def network(config, suffix='', train_mode=True):
if train_mode:
preprocessing = emptiness_preprocessing_train(config, model_name='network', suffix=suffix)
else:
preprocessing = emptiness_preprocessing_inference(config, suffix=suffix)
network = utils.FineTuneStep(name='network{}'.format(suffix),
transformer=models.SegmentationModel(**config.model['network']),
input_data=['callback_input'],
input_steps=[preprocessing],
adapter=Adapter({'datagen': E(preprocessing.name, 'datagen'),
'validation_datagen': E(preprocessing.name, 'validation_datagen'),
'meta_valid': E('callback_input', 'meta_valid'),
}),
is_trainable=True,
fine_tuning=config.model.network.training_config.fine_tuning,
experiment_directory=config.execution.experiment_dir)
mask_resize = Step(name='mask_resize{}'.format(suffix),
transformer=utils.make_apply_transformer(partial(postprocessing.resize_emptiness_predictions,
target_size=config.general.original_size),
output_name='resized_images',
apply_on=['images']),
input_steps=[network],
adapter=Adapter({'images': E(network.name, 'mask_prediction'),
}),
experiment_directory=config.execution.experiment_dir)
return mask_resize
def unet_tta(config, suffix=''):
preprocessing, tta_generator = pipelines.preprocessing_inference_tta(
config, model_name='unet')
unet = Step(name='unet{}'.format(suffix),
transformer=models.PyTorchUNet(**config.model['unet']),
input_data=['callback_input'],
input_steps=[preprocessing],
is_trainable=True,
experiment_directory=config.execution.experiment_dir)
tta_aggregator = pipelines.aggregator(
'tta_aggregator{}'.format(suffix),
unet,
tta_generator=tta_generator,
experiment_directory=config.execution.experiment_dir,
config=config.tta_aggregator)
prediction_renamed = Step(
name='prediction_renamed{}'.format(suffix),
transformer=IdentityOperation(),
input_steps=[tta_aggregator],
adapter=Adapter({
'mask_prediction':
E(tta_aggregator.name, 'aggregated_prediction')
}),
experiment_directory=config.execution.experiment_dir)
if config.general.loader_mode == 'resize_and_pad':
size_adjustment_function = partial(
postprocessing.crop_image,
target_size=config.general.original_size)
elif config.general.loader_mode == 'resize':
size_adjustment_function = partial(
postprocessing.resize_image,
target_size=config.general.original_size)
else:
raise NotImplementedError
mask_resize = Step(name='mask_resize{}'.format(suffix),
transformer=utils.make_apply_transformer(
size_adjustment_function,
output_name='resized_images',
apply_on=['images']),
input_steps=[prediction_renamed],
adapter=Adapter({
'images':
E(prediction_renamed.name, 'mask_prediction'),
}),
experiment_directory=config.execution.experiment_dir)
return mask_resize
def unet(config, suffix='', train_mode=True):
if train_mode:
preprocessing = pipelines.preprocessing_train(config,
model_name='unet',
suffix=suffix)
else:
preprocessing = pipelines.preprocessing_inference(config,
suffix=suffix)
unet = utils.FineTuneStep(
name='unet{}'.format(suffix),
transformer=models.PyTorchUNet(**config.model['unet']),
input_data=['callback_input'],
input_steps=[preprocessing],
adapter=Adapter({
'datagen':
E(preprocessing.name, 'datagen'),
'validation_datagen':
E(preprocessing.name, 'validation_datagen'),
'meta_valid':
E('callback_input', 'meta_valid'),
}),
is_trainable=True,
fine_tuning=config.model.unet.training_config.fine_tuning,
experiment_directory=config.execution.experiment_dir)
if config.general.loader_mode == 'resize_and_pad':
size_adjustment_function = partial(
postprocessing.crop_image,
target_size=config.general.original_size)
elif config.general.loader_mode == 'resize':
size_adjustment_function = partial(
postprocessing.resize_image,
target_size=config.general.original_size)
else:
raise NotImplementedError
mask_resize = Step(name='mask_resize{}'.format(suffix),
transformer=utils.make_apply_transformer(
size_adjustment_function,
output_name='resized_images',
apply_on=['images']),
input_steps=[unet],
adapter=Adapter({
'images':
E(unet.name, 'mask_prediction'),
}),
experiment_directory=config.execution.experiment_dir)
return mask_resize
def network_tta(config, suffix=''):
if SECOND_LEVEL:
raise NotImplementedError('Second level does not work with TTA')
preprocessing, tta_generator = pipelines.preprocessing_inference_tta(config, model_name='network')
if USE_DEPTH:
Network = models.SegmentationModelWithDepth
else:
Network = models.SegmentationModel
network = Step(name='network{}'.format(suffix),
transformer=Network(**config.model['network']),
input_data=['callback_input'],
input_steps=[preprocessing],
is_trainable=True,
experiment_directory=config.execution.experiment_dir)
tta_aggregator = pipelines.aggregator('tta_aggregator{}'.format(suffix), network,
tta_generator=tta_generator,
experiment_directory=config.execution.experiment_dir,
config=config.tta_aggregator)
prediction_renamed = Step(name='prediction_renamed{}'.format(suffix),
transformer=IdentityOperation(),
input_steps=[tta_aggregator],
adapter=Adapter({'mask_prediction': E(tta_aggregator.name, 'aggregated_prediction')
}),
experiment_directory=config.execution.experiment_dir)
if config.general.loader_mode == 'resize_and_pad':
size_adjustment_function = partial(postprocessing.crop_image, target_size=config.general.original_size)
elif config.general.loader_mode == 'resize' or config.general.loader_mode == 'stacking':
size_adjustment_function = partial(postprocessing.resize_image, target_size=config.general.original_size)
else:
raise NotImplementedError
mask_resize = Step(name='mask_resize{}'.format(suffix),
transformer=utils.make_apply_transformer(size_adjustment_function,
output_name='resized_images',
apply_on=['images']),
input_steps=[prediction_renamed],
adapter=Adapter({'images': E(prediction_renamed.name, 'mask_prediction'),
}),
experiment_directory=config.execution.experiment_dir)
return mask_resize
def network(config, suffix='', train_mode=True):
if SECOND_LEVEL:
preprocessing_train = stacking_preprocessing_train
preprocessing_inference = stacking_preprocessing_inference
else:
preprocessing_train = pipelines.preprocessing_train
preprocessing_inference = pipelines.preprocessing_inference
if train_mode:
preprocessing = preprocessing_train(config, model_name='network', suffix=suffix)
else:
preprocessing = preprocessing_inference(config, suffix=suffix)
if USE_DEPTH:
Network = models.SegmentationModelWithDepth
else:
Network = models.SegmentationModel
network = utils.FineTuneStep(name='network{}'.format(suffix),
transformer=Network(**config.model['network']),
input_data=['callback_input'],
input_steps=[preprocessing],
adapter=Adapter({'datagen': E(preprocessing.name, 'datagen'),
'validation_datagen': E(preprocessing.name, 'validation_datagen'),
'meta_valid': E('callback_input', 'meta_valid'),
}),
is_trainable=True,
fine_tuning=config.model.network.training_config.fine_tuning,
experiment_directory=config.execution.experiment_dir)
if config.general.loader_mode == 'resize_and_pad':
size_adjustment_function = partial(postprocessing.crop_image, target_size=config.general.original_size)
elif config.general.loader_mode == 'resize' or config.general.loader_mode == 'stacking':
size_adjustment_function = partial(postprocessing.resize_image, target_size=config.general.original_size)
else:
raise NotImplementedError
mask_resize = Step(name='mask_resize{}'.format(suffix),
transformer=utils.make_apply_transformer(size_adjustment_function,
output_name='resized_images',
apply_on=['images']),
input_steps=[network],
adapter=Adapter({'images': E(network.name, 'mask_prediction'),
}),
experiment_directory=config.execution.experiment_dir)
return mask_resize
