def unet(config, train_mode):
if train_mode:
save_output = False
load_saved_output = False
else:
save_output = False
load_saved_output = False
loader = preprocessing(config, model_type='single', is_train=train_mode)
unet = Step(name='unet',
transformer=PyTorchUNet(**config.unet),
input_steps=[loader],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=load_saved_output)
mask_postprocessed = mask_postprocessing(unet,
config,
save_output=save_output)
detached = multiclass_object_labeler(mask_postprocessed,
config,
save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[detached],
adapter={
'y_pred': ([(detached.name, 'labeled_images')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=False)
return output
def unet(config, train_mode):
if train_mode:
save_output = True
load_saved_output = False
preprocessing = preprocessing_train(config)
else:
save_output = True
load_saved_output = False
preprocessing = preprocessing_inference(config)
unet = Step(name='unet',
transformer=PyTorchUNet(**config.unet),
input_steps=[preprocessing],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=load_saved_output)
mask_postprocessed = mask_postprocessing(unet,
config,
save_output=save_output)
detached = nuclei_labeler(mask_postprocessed,
config,
save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[detached],
adapter={
'y_pred': ([(detached.name, 'labels')]),
},
cache_dirpath=config.env.cache_dirpath)
return output
def unet(config, train_mode):
save_output = False
load_saved_output = False
loader = preprocessing_generator(config, is_train=train_mode)
unet = Step(name='unet',
transformer=PyTorchUNetStream(**config.unet) if
config.execution.stream_mode else PyTorchUNet(**config.unet),
input_data=['callback_input'],
input_steps=[loader],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=load_saved_output)
mask_postprocessed = mask_postprocessing(loader,
unet,
config,
save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[mask_postprocessed],
adapter={
'y_pred':
([(mask_postprocessed.name, 'images_with_scores')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=False)
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 unet(config, train_mode):
if train_mode:
save_output = False
load_saved_output = False
else:
save_output = False
load_saved_output = False
loader = preprocessing(config, model_type='single', is_train=train_mode)
unet = Step(name='unet',
transformer=PyTorchUNetStream(**config.unet) if
config.execution.stream_mode else PyTorchUNet(**config.unet),
input_steps=[loader],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=load_saved_output)
mask_postprocessed = mask_postprocessing(unet,
config,
save_output=save_output)
if config.postprocessor["dilate_selem_size"] > 0:
mask_postprocessed = Step(
name='mask_dilation',
transformer=MaskDilatorStream(**config.postprocessor)
if config.execution.stream_mode else MaskDilator(
**config.postprocessor),
input_steps=[mask_postprocessed],
adapter={
'images': ([(mask_postprocessed.name, 'categorized_images')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=False)
detached = multiclass_object_labeler(mask_postprocessed,
config,
save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[detached],
adapter={
'y_pred': ([(detached.name, 'labeled_images')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=False)
return output
def unet(config, train_mode):
if train_mode:
save_output = False
load_saved_output = False
else:
save_output = False
load_saved_output = False
loader = preprocessing(config, model_type='single', is_train=train_mode)
unet = Step(name='unet',
transformer=PyTorchUNetStream(**config.unet) if config.execution.stream_mode else PyTorchUNet(
**config.unet),
input_steps=[loader],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output, load_saved_output=load_saved_output)
mask_postprocessed = mask_postprocessing(loader, unet, config, save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[mask_postprocessed],
adapter={'y_pred': ([(mask_postprocessed.name, 'images')]),
'y_scores': ([(mask_postprocessed.name, 'scores')])
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output,
load_saved_output=False)
return output
def unet_padded_tta(config):
save_output = False
if config.execution.stream_mode:
raise NotImplementedError('TTA not available in the stream mode')
loader, tta_generator = preprocessing_generator_padded_tta(config)
unet = Step(name='unet',
transformer=PyTorchUNet(**config.unet),
input_steps=[loader],
cache_dirpath=config.env.cache_dirpath,
save_output=save_output)
tta_aggregator = Step(name='tta_aggregator',
transformer=loaders.TestTimeAugmentationAggregator(),
input_steps=[unet, tta_generator],
adapter={
'images':
([(unet.name, 'multichannel_map_prediction')]),
'tta_params':
([(tta_generator.name, 'tta_params')]),
'img_ids': ([(tta_generator.name, 'img_ids')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output)
prediction_crop = Step(name='prediction_crop',
transformer=post.PredictionCrop(
**config.postprocessor.prediction_crop),
input_steps=[tta_aggregator],
adapter={
'images': ([(tta_aggregator.name,
'aggregated_prediction')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output)
prediction_renamed = Step(name='prediction_renamed',
transformer=Dummy(),
input_steps=[prediction_crop],
adapter={
'multichannel_map_prediction':
([(prediction_crop.name, 'cropped_images')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output)
mask_postprocessed = mask_postprocessing(loader,
prediction_renamed,
config,
save_output=save_output)
output = Step(name='output',
transformer=Dummy(),
input_steps=[mask_postprocessed],
adapter={
'y_pred':
([(mask_postprocessed.name, 'images_with_scores')]),
},
cache_dirpath=config.env.cache_dirpath,
save_output=save_output)
return output