def get_model(point_cloud,
is_training,
num_class,
global_pl,
params,
weight_decay=None,
bn_decay=None,
scname=''):
''' input: BxNxF
Use https://arxiv.org/pdf/1902.08570 as baseline
output:BxNx(cats*segms) '''
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
input_image = tf.expand_dims(point_cloud, -2)
k = params[0]
#adj = tf_util.pairwise_distance(point_cloud[:,:,:3])
adj = tf_util.pairwise_distanceR(point_cloud[:, :, :3])
n_heads = params[1]
nn_idx = tf_util.knn(adj, k=k)
net, locals_transform, coefs = gap_block(k, n_heads, nn_idx, point_cloud,
point_cloud,
('filter0', params[2]), bn_decay,
weight_decay, is_training, scname)
net = tf_util.conv2d(net,
params[3], [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet00',
bn_decay=bn_decay)
net00 = net
net = tf_util.conv2d(net,
params[4], [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet01' + scname,
bn_decay=bn_decay)
net01 = net
net = tf_util.conv2d(net,
params[5], [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet02' + scname,
bn_decay=bn_decay)
net02 = net
adj_matrix = tf_util.pairwise_distance(net)
nn_idx = tf_util.knn(adj_matrix, k=k)
adj_conv = nn_idx
n_heads = params[6]
net, locals_transform1, coefs2 = gap_block(k, n_heads, nn_idx, net,
point_cloud,
('filter1', params[7]),
bn_decay, weight_decay,
is_training, scname)
net = tf_util.conv2d(net,
params[8], [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet10',
bn_decay=bn_decay)
net10 = net
net = tf_util.conv2d(net,
params[9], [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet11' + scname,
bn_decay=bn_decay)
net11 = net
net = tf_util.conv2d(net,
params[10], [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet12' + scname,
bn_decay=bn_decay)
net12 = net
global_expand = tf.reshape(global_pl, [batch_size, 1, 1, -1])
global_expand = tf.tile(global_expand, [1, num_point, 1, 1])
global_expand = tf_util.conv2d(global_expand,
16, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='global_expand' + scname,
bn_decay=bn_decay)
net = tf.concat([
net00, net01, net02, net11, net12, global_expand, locals_transform,
locals_transform1
],
axis=-1)
net = tf_util.conv2d(net,
params[8], [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='agg' + scname,
bn_decay=bn_decay)
#net_tot = net
net = tf_util.avg_pool2d(net, [num_point, 1],
padding='VALID',
scope='avgpool' + scname)
expand = tf.tile(net, [1, num_point, 1, 1])
# net = tf.concat(axis=3, values=[expand,
# net_tot,
# ])
net = tf_util.conv2d(expand,
params[11], [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv2',
weight_decay=weight_decay,
is_dist=True)
net = tf_util.dropout(net,
keep_prob=0.6,
is_training=is_training,
scope='seg/dp1')
net = tf_util.conv2d(net,
params[11], [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv3',
weight_decay=weight_decay,
is_dist=True)
net = tf_util.dropout(net,
keep_prob=0.6,
is_training=is_training,
scope='seg/dp2')
net = tf_util.conv2d(net,
params[12], [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv4',
weight_decay=weight_decay,
is_dist=True)
net = tf_util.conv2d(net,
num_class, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
bn=False,
scope='seg/conv5',
weight_decay=weight_decay,
is_dist=True)
net = tf.cond(is_training, lambda: net, lambda: tf.nn.softmax(net))
net = tf.reshape(net, [batch_size, num_point, num_class])
return net, coefs, coefs2, adj_conv
def get_model(point_cloud,
is_training,
num_class,
weight_decay=None,
bn_decay=None,
scname=''):
''' input: BxNxF
output:BxNx(cats*segms) '''
batch_size = point_cloud.get_shape()[0]
num_point = point_cloud.get_shape()[1]
num_feat = point_cloud.get_shape()[2]
k = 10
adj = tf_util.pairwise_distanceR(point_cloud[:, :, :3])
n_heads = 1
nn_idx = tf_util.knn(adj, k=k)
net, locals_transform, coefs = gap_block(k, n_heads, nn_idx, point_cloud,
point_cloud, ('filter0', 16),
bn_decay, weight_decay,
is_training, scname)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet01' + scname,
bn_decay=bn_decay)
net01 = net
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet02' + scname,
bn_decay=bn_decay)
net02 = net
adj_matrix = tf_util.pairwise_distance(net)
nn_idx = tf_util.knn(adj_matrix, k=k)
adj_conv = nn_idx
n_heads = 1
net, locals_transform1, coefs2 = gap_block(k, n_heads, nn_idx, net,
point_cloud, ('filter1', 128),
bn_decay, weight_decay,
is_training, scname)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet11' + scname,
bn_decay=bn_decay)
net11 = net
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet12' + scname,
bn_decay=bn_decay)
net12 = net
net = tf.concat(
[net01, net02, net11, net12, locals_transform, locals_transform1],
axis=-1)
net = tf_util.conv2d(net,
3, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='agg' + scname,
bn_decay=bn_decay)
net = tf_util.avg_pool2d(net, [num_point, 1],
padding='VALID',
scope='avgpool' + scname)
max_pool = net
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
activation_fn=tf.nn.relu,
scope='fc1' + scname,
bn_decay=bn_decay)
net = tf_util.fully_connected(net,
128,
bn=True,
is_training=is_training,
activation_fn=tf.nn.relu,
scope='fc2' + scname,
bn_decay=bn_decay)
net = tf_util.fully_connected(net,
num_class,
activation_fn=None,
scope='fc3' + scname)
net = tf.squeeze(net)
return net, max_pool
def get_model(point_cloud,
is_training,
num_class,
global_pl,
weight_decay=None,
bn_decay=None,
scname=''):
''' input: BxNxF
output:BxNx(cats*segms) '''
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
num_feat = point_cloud.get_shape()[2].value
k = 10
adj = tf_util.pairwise_distanceR(point_cloud[:, :, :3])
n_heads = 1
nn_idx = tf_util.knn(adj, k=k)
net, locals_transform, coefs = gap_block(k, n_heads, nn_idx, point_cloud,
point_cloud, ('filter0', 16),
bn_decay, weight_decay,
is_training, scname)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet01' + scname,
bn_decay=bn_decay)
net01 = net
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet02' + scname,
bn_decay=bn_decay)
net02 = net
adj_matrix = tf_util.pairwise_distance(net)
nn_idx = tf_util.knn(adj_matrix, k=k)
adj_conv = nn_idx
n_heads = 1
net, locals_transform1, coefs2 = gap_block(k, n_heads, nn_idx, net,
point_cloud, ('filter1', 128),
bn_decay, weight_decay,
is_training, scname)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet11' + scname,
bn_decay=bn_decay)
net11 = net
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='gapnet12' + scname,
bn_decay=bn_decay)
net12 = net
global_expand = tf.reshape(global_pl, [batch_size, 1, 1, -1])
global_expand = tf.tile(global_expand, [1, num_point, 1, 1])
global_expand = tf_util.conv2d(global_expand,
16, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='global_expand' + scname,
bn_decay=bn_decay)
net = tf.concat([
net01, net02, net11, net12, global_expand, locals_transform,
locals_transform1
],
axis=-1)
net = tf_util.conv2d(net,
2, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope='agg' + scname,
bn_decay=bn_decay)
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='avgpool' + scname)
max_pool = net
expand = tf.tile(net, [1, num_point, 1, 1])
net = tf.concat(axis=3, values=[
expand,
net01,
net11,
])
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv2',
weight_decay=weight_decay,
is_dist=True)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv4',
weight_decay=weight_decay,
is_dist=True)
net = tf_util.conv2d(net,
num_class, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
bn=False,
scope='seg/conv5',
weight_decay=weight_decay,
is_dist=True)
net = tf.reshape(net, [batch_size, num_point, num_class])
return net, max_pool
def get_model(point_cloud,
mask,
is_training,
num_class,
weight_decay=None,
bn_decay=None,
scname=''):
batch_size = point_cloud.get_shape()[0]
k = 20
adj, mask_matrix = tf_util.pairwise_distanceR(point_cloud[:, :, :3], mask)
nn_idx = tf_util.knn(adj, k=k)
edge_feature_0 = get_edge_feature(point_cloud, nn_idx=nn_idx, k=k)
features_0 = GetLocalFeat(edge_feature_0,
scname + 'local0',
128,
is_training,
bn_decay=bn_decay)
adj = tf_util.pairwise_distance(features_0, mask_matrix)
nn_idx = tf_util.knn(adj, k=k)
edge_feature_1 = get_edge_feature(features_0, nn_idx=nn_idx, k=k)
features_1 = GetLocalFeat(edge_feature_1,
scname + 'local1',
64,
is_training,
bn_decay=bn_decay)
self_att_1, attention1 = GetSelfAtt(tf.squeeze(features_1),
mask,
scname + 'att1',
64,
is_training,
bn_decay=bn_decay)
self_att_2, attention2 = GetSelfAtt(self_att_1,
mask,
scname + 'att2',
64,
is_training,
bn_decay=bn_decay)
self_att_3, attention3 = GetSelfAtt(self_att_2,
mask,
scname + 'att3',
64,
is_training,
bn_decay=bn_decay)
concat = tf.concat([
self_att_1,
self_att_2,
self_att_3,
tf.squeeze(features_1),
],
axis=-1)
net = tf_util.conv1d(concat,
256,
1,
padding='VALID',
stride=1,
activation_fn=tf.nn.relu,
bn=True,
is_training=is_training,
scope=scname + 'concat',
bn_decay=bn_decay)
net = tf.reduce_mean(net, axis=2, keep_dims=True)
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net,
128,
bn=True,
is_training=is_training,
activation_fn=tf.nn.relu,
scope=scname + 'fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.5,
is_training=is_training,
scope=scname + 'dp1')
net = tf_util.fully_connected(net,
num_class,
activation_fn=None,
scope='fc3' + scname)
return net, attention1, attention2, attention3