def __init__(self, experiment_directory=None, config=None):
super(Maiden, self).__init__(experiment_directory)
# Privates
self._device = None
self._meta_config['exclude_attrs_from_save'] = [
'data_loader', '_device'
]
if config is not None: # TODO
self.read_config_file(config)
self.DEFAULT_DISPATCH = 'train'
self.auto_setup()
# register additional callbacks
self.register_run_info_callback()
self.register_reduce_dataset_callback()
self.trainer.register_callback(
SaveModelAtBestValidationScore(
to_directory=self.checkpoint_directory,
smoothness=0,
verbose=True))
if self.get('trainer/criterion/losses/fgbg') is not None:
self.build_fgbg_metric()
# register anywhere logger for scalars
register_logger(self, 'scalars')
def __init__(self, experiment_directory=None, config=None):
super(VaeCremiExperiment, self).__init__(experiment_directory)
# Privates
self._device = None
self._meta_config['exclude_attrs_from_save'] = [
'data_loader', '_device'
]
if config is not None:
self.read_config_file(config)
self.DEFAULT_DISPATCH = 'train'
self.auto_setup()
register_logger(self, 'scalars')
# register_logger(self, 'embedding')
# register_logger(self, 'image')
offsets = self.get_default_offsets()
self.set('global/offsets', offsets)
self.set(
'loaders/general/volume_config/segmentation/affinity_config/offsets',
offsets)
self.model_class = list(self.get('model').keys())[0]
self.set_devices()
def __init__(self, experiment_directory=None, config=None):
super(BaseCremiExperiment, self).__init__(experiment_directory)
# Privates
self._device = None
self._meta_config['exclude_attrs_from_save'] = [
'data_loader', '_device'
]
if config is not None:
self.read_config_file(config)
self.DEFAULT_DISPATCH = 'train'
self.auto_setup()
register_logger(self, 'scalars')
def __init__(self, experiment_directory=None, config=None):
super(BaseCremiExperiment, self).__init__(experiment_directory)
# Privates
self._device = None
self._meta_config['exclude_attrs_from_save'] = [
'data_loader', '_device'
]
if config is not None:
self.read_config_file(config)
self.DEFAULT_DISPATCH = 'train'
self.auto_setup()
# register_logger(FirelightLogger, "image")
register_logger(self, 'scalars')
# offsets = self.get_boundary_offsets()
# self.set('global/offsets', offsets)
# self.set('loaders/general/volume_config/segmentation/affinity_config/offsets', offsets)
if "model_class" in self.get('model'):
self.model_class = self.get('model/model_class')
else:
self.model_class = list(self.get('model').keys())[0]
if self.get("loaders/general/master_config/downscale_and_crop"
) is not None:
master_conf = self.get("loaders/general/master_config")
ds_config = self.get(
"loaders/general/master_config/downscale_and_crop")
nb_tensors = len(ds_config)
nb_inputs = self.get("model/model_kwargs/number_multiscale_inputs")
nb_targets = nb_tensors - nb_inputs
if "affinity_config" in master_conf:
affs_config = deepcopy(master_conf.get("affinity_config", {}))
if affs_config.get("use_dynamic_offsets", False):
raise NotImplementedError
nb_targets = 1
else:
affs_config.pop("global", None)
nb_targets = len(affs_config)
self.set("trainer/num_targets", nb_targets)
else:
self.set("trainer/num_targets", 1)
self.set_devices()