def seq_conv_train(config):
xy_train = Step(name='xy_train',
transformer=XYSplit(**config.xy_splitter),
input_data=['input'],
adapter={'meta': ([('input', 'meta')]),
'train_mode': ([('input', 'train_mode')])
},
cache_dirpath=config.env.cache_dirpath)
xy_inference = Step(name='xy_inference',
transformer=XYSplit(**config.xy_splitter),
input_data=['input'],
adapter={'meta': ([('input', 'meta_valid')]),
'train_mode': ([('input', 'train_mode')])
},
cache_dirpath=config.env.cache_dirpath)
loader_train = Step(name='loader',
transformer=MetadataImageSegmentationLoader(**config.loader),
input_data=['input'],
input_steps=[xy_train, xy_inference],
adapter={'X': ([('xy_train', 'X')], squeeze_inputs),
'y': ([('xy_train', 'y')], squeeze_inputs),
'train_mode': ([('input', 'train_mode')]),
'X_valid': ([('xy_inference', 'X')], squeeze_inputs),
'y_valid': ([('xy_inference', 'y')], squeeze_inputs),
},
cache_dirpath=config.env.cache_dirpath)
sequential_convnet = Step(name='sequential_convnet',
transformer=SequentialConvNet(**config.sequential_convnet),
input_steps=[loader_train],
cache_dirpath=config.env.cache_dirpath)
mask_resize = Step(name='mask_resize',
transformer=Resizer(),
input_data=['input'],
input_steps=[sequential_convnet],
adapter={'images': ([('sequential_convnet', 'predicted_masks')]),
'target_sizes': ([('input', 'target_sizes')]),
},
cache_dirpath=config.env.cache_dirpath)
thresholding = Step(name='thresholding',
transformer=Thresholder(**config.thresholder),
input_steps=[mask_resize],
adapter={'images': ([('mask_resize', 'resized_images')]),
},
cache_dirpath=config.env.cache_dirpath)
output = Step(name='output',
transformer=Dummy(),
input_steps=[thresholding],
adapter={'y_pred': ([('thresholding', 'binarized_images')]),
},
cache_dirpath=config.env.cache_dirpath)
return output
def unet_inference(config):
xy_inference = Step(name='xy_inference',
transformer=XYSplit(**config.xy_splitter),
input_data=['input'],
adapter={'meta': ([('input', 'meta')]),
'train_mode': ([('input', 'train_mode')])
},
cache_dirpath=config.env.cache_dirpath)
loader_inference = Step(name='loader',
transformer=MetadataImageSegmentationLoader(**config.loader),
input_data=['input'],
input_steps=[xy_inference, xy_inference],
adapter={'X': ([('xy_inference', 'X')], squeeze_inputs),
'y': ([('xy_inference', 'y')], squeeze_inputs),
'train_mode': ([('input', 'train_mode')]),
},
cache_dirpath=config.env.cache_dirpath)
unet_network = Step(name='unet_network',
transformer=PyTorchUNet(**config.unet_network),
input_steps=[loader_inference],
cache_dirpath=config.env.cache_dirpath)
mask_resize = Step(name='mask_resize',
transformer=Resizer(),
input_data=['input'],
input_steps=[unet_network],
adapter={'images': ([('unet_network', 'predicted_masks')]),
'target_sizes': ([('input', 'target_sizes')]),
},
cache_dirpath=config.env.cache_dirpath)
thresholding = Step(name='thresholding',
transformer=Thresholder(**config.thresholder),
input_steps=[mask_resize],
adapter={'images': ([('mask_resize', 'resized_images')]),
},
cache_dirpath=config.env.cache_dirpath)
output = Step(name='output',
transformer=Dummy(),
input_steps=[thresholding],
adapter={'y_pred': ([('thresholding', 'binarized_images')]),
},
cache_dirpath=config.env.cache_dirpath)
return output
def preprocessing_inference(config):
if config.execution.load_in_memory:
reader_inference = Step(
name='reader_inference',
transformer=ImageReader(**config.reader_single),
input_data=['input'],
adapter={
'meta': ([('input', 'meta')]),
'train_mode': ([('input', 'train_mode')]),
},
cache_dirpath=config.env.cache_dirpath)
loader = Step(name='loader',
transformer=ImageSegmentationLoader(**config.loader),
input_data=['input'],
input_steps=[reader_inference],
adapter={
'X': ([('reader_inference', 'X')]),
'y': ([('reader_inference', 'y')]),
'train_mode': ([('input', 'train_mode')]),
},
cache_dirpath=config.env.cache_dirpath)
else:
xy_inference = Step(name='xy_inference',
transformer=XYSplit(**config.xy_splitter),
input_data=['input'],
adapter={
'meta': ([('input', 'meta')]),
'train_mode': ([('input', 'train_mode')])
},
cache_dirpath=config.env.cache_dirpath)
loader = Step(
name='loader',
transformer=MetadataImageSegmentationLoader(**config.loader),
input_data=['input'],
input_steps=[xy_inference, xy_inference],
adapter={
'X': ([('xy_inference', 'X')], squeeze_inputs),
'y': ([('xy_inference', 'y')], squeeze_inputs),
'train_mode': ([('input', 'train_mode')]),
},
cache_dirpath=config.env.cache_dirpath)
return loader