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chore: Optimize the trainer output information and PSNR. #187

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Merged
chairc merged 1 commit into from
Dec 31, 2025
+25 −22
Merged

chore: Optimize the trainer output information and PSNR. #187

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23 changes: 11 additions & 12 deletions iddm/model/trainers/autoencoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@
from iddm.utils.utils import check_and_create_dir, save_images
from iddm.utils.dataset import get_dataset, post_image
from iddm.utils.logger import get_logger
from iddm.utils.metrics import compute_psnr

logger = get_logger(name=__name__)

Expand Down Expand Up @@ -185,10 +186,9 @@ def train_in_iter(self):
train_loss_list.append(train_loss.item())
# Loss per epoch
self.avg_train_loss = sum(train_loss_list) / len(train_loss_list)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Train loss",
scalar_value=self.avg_train_loss,
global_step=self.epoch)
logger.info(f"Train loss: {self.avg_train_loss}")
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg train loss({self.loss_func})",
scalar_value=self.avg_train_loss, global_step=self.epoch)
logger.info(f"[{self.device}]: Train loss: {self.avg_train_loss}")
logger.info(msg="Finish train mode.")

# Val
Expand All @@ -211,9 +211,9 @@ def train_in_iter(self):
global_step=self.epoch * self.len_val_dataloader + i)
val_loss_list.append(val_loss.item())

# TODO: Metric
score = 0
self.tb_logger.add_scalar(tag=f"[{self.device}]: Score({self.loss_func})", scalar_value=score,
# Metric PSNR
score = compute_psnr(mse=val_loss.item())
self.tb_logger.add_scalar(tag=f"[{self.device}]: Score(PSNR)", scalar_value=score,
global_step=self.epoch * self.len_val_dataloader + i)
score_list.append(score)

Expand All @@ -233,12 +233,11 @@ def train_in_iter(self):
# Loss, score per epoch
self.avg_val_loss = sum(val_loss_list) / len(val_loss_list)
self.avg_score = sum(score_list) / len(score_list)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Val loss", scalar_value=self.avg_val_loss,
global_step=self.epoch)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg score", scalar_value=self.avg_score,
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg val loss({self.loss_func})",
scalar_value=self.avg_val_loss, global_step=self.epoch)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg score(PSNR)", scalar_value=self.avg_score,
global_step=self.epoch)
logger.info(f"Val loss: {self.avg_val_loss}, Score: {self.avg_score}")
self.model.train()
logger.info(f"[{self.device}]: Val loss: {self.avg_val_loss}, Score(PSNR): {self.avg_score}")
logger.info(msg="Finish val mode.")

def after_iter(self):
Expand Down
8 changes: 6 additions & 2 deletions iddm/model/trainers/dm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -220,6 +220,7 @@ def train_in_iter(self):
"""
# Initialize images and labels
images, labels, loss_list = None, None, []
# Train mode
for i, (images, labels) in enumerate(self.pbar):
# The images are all resized in dataloader
images = images.to(self.device)
Expand Down Expand Up @@ -266,12 +267,15 @@ def train_in_iter(self):

# TensorBoard logging
self.pbar.set_postfix(MSE=loss.item())
self.tb_logger.add_scalar(tag=f"[{self.device}]: MSE", scalar_value=loss.item(),
self.tb_logger.add_scalar(tag=f"[{self.device}]: Train loss({self.loss_func})", scalar_value=loss.item(),
global_step=self.epoch * self.len_dataloader + i)
loss_list.append(loss.item())
# Loss per epoch
self.tb_logger.add_scalar(tag=f"[{self.device}]: Loss", scalar_value=sum(loss_list) / len(loss_list),
avg_train_loss = sum(loss_list) / len(loss_list)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg train loss", scalar_value=avg_train_loss,
global_step=self.epoch)
logger.info(msg=f"[{self.device}]: Train loss: {avg_train_loss}.")
logger.info(msg="Finish train mode.")

def after_iter(self):
"""
Expand Down
16 changes: 8 additions & 8 deletions iddm/model/trainers/sr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,9 +209,10 @@ def train_in_iter(self):
global_step=self.epoch * self.len_train_dataloader + i)
train_loss_list.append(train_loss.item())
# Loss per epoch
self.tb_logger.add_scalar(tag=f"[{self.device}]: Train loss",
scalar_value=sum(train_loss_list) / len(train_loss_list),
avg_train_loss = sum(train_loss_list) / len(train_loss_list)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg train loss({self.loss_func})", scalar_value=avg_train_loss,
global_step=self.epoch)
logger.info(msg=f"[{self.device}]: Train loss:{avg_train_loss}")
logger.info(msg="Finish train mode.")

# Val
Expand Down Expand Up @@ -240,9 +241,9 @@ def train_in_iter(self):
# Metric
ssim_res = compute_ssim(image_outputs=output, image_sources=hr_images)
psnr_res = compute_psnr(mse=val_loss.item())
self.tb_logger.add_scalar(tag=f"[{self.device}]: SSIM({self.loss_func})", scalar_value=ssim_res,
self.tb_logger.add_scalar(tag=f"[{self.device}]: SSIM", scalar_value=ssim_res,
global_step=self.epoch * self.len_val_dataloader + i)
self.tb_logger.add_scalar(tag=f"[{self.device}]: PSNR({self.loss_func})", scalar_value=psnr_res,
self.tb_logger.add_scalar(tag=f"[{self.device}]: PSNR", scalar_value=psnr_res,
global_step=self.epoch * self.len_val_dataloader + i)
ssim_list.append(ssim_res)
psnr_list.append(psnr_res)
Expand All @@ -268,12 +269,11 @@ def train_in_iter(self):
self.avg_val_loss = sum(val_loss_list) / len(val_loss_list)
self.avg_ssim = sum(ssim_list) / len(ssim_list)
self.avg_psnr = sum(psnr_list) / len(psnr_list)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Val loss", scalar_value=self.avg_val_loss,
global_step=self.epoch)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Val avg loss({self.loss_func})",
scalar_value=self.avg_val_loss, global_step=self.epoch)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg ssim", scalar_value=self.avg_ssim, global_step=self.epoch)
self.tb_logger.add_scalar(tag=f"[{self.device}]: Avg psnr", scalar_value=self.avg_psnr, global_step=self.epoch)
logger.info(f"Val loss: {self.avg_val_loss}, SSIM: {self.avg_ssim}, PSNR: {self.avg_psnr}")
self.model.train()
logger.info(f"[{self.device}]: Val loss: {self.avg_val_loss}, SSIM: {self.avg_ssim}, PSNR: {self.avg_psnr}")
logger.info(msg="Finish val mode.")

def after_iter(self):
Expand Down