return inference_step(image, architecture=model, activation=torch.sigmoid)
# metrics
individual_metrics = {
'dice': lambda gt, pred: dice_score(gt.astype(bool), pred >= .5)
}
val_metrics = convert_to_aggregated(individual_metrics)
# run experiment
logger = TBLogger(EXPERIMENT_PATH / FOLD / 'logs')
save_json(CONFIG, EXPERIMENT_PATH / 'config.json')
train(train_step, batch_iter, n_epochs=CONFIG['n_epochs'], logger=logger,
validate=lambda : compute_metrics(predict, dataset.load_image, dataset.load_gt, val_ids, val_metrics),
architecture=model, optimizer=optimizer, criterion=criterion, lr=CONFIG['lr'])
save_model_state(model, EXPERIMENT_PATH / FOLD / 'model.pth')
commands.predict(
ids=test_ids,
output_path=EXPERIMENT_PATH / FOLD / 'predictions',
load_x=dataset.load_image,
predict_fn=predict
)
commands.evaluate_individual_metrics(
load_y_true=dataset.load_gt,
metrics=individual_metrics,
predictions_path=EXPERIMENT_PATH / FOLD / 'predictions',
results_path=EXPERIMENT_PATH / FOLD / 'metrics'
)
val_metrics = convert_to_aggregated(individual_metrics)
# run experiment
logger = TBLogger(EXPERIMENT_PATH / FOLD / 'logs')
commands.populate(EXPERIMENT_PATH / 'config.json', save_json, CONFIG, EXPERIMENT_PATH / 'config.json')
commands.populate(EXPERIMENT_PATH / FOLD / 'model.pth', lambda : [
train(train_step, batch_iter, n_epochs=CONFIG['n_epochs'], logger=logger,
validate=lambda : compute_metrics(predict, train_dataset.load_image,
lambda i: (train_dataset.load_gt(i), train_dataset.load_spacing(i)),
val_ids, val_metrics),
architecture=model, optimizer=optimizer, criterion=criterion, lr=CONFIG['lr']),
save_model_state(model, EXPERIMENT_PATH / FOLD / 'model.pth')
])
load_model_state(model, EXPERIMENT_PATH / FOLD / 'model.pth')
for target_slice_spacing in CONFIG['target_slice_spacing']:
test_dataset = ChangeSliceSpacing(dataset, new_slice_spacing=target_slice_spacing)
commands.predict(
ids=test_ids,
output_path=EXPERIMENT_PATH / FOLD / f"predictions_{CONFIG['source_slice_spacing']}_to_{target_slice_spacing}",
load_x=test_dataset.load_image,
predict_fn=predict
)
commands.evaluate_individual_metrics(
load_y_true=lambda i: (test_dataset.load_gt(i), test_dataset.load_spacing(i)),
metrics=individual_metrics,
predictions_path=EXPERIMENT_PATH / FOLD / f"predictions_{CONFIG['source_slice_spacing']}_to_{target_slice_spacing}",
results_path=EXPERIMENT_PATH / FOLD / f"metrics_{CONFIG['source_slice_spacing']}_to_{target_slice_spacing}"
)
train(crf_train_step,
batch_iter,
n_epochs=CONFIG['n_epochs'],
logger=logger,
validate=lambda:
compute_metrics(predict=predict,
load_x=lambda i:
(dataset.load_image(i), dataset.load_spacing(i)),
load_y=lambda i:
(dataset.load_gt(i), dataset.load_spacing(i)),
ids=val_ids,
metrics=val_metrics),
architecture=model,
optimizer=optimizer,
criterion=criterion,
lr=CONFIG['lr'],
with_crf=with_crf),
save_model_state(model, EXPERIMENT_PATH / FOLD / 'model.pth')
])
load_model_state(model, EXPERIMENT_PATH / FOLD / 'model.pth')
commands.predict(ids=test_ids,
output_path=EXPERIMENT_PATH / FOLD / f"predictions",
load_x=lambda i:
(dataset.load_image(i), dataset.load_spacing(i)),
predict_fn=predict)
commands.evaluate_individual_metrics(
load_y_true=lambda i: (dataset.load_gt(i), dataset.load_spacing(i)),
metrics=individual_metrics,
predictions_path=EXPERIMENT_PATH / FOLD / f"predictions",
results_path=EXPERIMENT_PATH / FOLD / f"metrics")