def pointSIFT_group_four_with_idx(xyz, idx, points, use_xyz=True):
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, 8/32, 3)
# translation normalization
grouped_xyz -= tf.tile(tf.expand_dims(xyz, 2), [1, 1, 32, 1])
#if points is not None:
if not (len(points.shape) == 1):
# (batch_size, npoint, 8/32, channel)
grouped_points = group_point(points, idx)
if use_xyz:
# (batch_size, npoint, 8/32, 3+channel)
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return xyz, new_points, idx, grouped_xyz
def sample_and_group(npoint,
radius,
nsample,
xyz,
points,
knn=False,
use_xyz=True):
'''
Input:
npoint: int32
radius: float32
nsample: int32
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
knn: bool, if True use kNN instead of radius search
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Output:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
(subtracted by seed point XYZ) in local regions
'''
new_xyz = gather_point(xyz, farthest_point_sample(
npoint, xyz)) # (batch_size, npoint, 3)
if knn:
_, idx = knn_point(nsample, xyz, new_xyz)
else:
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2),
[1, 1, nsample, 1]) # translation normalization
#if points is not None:
if not (len(points.shape) == 1):
# (batch_size, npoint, nsample, channel)
grouped_points = group_point(points, idx)
if use_xyz:
# (batch_size, npoint, nample, 3+channel)
new_points = tf.concat([grouped_xyz, grouped_points], axis=-1)
else:
new_points = grouped_points
else:
new_points = grouped_xyz
return new_xyz, new_points, idx, grouped_xyz
def call(self, inputs, training=None, **kwargs):
xyz, points = inputs
if training is None:
training = tf.keras.backend.learning_phase()
# conv 1
_, new_points, idx, _ = pointSIFT_group(self.radius,
xyz,
points,
use_xyz=self.use_xyz)
for i in range(3):
new_points = self.conv2d(new_points,
self.out_channel, [1, 2],
self._name + '-c0_conv%d' % (i), [1, 2],
padding='VALID',
activation_fn='ReLU',
training=training)
new_points = tf.squeeze(new_points, [2])
# conv 2
#_, new_points, idx, _ = pointSIFT_group_with_idx(xyz,
# idx=idx,
# points=new_points,
#use_xyz=self.use_xyz)
new_points = group_point(new_points, idx)
for i in range(3):
if i == 2:
act = 'None'
else:
act = 'ReLU'
new_points = self.conv2d(new_points,
self.out_channel, [1, 2],
self._name + '-c1_conv%d' % (i), [1, 2],
padding='VALID',
activation_fn=act,
training=training)
new_points = tf.squeeze(new_points, [2])
# residual part
#if points is not None:
if not (len(points.shape) == 1):
if self.same_dim is True:
points = self.conv1d(points,
self.out_channel,
1,
self._name + '-merge_channel_fc',
1,
padding='VALID',
activation_fn='ReLU',
training=training)
if self.merge == 'add':
new_points = new_points + points
elif self.merge == 'concat':
new_points = tf.concat([new_points, points], axis=-1)
else:
print("way not found!!")
new_points = tf.nn.relu(new_points)
return xyz, new_points, idx