NaN values

[saisusmitha]

@JuliaWolleb

1. Getting nan values in very early stages of training why is that? what might be going wrong? Kindly let me know how to solve the nan issue.

---

| grad_norm | nan |
| loss | nan |
| loss_q0 | nan |
| loss_q1 | nan |
| loss_q2 | nan |
| loss_q3 | nan |
| mse | nan |
| mse_q0 | nan |
| mse_q1 | nan |
| mse_q2 | nan |
| mse_q3 | nan |
| param_norm | nan |
| samples | 1.25e+05 |
| step | 2.09e+04 |
| vb | nan |
| vb_q0 | nan |
| vb_q1 | nan |
| vb_q2 | nan |
| vb_q3 | nan |

[guanzhenghua]

It seems that you have run this code.
Do you use your own data set or brast2020? What is your result like? Can you attach pictures.

[saisusmitha]

@guanzhenghua I have run on brats dataset and one other dataset- but for both I didn't get good output segmentation - I didn't figure out exactly why.

For brats - order of images [ GT, 4 I/p Images]

These are output samples

For another dataset - lungs dataset - The output is -

I don't know exactly why I am getting extra dots around my segmentation. If you have any idea why let me know.
@guanzhenghua @JuliaWolleb any idea where I am going wrong?

[guanzhenghua]

@saisusmitha
You preprocessed the final five resulting images and it didn't feel right. Because you just binarized it. Only the mask needs to be binarized, and we don't need to do additional binarization of the mask because the author's code includes this step.

So we just need to slice the four original images one mask input. for example.





But my training result is not good. If you get a good result, can you reply me.
thanks a lot.

[saisusmitha]

@guanzhenghua I think yes, the output segmentations i.e samples include input images too. @JuliaWolleb Can you let us know if this is the case - and if not what can be done?