Algorithms/Segment/Training not work

[xuchao-25824839]

Expected Behavior

Traning done then output model

Current Behavior

1. [\d+].xml is no valid DICOM
2. ValueError: could not broadcast input array from shape (332,360,360) into shape (512,512,512)

Possible Solution

Steps to Reproduce

1. export COMPOSE_FILE=local.yml && docker-compose up
2. docker exec -it concept-to-clinic_prediction_1 bash
3. python

import matplotlib
matplotlib.use('Agg')
from src.algorithms.segment.src.data_generation import prepare_training_data
prepare_training_data()
from src.algorithms.segment.src.training import train
train()
output
ERROR:root:69.xml is no valid DICOM
Traceback (most recent call last):
File "", line 1, in
File "/app/src/algorithms/segment/src/training.py", line 78, in train
:CUBOID_IMAGE_SHAPE[2]]
ValueError: could not broadcast input array from shape (332,360,360) into shape (512,512,512)

Context (Environment)

Detailed Description

FIY concept-to-clinic/docs/design-doc.html

Training

In order to train the segmentation model, the following steps are necessary:

1. Download the LIDC dataset.
2. Put it in tests/assets/test_image_data/full optionally using a symbolic link
3. pylidc wraps the LIDC dataset and provides information
   such as annotated nodules, visualization methods etc. You must create a .pylidrc file that specifies
   the path to your local LIDC dataset like so
4. Run prepare_training_data
   to generate the binary segmentation masks in prediction/src/algorithms/segment/assets.
5. Finally, to train the model run:

   from src.algorithms.segment.src.training import train
   train()

Possible Implementation

Checklist before submitting

• I have confirmed this using the officially supported Docker Compose setup using the local.yml configuration and ensured that I built the containers again and they reflect the most recent version of the project at the HEAD commit on the master branch
• I have searched through the other currently open issues and am confident this is not a duplicate of an existing bug
• I provided a minimal code snippet or list of steps that reproduces the bug.
• I provided screenshots where appropriate
• I filled out all the relevant sections of this template