def validation_step(self, batch, batch_idx: int):
inputs, targets = batch["image"], batch["label"]
logits = self(inputs)
loss = self.criterion(logits.view(-1), targets.view(-1))
if self.current_epoch % self.hparams.log_mod == 0 and batch_idx == 0:
log_all_info(
module=self,
target=targets[0],
preb=logits[0],
loss=loss,
batch_idx=batch_idx,
state="val",
)
self.log("val_loss", loss, sync_dist=True, on_step=True, on_epoch=True)
targets = targets.cpu().detach().numpy().squeeze()
predicts = logits.cpu().detach().numpy().squeeze()
if batch_idx <= 5:
np.savez(f"{batch_idx}.npz", target=targets, predict=predicts)
mse = np.square(np.subtract(targets, predicts)).mean()
ssim_ = ssim(targets,
predicts,
data_range=predicts.max() - predicts.min())
psnr_ = psnr(targets,
predicts,
data_range=predicts.max() - predicts.min())
brain_mask = targets == targets[0][0][0]
pred_clip = np.clip(predicts, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(predicts))
targ_clip = np.clip(targets, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(targets))
pred_255 = np.floor(256 * (pred_clip /
(self.clip_min + self.clip_max)))
targ_255 = np.floor(256 * (targ_clip /
(self.clip_min + self.clip_max)))
pred_255[brain_mask] = 0
targ_255[brain_mask] = 0
diff_255 = np.absolute(pred_255.ravel() - targ_255.ravel())
mae = np.mean(diff_255)
diff_255_mask = np.absolute(pred_255[~brain_mask].ravel() -
targ_255[~brain_mask].ravel())
np.mean(diff_255_mask)
# return {"MAE": mae, "MAE_mask": mae_mask, "MSE": mse}
return {"MAE": mae, "MSE": mse, "SSIM": ssim_, "PSNR": psnr_}
def test_step(self, batch, batch_idx: int):
inputs, targets = batch["image"], batch["label"]
logits = self(inputs)
loss = self.criterion(logits.view(-1), targets.view(-1))
if batch_idx <= 15:
log_all_info(
module=self,
target=targets,
preb=logits,
loss=loss,
batch_idx=batch_idx,
state="val",
)
targets = targets.cpu().detach().numpy().squeeze()
predicts = logits.cpu().detach().numpy().squeeze()
mse = np.square(np.subtract(targets, predicts)).mean()
if batch_idx <= 5:
np.savez(f"{batch_idx}.npz", target=targets, predict=predicts)
brain_mask = targets == targets[0][0][0]
pred_clip = np.clip(predicts, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(predicts))
targ_clip = np.clip(targets, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(targets))
pred_255 = np.floor(256 * (pred_clip /
(self.clip_min + self.clip_max)))
targ_255 = np.floor(256 * (targ_clip /
(self.clip_min + self.clip_max)))
pred_255[brain_mask] = 0
targ_255[brain_mask] = 0
diff_255 = np.absolute(pred_255.ravel() - targ_255.ravel())
mae = np.mean(diff_255)
diff_255_mask = np.absolute(pred_255[~brain_mask].ravel() -
targ_255[~brain_mask].ravel())
mae_mask = np.mean(diff_255_mask)
return {"MAE": mae, "MAE_mask": mae_mask, "MSE": mse}
def validation_step(self, batch, batch_idx: int):
inputs, targets = batch
inputs, targets = torch.squeeze(inputs), torch.squeeze(
targets) # only use 2D slices to train
logits = self(inputs)
loss = self.criterion(logits.view(-1), targets.view(-1))
if self.current_epoch % 15 == 0 and batch_idx == 0:
log_all_info(
module=self,
target=targets,
preb=logits,
loss=loss,
batch_idx=batch_idx,
state="val",
)
self.log("val_loss", loss, sync_dist=True, on_step=True, on_epoch=True)
targets = targets.cpu().detach().numpy().squeeze()
predicts = logits.cpu().detach().numpy().squeeze()
brain_mask = targets == targets[0][0][0]
pred_clip = np.clip(predicts, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(predicts))
targ_clip = np.clip(targets, -self.clip_min, self.clip_max) - min(
-self.clip_min, np.min(targets))
pred_255 = np.floor(256 * (pred_clip /
(self.clip_min + self.clip_max)))
targ_255 = np.floor(256 * (targ_clip /
(self.clip_min + self.clip_max)))
pred_255[brain_mask] = 0
targ_255[brain_mask] = 0
diff_255 = np.absolute(pred_255.ravel() - targ_255.ravel())
mae = np.mean(diff_255)
diff_255_mask = np.absolute(pred_255[~brain_mask].ravel() -
targ_255[~brain_mask].ravel())
mae_mask = np.mean(diff_255_mask)
return {"MAE": mae, "MAE_mask": mae_mask}
def training_step(self, batch, batch_idx: int):
inputs, targets = batch["image"], batch["label"]
logits = self(inputs)
loss = self.criterion(logits.view(-1), targets.view(-1))
if self.current_epoch % self.hparams.log_mod == 0 and batch_idx == 0:
log_all_info(
module=self,
target=targets[0],
preb=logits[0],
loss=loss,
batch_idx=batch_idx,
state="train",
)
self.log("train_loss",
loss,
sync_dist=True,
on_step=True,
on_epoch=True)
return {"loss": loss}
def training_step(self, batch, batch_idx: int):
inputs, targets = batch
inputs, targets = torch.squeeze(inputs), torch.squeeze(
targets) # only use 2D slices to train
logits = self(inputs)
loss = self.criterion(logits.view(-1), targets.view(-1))
if self.current_epoch % 15 == 0 and batch_idx == 0:
log_all_info(
module=self,
target=targets,
preb=logits,
loss=loss,
batch_idx=batch_idx,
state="train",
)
self.log("train_loss",
loss,
sync_dist=True,
on_step=True,
on_epoch=True)
return {"loss": loss}