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assignment-a3: use tensorboard for logging

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franksim
2025-10-31 13:17:46 +00:00
parent 3d676116a7
commit c8e5796aef
2 changed files with 35 additions and 12 deletions
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1
.gitignore vendored
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@@ -1,5 +1,6 @@
.venv/ .venv/
.data .data
runs/
__pycache__/ __pycache__/
*.code-workspace *.code-workspace
.vscode/ .vscode/

46
mmp/a3/main.py
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@@ -1,35 +1,57 @@
import torch import torch
from a2.main import MmpNet, get_criterion_optimizer, log_epoch_progress, train_epoch, eval_epoch import argparse
from a2.main import MmpNet, get_criterion_optimizer, train_epoch, eval_epoch
from a3.dataset import get_dataloader from a3.dataset import get_dataloader
def main(): def main():
"""Put your code for Exercise 3.3 in here""" """Put your code for Exercise 3.3 in here"""
parser = argparse.ArgumentParser()
parser.add_argument('--tensorboard', action='store_true',
help='Enable TensorBoard logging')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_epochs = 10 train_epochs = 10
model = MmpNet(num_classes=10).to(device=device) model = MmpNet(num_classes=2).to(device=device)
dataloader_train = get_dataloader(path_to_data="/home/ubuntu/mmp_wise2526_franksim/.data/mmp-public-3.2", dataloader_train = get_dataloader(
image_size=244, batch_size=32, num_workers=6, is_train=True) path_to_data="/home/ubuntu/mmp_wise2526_franksim/.data/mmp-public-3.2",
dataloader_eval = get_dataloader(path_to_data="/home/ubuntu/mmp_wise2526_franksim/.data/mmp-public-3.2", image_size=244, batch_size=32, num_workers=6, is_train=True
image_size=244, batch_size=32, num_workers=6, is_train=False) )
dataloader_eval = get_dataloader(
path_to_data="/home/ubuntu/mmp_wise2526_franksim/.data/mmp-public-3.2",
image_size=244, batch_size=32, num_workers=6, is_train=False
)
criterion, optimizer = get_criterion_optimizer(model=model) criterion, optimizer = get_criterion_optimizer(model=model)
for epoche in range(train_epochs): writer = None
log_epoch_progress(epoche, train_epochs, "start") if args.tensorboard:
train_epoch( from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(log_dir="runs/a3_mmpnet")
for epoch in range(train_epochs):
train_loss = train_epoch(
model=model, model=model,
loader=dataloader_train, loader=dataloader_train,
optimizer=optimizer, optimizer=optimizer,
device=device, device=device,
criterion=criterion, criterion=criterion,
) )
eval_epoch( val_acc = eval_epoch(
model=model, model=model,
loader=dataloader_eval, loader=dataloader_eval,
device=device device=device
) )
log_epoch_progress(epoche, train_epochs, "end")
print(
f"Epoch [{epoch+1}/{train_epochs}] - Train Loss: {train_loss:.4f} - Val Acc: {val_acc:.4f}")
if writer is not None:
writer.add_scalar("Loss/train", train_loss, epoch)
writer.add_scalar("Accuracy/val", val_acc, epoch)
if writer is not None:
writer.close()
if __name__ == "__main__": if __name__ == "__main__":