def mask_and_label_conversion(self, semantic, loss_mask):
semantic = semantic.astype('int32')
label = class_mapping.index_to_label_vec_func(semantic)
loss_mask = (label != 12) * loss_mask
label[label == 12] = 0
return label, loss_mask
def mask_and_label_conversion(self, semantic, loss_mask):
labels = []
loss_masks = []
for i, s in enumerate(semantic):
sem = s.astype('int32')
label = class_mapping.index_to_label_vec_func(sem)
loss_mask_ = (label != 12) * loss_mask[i]
label[label == 12] = 0
labels.append(label)
loss_masks.append(loss_mask_)
return labels, loss_masks
import class_mapping
parser = argparse.ArgumentParser()
parser.add_argument('--data_root',
default='../processed_data',
help='Data dir [default: .]')
FLAGS = parser.parse_args()
data_root = FLAGS.data_root
npz_files = glob.glob(os.path.join(data_root, '*npz'))
labelweights = np.zeros(12, dtype='int64')
for j, n in enumerate(npz_files):
print(j)
data = np.load(os.path.join(data_root, n))
semantic = data['semantic']
label = class_mapping.index_to_label_vec_func(semantic)
mask = label != 12
for i in range(12):
labelweights[i] += np.sum((label == i) & mask)
labelweights = labelweights.astype(np.float128)
labelweights = labelweights / np.sum(labelweights)
np.savez_compressed(os.path.join('.', 'labelweights.npz'),
labelweights=labelweights)