Modes

from wholeslidedata.data.mode import WholeSlideMode
from wholeslidedata.iterators.batchiterator import create_batch_iterator

list(WholeSlideMode)

[<WholeSlideMode.default: 1>,
 <WholeSlideMode.training: 2>,
 <WholeSlideMode.validation: 3>,
 <WholeSlideMode.test: 4>,
 <WholeSlideMode.inference: 5>]

Mode specific settings in user config

---

Using different modes allow for specific settings. For example for training mode and validation mode you can set different batch sizes in the config file

See also

Checkout the Dicfg documentation about context keys

user_config = './configs/user_config_modes.yml'
!cat {user_config}

wholeslidedata:
    default:
        yaml_source:
            training:
                - wsi: 
                    path: /tmp/TCGA-21-5784-01Z-00-DX1.tif
                  wsa: 
                    path: /tmp/TCGA-21-5784-01Z-00-DX1.xml       

            validation:
                - wsi: 
                    path: /tmp/TCGA-21-5784-01Z-00-DX1.tif
                  wsa: 
                    path: /tmp/TCGA-21-5784-01Z-00-DX1.xml   

        labels:
            stroma: 1
            tumor: 2
            lymphocytes: 3
            
        batch_shape:
            batch_size: 4
            spacing: 1.0
            shape: [512, 512, 3]
            
            
    validation:
        batch_shape:
            batch_size: 8

training_iterator = create_batch_iterator(mode='training', user_config=user_config)
validation_iterator = create_batch_iterator(mode='validation', user_config=user_config)
print('Batch size training: ', training_iterator.batch_size)
print('Batch size validation: ', validation_iterator.batch_size)
training_iterator.stop()
validation_iterator.stop()

Batch size training:  4
Batch size validation:  8