mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 14:39:00 +00:00
feat: validate every 100 steps
Former-commit-id: a3367de4ed56c5a708d66e7cd6be27d52bb92ccc [formerly 4606a91526eae57d56fc93df7ed34b867495e1c5] Former-commit-id: 6584449cd25b18ddd46f6804c8b1653e1c72dda0
This commit is contained in:
Laurent Fainsin
parent
cf8f52735a
commit
2571e5c6d3
151
src/train.py
151
src/train.py
|
|
@ -137,7 +137,7 @@ if __name__ == "__main__":
|
|||
wandb.run.log_artifact(artifact)
|
||||
|
||||
# log gradients and weights four time per epoch
|
||||
wandb.watch(net, log_freq=(len(train_loader) + len(val_loader)) // 4)
|
||||
wandb.watch(net, criterion, log_freq=100)
|
||||
|
||||
# print the config
|
||||
logging.info(f"wandb config:\n{yaml.dump(wandb.config.as_dict())}")
|
||||
|
|
@ -198,92 +198,87 @@ if __name__ == "__main__":
|
|||
}
|
||||
)
|
||||
|
||||
# Evaluation round
|
||||
net.eval()
|
||||
accuracy = 0
|
||||
val_loss = 0
|
||||
dice = 0
|
||||
mae = 0
|
||||
with tqdm(val_loader, total=len(ds_valid), desc="val", unit="img", leave=False) as pbar:
|
||||
for images, masks_true in val_loader:
|
||||
if step and (step % 100 == 0 or step == len(train_loader)):
|
||||
# Evaluation round
|
||||
net.eval()
|
||||
accuracy = 0
|
||||
val_loss = 0
|
||||
dice = 0
|
||||
mae = 0
|
||||
with tqdm(val_loader, total=len(ds_valid), desc="val", unit="img", leave=False) as pbar2:
|
||||
for images, masks_true in val_loader:
|
||||
|
||||
# transfer images to device
|
||||
images = images.to(device=device)
|
||||
masks_true = masks_true.unsqueeze(1).to(device=device)
|
||||
# transfer images to device
|
||||
images = images.to(device=device)
|
||||
masks_true = masks_true.unsqueeze(1).to(device=device)
|
||||
|
||||
# forward
|
||||
with torch.inference_mode():
|
||||
masks_pred = net(images)
|
||||
# forward
|
||||
with torch.inference_mode():
|
||||
masks_pred = net(images)
|
||||
|
||||
# compute metrics
|
||||
val_loss += criterion(pred_masks, true_masks)
|
||||
mae += torch.nn.functional.l1_loss(pred_masks_bin, true_masks)
|
||||
masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
|
||||
accuracy += (true_masks == pred_masks_bin).float().mean()
|
||||
dice += dice_coeff(masks_pred_bin, masks_true)
|
||||
# compute metrics
|
||||
val_loss += criterion(pred_masks, true_masks)
|
||||
mae += torch.nn.functional.l1_loss(pred_masks_bin, true_masks)
|
||||
masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
|
||||
accuracy += (true_masks == pred_masks_bin).float().mean()
|
||||
dice += dice_coeff(masks_pred_bin, masks_true)
|
||||
|
||||
# update progress bar
|
||||
pbar.update(images.shape[0])
|
||||
# update progress bar
|
||||
pbar2.update(images.shape[0])
|
||||
|
||||
accuracy /= len(val_loader)
|
||||
val_loss /= len(val_loader)
|
||||
dice /= len(val_loader)
|
||||
mae /= len(val_loader)
|
||||
accuracy /= len(val_loader)
|
||||
val_loss /= len(val_loader)
|
||||
dice /= len(val_loader)
|
||||
mae /= len(val_loader)
|
||||
|
||||
# save the last validation batch to table
|
||||
table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
|
||||
for i, (img, mask, pred, pred_bin) in enumerate(
|
||||
zip(
|
||||
images.to("cpu"),
|
||||
masks_true.to("cpu"),
|
||||
masks_pred.to("cpu"),
|
||||
masks_pred_bin.to("cpu").squeeze().int().numpy(),
|
||||
)
|
||||
):
|
||||
table.add_data(
|
||||
i,
|
||||
wandb.Image(img),
|
||||
wandb.Image(mask),
|
||||
wandb.Image(
|
||||
pred,
|
||||
masks={
|
||||
"predictions": {
|
||||
"mask_data": pred_bin,
|
||||
"class_labels": class_labels,
|
||||
},
|
||||
# save the last validation batch to table
|
||||
table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
|
||||
for i, (img, mask, pred, pred_bin) in enumerate(
|
||||
zip(
|
||||
images.to("cpu"),
|
||||
masks_true.to("cpu"),
|
||||
masks_pred.to("cpu"),
|
||||
masks_pred_bin.to("cpu").squeeze().int().numpy(),
|
||||
)
|
||||
):
|
||||
table.add_data(
|
||||
i,
|
||||
wandb.Image(img),
|
||||
wandb.Image(mask),
|
||||
wandb.Image(
|
||||
pred,
|
||||
masks={
|
||||
"predictions": {
|
||||
"mask_data": pred_bin,
|
||||
"class_labels": class_labels,
|
||||
},
|
||||
},
|
||||
),
|
||||
)
|
||||
|
||||
# log validation metrics
|
||||
wandb.log(
|
||||
{
|
||||
"predictions": table,
|
||||
"train/learning_rate": optimizer.state_dict()["param_groups"][0]["lr"],
|
||||
"val/accuracy": accuracy,
|
||||
"val/bce": val_loss,
|
||||
"val/dice": dice,
|
||||
"val/mae": mae,
|
||||
},
|
||||
),
|
||||
)
|
||||
commit=False,
|
||||
)
|
||||
|
||||
# log validation metrics
|
||||
wandb.log(
|
||||
{
|
||||
"predictions": table,
|
||||
"train/learning_rate": optimizer.state_dict()["param_groups"][0]["lr"],
|
||||
"val/accuracy": accuracy,
|
||||
"val/bce": val_loss,
|
||||
"val/dice": dice,
|
||||
"val/mae": mae,
|
||||
},
|
||||
commit=False,
|
||||
)
|
||||
# update hyperparameters
|
||||
net.train()
|
||||
scheduler.step(dice)
|
||||
|
||||
# update hyperparameters
|
||||
net.train()
|
||||
scheduler.step(dice)
|
||||
|
||||
# save weights when epoch end
|
||||
torch.save(net.state_dict(), f"checkpoints/model-{epoch}.pth")
|
||||
artifact = wandb.Artifact("pth", type="model")
|
||||
artifact.add_file(f"checkpoints/model-{epoch}.pth")
|
||||
wandb.run.log_artifact(artifact)
|
||||
|
||||
# export model to onnx format
|
||||
dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
|
||||
torch.onnx.export(net, dummy_input, f"checkpoints/model-{epoch}.onnx")
|
||||
artifact = wandb.Artifact("onnx", type="model")
|
||||
artifact.add_file(f"checkpoints/model-{epoch}.onnx")
|
||||
wandb.run.log_artifact(artifact)
|
||||
# export model to onnx format when validation ends
|
||||
dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
|
||||
torch.onnx.export(net, dummy_input, f"checkpoints/model-{epoch}-{step}.onnx")
|
||||
artifact = wandb.Artifact("onnx", type="model")
|
||||
artifact.add_file(f"checkpoints/model-{epoch}-{step}.onnx")
|
||||
wandb.run.log_artifact(artifact)
|
||||
|
||||
# stop wandb
|
||||
wandb.run.finish()
|
||||
|
|
|
|||
Loading…
Reference in a new issue