def __init__(self,
patients,
transform=[None, None],
feature=None,
cores=None,
p=1,
task=None,
process=False):
if task is None:
task = Task()
task.start(name='IDC Dataset', desc='Loading Images...')
if transform is None:
transform = [None, None]
if process:
self.data, self.classes = task.pool(
proc_wrapper,
patients,
process=True,
shared_args=[transform[0], transform[1], p, feature],
shared_init=pinit,
name='Loader',
recursive=True,
reducer=proc_reducer,
cores=cores)
else:
self.data, self.classes = task.pool(load_patient,
patients,
process=False,
shared_kwargs={
'transform': transform,
'feature': feature,
'p': p
},
reducer=reducer,
name='Loader',
recursive=False,
threads=cores)
task.done(self.data,
self.classes,
desc="{n} images ({p}%) sampled from {k} patients".format(
n=self.classes.shape[0], k=len(patients), p=p * 100))
def __new(self, task, cores):
if task is None:
task = Task()
task.start(name='Random Binning Feature', desc=self.__str__())
gen = task.pool(get_p_set, [self.d for _ in range(self.D)],
cores=cores,
process=True,
name='Random Binning Feature')
self.delta = np.array([x[0] for x in gen], dtype=np.float32)
self.mu = np.array([x[1] for x in gen], dtype=np.float32)
task.done(self.delta,
self.mu,
desc="{desc} created".format(desc=self.__str__()))