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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
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@ -137,7 +137,7 @@ if __name__ == "__main__":
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wandb.run.log_artifact(artifact)
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# log gradients and weights four time per epoch
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wandb.watch(net, log_freq=(len(train_loader) + len(val_loader)) // 4)
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wandb.watch(net, criterion, log_freq=100)
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# print the config
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logging.info(f"wandb config:\n{yaml.dump(wandb.config.as_dict())}")
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@ -198,92 +198,87 @@ if __name__ == "__main__":
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}
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)
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# Evaluation round
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net.eval()
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accuracy = 0
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val_loss = 0
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dice = 0
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mae = 0
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with tqdm(val_loader, total=len(ds_valid), desc="val", unit="img", leave=False) as pbar:
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for images, masks_true in val_loader:
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if step and (step % 100 == 0 or step == len(train_loader)):
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# Evaluation round
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net.eval()
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accuracy = 0
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val_loss = 0
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dice = 0
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mae = 0
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with tqdm(val_loader, total=len(ds_valid), desc="val", unit="img", leave=False) as pbar2:
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for images, masks_true in val_loader:
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# transfer images to device
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images = images.to(device=device)
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masks_true = masks_true.unsqueeze(1).to(device=device)
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# transfer images to device
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images = images.to(device=device)
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masks_true = masks_true.unsqueeze(1).to(device=device)
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# forward
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with torch.inference_mode():
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masks_pred = net(images)
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# forward
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with torch.inference_mode():
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masks_pred = net(images)
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# compute metrics
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val_loss += criterion(pred_masks, true_masks)
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mae += torch.nn.functional.l1_loss(pred_masks_bin, true_masks)
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masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
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accuracy += (true_masks == pred_masks_bin).float().mean()
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dice += dice_coeff(masks_pred_bin, masks_true)
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# compute metrics
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val_loss += criterion(pred_masks, true_masks)
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mae += torch.nn.functional.l1_loss(pred_masks_bin, true_masks)
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masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
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accuracy += (true_masks == pred_masks_bin).float().mean()
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dice += dice_coeff(masks_pred_bin, masks_true)
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# update progress bar
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pbar.update(images.shape[0])
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# update progress bar
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pbar2.update(images.shape[0])
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accuracy /= len(val_loader)
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val_loss /= len(val_loader)
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dice /= len(val_loader)
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mae /= len(val_loader)
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accuracy /= len(val_loader)
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val_loss /= len(val_loader)
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dice /= len(val_loader)
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mae /= len(val_loader)
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# save the last validation batch to table
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table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
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for i, (img, mask, pred, pred_bin) in enumerate(
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zip(
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images.to("cpu"),
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masks_true.to("cpu"),
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masks_pred.to("cpu"),
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masks_pred_bin.to("cpu").squeeze().int().numpy(),
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)
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):
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table.add_data(
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i,
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wandb.Image(img),
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wandb.Image(mask),
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wandb.Image(
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pred,
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masks={
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"predictions": {
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"mask_data": pred_bin,
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"class_labels": class_labels,
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},
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# save the last validation batch to table
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table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
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for i, (img, mask, pred, pred_bin) in enumerate(
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zip(
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images.to("cpu"),
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masks_true.to("cpu"),
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masks_pred.to("cpu"),
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masks_pred_bin.to("cpu").squeeze().int().numpy(),
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)
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):
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table.add_data(
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i,
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wandb.Image(img),
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wandb.Image(mask),
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wandb.Image(
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pred,
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masks={
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"predictions": {
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"mask_data": pred_bin,
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"class_labels": class_labels,
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},
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},
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),
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)
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# log validation metrics
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wandb.log(
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{
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"predictions": table,
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"train/learning_rate": optimizer.state_dict()["param_groups"][0]["lr"],
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"val/accuracy": accuracy,
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"val/bce": val_loss,
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"val/dice": dice,
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"val/mae": mae,
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},
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),
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)
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commit=False,
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)
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# log validation metrics
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wandb.log(
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{
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"predictions": table,
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"train/learning_rate": optimizer.state_dict()["param_groups"][0]["lr"],
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"val/accuracy": accuracy,
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"val/bce": val_loss,
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"val/dice": dice,
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"val/mae": mae,
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},
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commit=False,
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)
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# update hyperparameters
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net.train()
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scheduler.step(dice)
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# update hyperparameters
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net.train()
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scheduler.step(dice)
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# save weights when epoch end
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torch.save(net.state_dict(), f"checkpoints/model-{epoch}.pth")
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artifact = wandb.Artifact("pth", type="model")
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artifact.add_file(f"checkpoints/model-{epoch}.pth")
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wandb.run.log_artifact(artifact)
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# export model to onnx format
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dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
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torch.onnx.export(net, dummy_input, f"checkpoints/model-{epoch}.onnx")
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artifact = wandb.Artifact("onnx", type="model")
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artifact.add_file(f"checkpoints/model-{epoch}.onnx")
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wandb.run.log_artifact(artifact)
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# export model to onnx format when validation ends
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dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
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torch.onnx.export(net, dummy_input, f"checkpoints/model-{epoch}-{step}.onnx")
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artifact = wandb.Artifact("onnx", type="model")
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artifact.add_file(f"checkpoints/model-{epoch}-{step}.onnx")
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wandb.run.log_artifact(artifact)
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# stop wandb
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wandb.run.finish()
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