Created
December 18, 2020 05:31
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| callbacks = [ | |
| # Each criterion is calculated separately. | |
| CriterionCallback( | |
| input_key="mask", | |
| prefix="loss_dice", | |
| criterion_key="dice" | |
| ), | |
| CriterionCallback( | |
| input_key="mask", | |
| prefix="loss_bce", | |
| criterion_key="bce" | |
| ), | |
| # And only then we aggregate everything into one loss. | |
| MetricAggregationCallback( | |
| prefix="loss", | |
| mode="weighted_sum", | |
| metrics={ | |
| "loss_dice": 1.0, | |
| "loss_bce": 0.8 | |
| }, | |
| ), | |
| # metrics | |
| IoUMetricsCallback( | |
| mode='binary', | |
| input_key='mask', | |
| ) | |
| ] | |
| runner = dl.SupervisedRunner(input_key="features", input_target_key="mask") | |
| runner.train( | |
| model=model, | |
| criterion=criterion, | |
| optimizer=optimizer, | |
| scheduler=scheduler, | |
| loaders=loaders, | |
| callbacks=callbacks, | |
| logdir='../logs/xray_test_log', | |
| num_epochs=100, | |
| main_metric="loss", | |
| minimize_metric=True, | |
| verbose=True, | |
| ) |
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