with_crf = Switch(False, epoch_to_value={100: True})
# predict
@unpack_args
@add_extract_dims(1, 2)
def predict(image, spacing):
return inference_step(image,
spacing,
architecture=model,
activation=torch.sigmoid)
# metrics
individual_metrics = {
'dice': binary_to_bool(drop_spacing(dice_score)),
'surface_dice': binary_to_bool(surface_dice),
}
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(crf_train_step,
batch_iter,
n_epochs=CONFIG['n_epochs'],
logger=logger,
validate=lambda:
).to(CONFIG['device'])
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG['lr'])
# predict
@patches_grid(patch_size=PATCH_SIZE, stride=7 * PATCH_SIZE // 8)
@add_extract_dims(1)
def predict(image):
return inference_step(image, architecture=model, activation=nn.Softmax(1))
# 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')
# predict
@unpack_args
@add_extract_dims(1)
def predict(image, spacing):
return inference_step(image,
spacing,
architecture=model,
activation=nn.Softmax(1))
# 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: [