aimagelab-zip / alveolar_canal Goto Github PK

Hi,

I was trying to test your network and your wirghts on another database. But It fails each time...
Nevertheles, the size of each data (364,704,704) is bigger than the one you provided. Do you have any idea why it fails each time ?.

You will find attached one exemple of the database that I am trying to work with. Thank you

test

Best Regards,
Hamid FSIAN

There is no canal inference in this repo, so i can't test pre-trained checkpoint
How can I test pre-trained model from whole volume data

I am trying to run this code by using maxillo dataset. But it occurs a error below .

Traceback (most recent call last):
File "/home/syu/Documents/maison/alveolar_canal/main.py", line 170, in
val_iou, val_dice = experiment.test(phase="Validation")
File "/home/syu/Documents/maison/alveolar_canal/experiments/experiment.py", line 276, in test
loss = self.loss(output.unsqueeze(0), gt.unsqueeze(0), partition_weights)
File "/home/syu/Documents/maison/alveolar_canal/losses/LossFactory.py", line 60, in call
raise ValueError(f'Loss {loss_name} has some NaN')
ValueError: Loss DiceLoss has some NaN

I'm wondering if this code and dataset work as written on the readme or if there are no corrupted files in this dataset.
Big congratulation ti your acivements in cvpr!

Sorry for bothering you.I get NaN errors during canal_pretrain process.

From another issue https://github.com/AImageLab-zip/alveolar_canal/issues/7
I find that my prediction will get NaN during training.Could you help me to find the problem.

I wonder what is the gt that is used to train the Deep Label Expansion stage?From my observation,it is seemd that labels generated from torchio.LabelMap are used.Thanks very much!

As stated in potpov/alveolar_canal#3 the checkpoints for the baseline network (PosPadUNet3D) seems broken and must be fixed.

Hello, I'm trying to run inference by executing the main.py with a configuration file based on the gen-inference-unet in the configs folder, I'm pointing my dataset to a folder with the images I want to segment in .npy format. I get the following error:

INFO:root:loading preprocessing
Traceback (most recent call last):
  File "/home/renan/anaconda3/envs/alveolar_canal/lib/python3.9/site-packages/munch/__init__.py", line 103, in __getattr__
    return object.__getattribute__(self, k)
AttributeError: 'Munch' object has no attribute 'preprocessing'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/renan/anaconda3/envs/alveolar_canal/lib/python3.9/site-packages/munch/__init__.py", line 106, in __getattr__
    return self[k]
KeyError: 'preprocessing'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/renan/alveolar_canal/main.py", line 92, in <module>
    if config.data_loader.preprocessing is None:
  File "/home/renan/anaconda3/envs/alveolar_canal/lib/python3.9/site-packages/munch/__init__.py", line 108, in __getattr__
    raise AttributeError(k)
AttributeError: preprocessing

looking at the code there seems to be a section which should handle none preprocessing fields, but it doesn't seem to be working, here is my yml:

# title of the experiment
title: canal_generator_train
# Where to output everything, in this path a folder with
# the same name as the title is created containing checkpoints,
# logs and a copy of the config used
project_dir: './results'
seed: 47

# which experiment to execute: Segmentation or Generation
experiment:
  name: Segmentation

data_loader:
  dataset: ./data/MG_scan_test.nii.gz
  # null to use training_set, generated to used the generated dataset
  training_set: null
  # which augmentations to use, see: augmentations.yaml
  augmentations: configs/augmentations.yaml
  background_suppression: 0
  batch_size: 2
  labels:
    BACKGROUND: 0
    INSIDE: 1
  mean: 0.08435
  num_workers: 8
  # shape of a single patch
  patch_shape:
  - 120
  - 120
  - 120
  # reshape of the whole volume before extracting the patches
  resize_shape:
  - 168
  - 280
  - 360
  sampler_type: grid
  grid_overlap: 0
  std: 0.17885
  volumes_max: 2100
  volumes_min: 0
  weights:
  - 0.000703
  - 0.999

# which network to use
model:
  name: PosPadUNet3D

loss:
  name: Jaccard

lr_scheduler:
  name: Plateau

optimizer:
  learning_rate: 0.1
  name: SGD

trainer:
  # Reload the last checkpoints?
  reload: False
  checkpoint: ./checkpoints/seg-checkpoint.pth
  # train the network
  do_train: False
  # do a single test of the network with the loaded checkpoints
  do_test: False
  # generate the synthetic dense dataset
  do_inference: True
  epochs: 100

Any help is appreciated, thanks.

Hello author! I have an error in code reproduction that I would like to get your answer to. When I run the command python main.py --configs config/seg-pretraining.yaml, I get a divide by zero error, the error comes from expierments line 204 running continue operation on the whole dataset, what causes this? Because torch.sum(gt_count) is always equal to 0, and does synthetic_loader only use dense tags generated by gen-inference.yaml? Looking for your answer to the above error when trying to reproduce your work.