ENSEEIHT/projet-long
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feat: export to onnx

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Former-commit-id: fd7e5a5ab785263a16381545ca31fd9e7fe86743 [formerly 10fdf9732fbcf4d922d945adc625e948e5f6e775]
Former-commit-id: 871745033b59e626fc38b38bfc8685c6a6366ecf
This commit is contained in:
Laurent Fainsin 2022-06-30 11:44:20 +02:00
parent 81938b944e
commit dc4a399c0f
2 changed files with 30 additions and 11 deletions
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2
.gitignore vendored
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@ -6,5 +6,7 @@ wandb/
images/
*.pth
*.onnx
*.png
*.jpg

39
src/train.py
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@ -5,6 +5,7 @@ from pathlib import Path
import albumentations as A
import torch
import torch.nn as nn
import torch.onnx
from albumentations.pytorch import ToTensorV2
from torch import optim
from torch.utils.data import DataLoader
@ -17,7 +18,7 @@ from unet import UNet
from utils.paste import RandomPaste
CHECKPOINT_DIR = Path("./checkpoints/")
DIR_TRAIN_IMG = Path("/home/lilian/data_disk/lfainsin/val2017")
DIR_TRAIN_IMG = Path("/home/lilian/data_disk/lfainsin/smolval2017")
DIR_VALID_IMG = Path("/home/lilian/data_disk/lfainsin/smoltrain2017/")
DIR_SPHERE_IMG = Path("/home/lilian/data_disk/lfainsin/spheres/Images/")
DIR_SPHERE_MASK = Path("/home/lilian/data_disk/lfainsin/spheres/Masks/")
@ -89,16 +90,17 @@ def main():
logging.info(f"Using device {device}")
# enable cudnn benchmarking
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.benchmark = True
# 0. Create network
features = [16, 32, 64, 128]
net = UNet(n_channels=args.n_channels, n_classes=args.classes, features=features)
net = UNet(n_channels=3, n_classes=args.classes, features=features)
nb_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
logging.info(
f"""Network:
input channels: {net.n_channels}
output channels: {net.n_classes}
nb parameters: {sum(p.numel() for p in net.parameters() if p.requires_grad)}
nb parameters: {nb_params}
features: {features}
"""
)
@ -152,19 +154,21 @@ def main():
criterion = nn.BCEWithLogitsLoss()
# setup wandb
run = wandb.init(
wandb.init(
project="U-Net-tmp",
config=dict(
epochs=args.epochs,
batch_size=args.batch_size,
learning_rate=args.lr,
amp=args.amp,
features=features,
parameters=nb_params,
),
)
wandb.watch(net, log_freq=100)
artifact_model = wandb.Artifact("model", type="model")
artifact_model.add_file("model.pth")
run.log_artifact(artifact_model)
wandb.watch(net, log_freq=len(ds_train) // args.batch_size // 4)
artifact = wandb.Artifact("model", type="model")
artifact.add_file("model.pth")
wandb.run.log_artifact(artifact)
logging.info(
f"""Starting training:
@ -228,13 +232,25 @@ def main():
}
)
print(f"Train Loss: {train_loss:.3f}, Valid Score: {val_score:3f}")
logging.info(
f"""Validation ended:
Train Loss: {train_loss}
Valid Score: {val_score}
"""
)
# save weights when epoch end
torch.save(net.state_dict(), "model.pth")
artifact = wandb.Artifact("model", type="model")
artifact.add_file("model.pth")
wandb.run.log_artifact(artifact)
logging.info(f"model saved!")
run.finish()
# export model to onnx format
dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
torch.onnx.export(net, dummy_input, "model.onnx")
wandb.run.finish()
except KeyboardInterrupt:
torch.save(net.state_dict(), "INTERRUPTED.pth")
@ -244,3 +260,4 @@ def main():
if __name__ == "__main__":
main()
# TODO: fix toutes les metrics, loss, accuracy, dice...