def get_point_info(points, mask_ratio=0, mask=-1):
with tf.name_scope('points_info'):
pts = points_property(points, property_name='xyz', channel=4)
label = points_property(points, property_name='label', channel=1)
label = tf.reshape(label, [-1])
label_mask = label > mask # mask out invalid points, -1
if mask_ratio > 0: # random drop some points to speed up training
rnd_mask = tf.random.uniform(tf.shape(label_mask)) > mask_ratio
label_mask = tf.logical_and(label_mask, rnd_mask)
pts = tf.boolean_mask(pts, label_mask)
label = tf.boolean_mask(label, label_mask)
return pts, label
def __call__(self, dataset='train', training=True, reuse=False, gpu_num=1):
debug_checks = {}
FLAGS = self.flags
with tf.device('/cpu:0'):
flags_data = FLAGS.DATA.train if dataset == 'train' else FLAGS.DATA.test
data_iter = self.create_dataset(flags_data)
tower_tensors = []
for i in range(gpu_num):
with tf.device('/gpu:%d' % i):
with tf.name_scope('device_%d' % i):
octree, _labels, points = data_iter.get_next()
debug_checks["{}/input_octree".format(dataset)] = octree
debug_checks["{}/input_points".format(dataset)] = points
debug_checks["{}/input_labels".format(dataset)] = _labels
pts, label = get_point_info(points, flags_data.mask_ratio)
print("mask ratio for {} is {}".format(
dataset, flags_data.mask_ratio))
debug_checks["{}/input_point_info/points".format(
dataset)] = pts
debug_checks["{}/input_point_info/labels".format(
dataset)] = label
debug_checks["{}/input_point_info/normals".format(
dataset)] = points_property(points,
property_name='normal',
channel=3)
if not FLAGS.LOSS.point_wise:
pts, label = None, get_seg_label(
octree, FLAGS.MODEL.depth_out)
debug_checks["{}/input_seg_label/points"] = pts
debug_checks["{}/input_seg_label/label"] = label
logit = seg_network(octree,
FLAGS.MODEL,
training,
reuse,
pts=pts)
debug_checks["{}/logit".format(dataset)] = logit
losses, dc = loss_functions_seg_debug_checks(
logit,
label,
FLAGS.LOSS.num_class,
FLAGS.LOSS.weight_decay,
'ocnn',
mask=0)
debug_checks.update(dc)
tensors = losses + [losses[0] + losses[2]] # total loss
names = ['loss', 'accu', 'regularizer', 'total_loss']
if flags_data.batch_size == 1:
num_class = FLAGS.LOSS.num_class
intsc, union = tf_IoU_per_shape(logit,
label,
num_class,
mask=0)
iou = tf.constant(
0.0) # placeholder, calc its value later
tensors = [iou] + tensors + intsc + union
names = ['iou'] + names + \
['intsc_%d' % i for i in range(num_class)] + \
['union_%d' % i for i in range(num_class)]
tower_tensors.append(tensors)
reuse = True
tensors = tower_tensors[0] if gpu_num == 1 else list(
zip(*tower_tensors))
return tensors, names, debug_checks