remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
remap_lut[list(class_remap.keys())] = list(class_remap.values())
# print(remap_lut)
# create evaluator
ignore = []
for cl, ign in class_ignore.items():
if ign:
x_cl = int(cl)
ignore.append(x_cl)
print("Ignoring xentropy class ", x_cl, " in IoU evaluation")
# create evaluator
if FLAGS.backend == "torch":
from auxiliary.torch_ioueval import iouEval
evaluator = iouEval(nr_classes, ignore)
if FLAGS.backend == "numpy":
from auxiliary.np_ioueval import iouEval
evaluator = iouEval(nr_classes, ignore)
else:
print("Backend for evaluator should be one of ", str(backends))
quit()
evaluator.reset()
# get test set
test_sequences = DATA["split"][FLAGS.split]
# get label paths
label_names = []
for sequence in test_sequences:
sequence = '{0:02d}'.format(int(sequence))
# print(remap_lut)
# create evaluator
ignore = []
for cl, ign in class_ignore.items():
if ign:
x_cl = int(cl)
ignore.append(x_cl)
print("Ignoring xentropy class ", x_cl, " in IoU evaluation")
# create evaluator
evaluators = []
for i in range(len(DISTANCES)):
if FLAGS.backend == "torch":
from auxiliary.torch_ioueval import iouEval
evaluators.append(iouEval(nr_classes, ignore))
evaluators[i].reset()
elif FLAGS.backend == "numpy":
from auxiliary.np_ioueval import iouEval
evaluators.append(iouEval(nr_classes, ignore))
evaluators[i].reset()
else:
print("Backend for evaluator should be one of ", str(backends))
quit()
# get test set
test_sequences = DATA["split"][FLAGS.split]
# get scan paths
scan_names = []
for sequence in test_sequences: