# 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(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:
# metrics
individual_metrics = {
**{f'dice_{c}': multiclass_to_bool(drop_spacing(dice_score), c) for c in range(1, dataset.n_classes)},
**{f'surface_dice_{c}': multiclass_to_bool(surface_dice, c) for c in range(1, dataset.n_classes)}
}
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(
# for more details about the batch iterators we use
batch_iter = Infinite(
# get a random pair of paths
sample(paths),
# load the image-contour pair
unpack_args(load_pair),
# get a random slice
unpack_args(get_random_slice),
# simple augmentation
unpack_args(random_flip),
batch_size=batch_size,
batches_per_epoch=samples_per_train // (batch_size * n_epochs),
combiner=combiner)
model = to_device(Network(), args.device)
optimizer = Adam(model.parameters(), lr=lr)
# Here we use a general training function with a custom `train_step`.
# See the tutorial for more details: https://deep-pipe.readthedocs.io/en/latest/tutorials/training.html
train(
train_step,
batch_iter,
n_epochs=n_epochs,
logger=ConsoleLogger(),
# additional arguments to `train_step`
model=model,
optimizer=optimizer)
save_model_state(model, args.output)