LOGGING IN NNUNET?

[lamtaynha1234]

i am trying to figure out how to render the image of train model using nnunet to wandb or whatever. please help me.

[sten2lu]

Hi @lamtaynha1234,

To log and visualize model training images from nnU-Net using Weights and Biases (wandb), you need to integrate wandb with nnU-Net’s training workflow. Here’s a step-by-step guide to help you achieve this:

Steps for Integration:

1. Install wandb: First, ensure you have wandb installed. You can install it using pip:

   pip install wandb

2. Set up wandb: If you haven’t already, initialize wandb in your Python environment or in your script:

   
   import wandb

wandb.init(project="nnUNet_project", entity="your_wandb_username")

Replace "nnUNet_project" with the desired name of your project and "your_wandb_username" with your wandb username.

3.  Modify nnU-Net Training Script:
You’ll need to modify the nnU-Net training code to log images and metrics to wandb. 
- Locate the train() function within the desired training script.
- Inside this function, after each epoch (or at certain intervals), log training metrics and images to wandb.
Example of logging images and metrics:
```python
import numpy as np

# Assuming `output` is the predicted segmentation and `target` is the ground truth
# Convert numpy arrays to images, if necessary

for batch_idx, (output, target) in enumerate(validation_data_loader):
    wandb.log({
        "epoch": epoch,
        "input_image": wandb.Image(input_image),  # Your input image
        "predicted_segmentation": wandb.Image(np.argmax(output.cpu().numpy(), axis=1)[0]),  # First output of batch
        "ground_truth": wandb.Image(target[0].cpu().numpy())  # Ground truth image
    })

Ensure that the predicted segmentation output and ground truth are properly formatted for logging. You can use wandb.Image() to log images, and it will automatically display them in the wandb dashboard.

4. Running the Training: When you run the modified nnU-Net training script, wandb will automatically sync the images and metrics to your wandb project. Ensure that your project is properly linked to your wandb account, and you should be able to monitor the training process, including the images, on the wandb dashboard.

Bonus: Automatic Logging

If you want to automatically integrate wandb with the nnU-Net repository without modifying the source code too much, you can create a custom nnUNetTrainer class that inherits from the base trainer, overriding methods to include wandb logging.

This should allow you to render and log images of the training model using wandb with nnU-Net. You can adapt this workflow based on the specific part of the model’s training or validation output you want to visualize.

Best regards,

Carsten

[lamtaynha1234]

Hi @sten2lu, Thanks for taking the time to answer my question but I have a few more questions:

1. If I create a file like this, which folder will it be in or can it be in any folder?
2. And if I do that, do I need to connect the above code to your existing code? Thanks for your help

[lamtaynha1234]

One more, after i modify the nnU-Net training code, how can i run this code again.

[sten2lu]

Hi @lamtaynha1234,

1. you will need to create the new trainer in the nnunetv2/training/nnUNetTrainer folder.
2. You will need to implement the code yourself in the nnUNetTrainer.
3. To select your new nnUNetTrainer you will need to select it during training.

For the exact implementation I refer you to the documentation.

Best regards, Carsten