test_outputs)
metric_tc = DiceScore(y_pred=test_outputs[:, 0:1], y=labels[:, 0:1], include_background = True)
metric_wt = DiceScore(y_pred=test_outputs[:, 1:2], y=labels[:, 1:2], include_background = True)
metric_et = DiceScore(y_pred=test_outputs[:, 2:3], y=labels[:, 2:3], include_background = True)
mean_test_dice = (metric_tc + metric_wt + metric_et)/3
self.test_step_loss.append(loss)
self.test_step_dice.append(mean_test_dice)
self.test_step_dice_tc.append(metric_tc)
self.test_step_dice_wt.append(metric_wt)
self.test_step_dice_et.append(metric_et)
return {'test_loss': loss, 'test_mean_dice': mean_test_dice, 'test_dice_tc': metric_tc,
'test_dice_wt': metric_wt, 'test_dice_et': metric_et}
def test_epoch_end(self):
loss = torch.stack(self.test_step_loss).mean()
mean_test_dice = torch.stack(self.test_step_dice).mean()
metric_tc = torch.stack(self.test_step_dice_tc).mean()
metric_wt = torch.stack(self.test_step_dice_wt).mean()
metric_et = torch.stack(self.test_step_dice_et).mean()
self.log('test/Loss', loss)
self.log('test/MeanDiceScore', mean_test_dice)
self.log('test/DiceTC', metric_tc)
self.log('test/DiceWT', metric_wt)
self.log('test/DiceET', metric_et)
with open('{}/test_log.csv'.format(self.logger.log_dir), 'w') as f:
writer = csv.writer(f)
writer.writerow(["Mean Test Dice", "Dice TC", "Dice WT", "Dice ET"])
writer.writerow([mean_test_dice, metric_tc, metric_wt, metric_et])
self.test_step_loss.clear()
self.test_step_dice.clear()
self.test_step_dice_tc.clear()
self.test_step_dice_wt.clear()
self.test_step_dice_et.clear()
return {'test_MeanDiceScore': mean_test_dice}
def configure_optimizers(self):
optimizer = torch.optim.Adam(
self.model.parameters(), self.lr, weight_decay=1e-5, amsgrad=True
)
# optimizer = AdaBelief(self.model.parameters(),
# lr=self.lr, eps=1e-16,
# betas=(0.9,0.999), weight_decouple = True,
# rectify = False)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 200)
return [optimizer], [scheduler]
def train_dataloader(self):
return self.train_loader
def val_dataloader(self):
return self.val_loader
def test_dataloader(self):
return self.test_loader |