def gap_block(k, n_heads, nn_idx, net, point_cloud, edge_size, bn_decay,
weight_decay, is_training, scname):
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(
net,
edge_size[1],
nn_idx,
activation=tf.nn.relu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer{0}'.format(edge_size[0]) + scname,
k=k,
i=i)
attns.append(
edge_feature
) # This is the edge feature * att. coeff. activated by RELU, one per particle
local_features.append(locals) #Those are the yij
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud, -2), neighbors_features], axis=-1)
locals_transform = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
return neighbors_features, locals_transform, coefs
def gap_block(k,
n_heads,
nn_idx,
net,
edge_size,
bn_decay,
weight_decay,
is_training,
scname,
bn=True):
attns = []
local_features = []
for i in range(n_heads):
edge_feature, self_attention, locals = attn_feature(
net,
edge_size[1],
nn_idx,
activation=tf.nn.relu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
bn=bn,
layer=scname + 'layer',
k=k,
i=i)
attns.append(
edge_feature
) # This is the edge feature * att. coeff. activated by RELU, one per particle
local_features.append(locals) #Those are the yij
neighbors_features = tf.concat(attns, axis=-1)
batch_size = net.get_shape().as_list()[0]
net = tf.squeeze(net)
if batch_size == 1:
net = tf.expand_dims(net, 0)
neighbors_features = tf.concat(
[tf.expand_dims(net, -2), neighbors_features], axis=-1)
locals_transform = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
return neighbors_features, locals_transform
def get_model(point_cloud, is_training, bn_decay=None):
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
k = 20
adj_matrix = tf_util.pairwise_distance(point_cloud)
nn_idx = tf_util.knn(adj_matrix, k=k)
n_heads = 1
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(point_cloud,
16,
nn_idx,
activation=tf.nn.elu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer0',
k=k,
i=i)
attns.append(edge_feature)
local_features.append(locals)
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud, -2), neighbors_features], axis=-1)
locals_max_transform = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(neighbors_features,
locals_max_transform,
is_training,
bn_decay,
K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
adj_matrix = tf_util.pairwise_distance(point_cloud_transformed)
nn_idx = tf_util.knn(adj_matrix, k=k)
n_heads = 4
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(point_cloud_transformed,
16,
nn_idx,
activation=tf.nn.elu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer1',
k=k,
i=i)
attns.append(edge_feature)
local_features.append(locals)
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud_transformed, -2), neighbors_features],
axis=-1)
net = tf_util.conv2d(neighbors_features,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet1',
bn_decay=bn_decay)
net1 = net
locals_max = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet2',
bn_decay=bn_decay)
net2 = net
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet3',
bn_decay=bn_decay)
net3 = net
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet4',
bn_decay=bn_decay)
net4 = net
net = tf_util.conv2d(tf.concat([net1, net2, net3, net4, locals_max],
axis=-1),
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='agg',
bn_decay=bn_decay)
net = tf.reduce_max(net, axis=1, keep_dims=True)
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net,
512,
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.5,
is_training=is_training,
scope='dp1')
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.5,
is_training=is_training,
scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points
def get_model(point_cloud, input_label, is_training, cat_num, part_num, \
batch_size, num_point, weight_decay, bn_decay=None):
k = 30
adj_matrix = tf_util.pairwise_distance(point_cloud)
nn_idx = tf_util.knn(adj_matrix, k=k)
n_heads = 1
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(point_cloud,
16,
nn_idx,
activation=tf.nn.elu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer0',
k=k,
i=i,
is_dist=True)
attns.append(edge_feature)
local_features.append(locals)
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud, -2), neighbors_features], axis=-1)
locals_max_transform = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(neighbors_features,
locals_max_transform,
is_training,
bn_decay,
K=3,
is_dist=True)
point_cloud_transformed = tf.matmul(point_cloud, transform)
adj_matrix = tf_util.pairwise_distance(point_cloud_transformed)
nn_idx = tf_util.knn(adj_matrix, k=k)
n_heads = 4
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(point_cloud_transformed,
16,
nn_idx,
activation=tf.nn.elu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer1',
k=k,
i=i,
is_dist=True)
attns.append(edge_feature)
local_features.append(locals)
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud_transformed, -2), neighbors_features],
axis=-1)
locals_max1 = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
net = tf_util.conv2d(neighbors_features,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet1',
bn_decay=bn_decay,
is_dist=True)
net1 = net
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet2',
bn_decay=bn_decay,
is_dist=True)
net2 = net
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet3',
bn_decay=bn_decay,
is_dist=True)
net3 = net
adj_matrix = tf_util.pairwise_distance(net)
nn_idx = tf_util.knn(adj_matrix, k=k)
n_heads = 4
attns = []
local_features = []
for i in range(n_heads):
edge_feature, coefs, locals = attn_feature(net,
128,
nn_idx,
activation=tf.nn.elu,
in_dropout=0.6,
coef_dropout=0.6,
is_training=is_training,
bn_decay=bn_decay,
layer='layer2',
k=k,
i=i,
is_dist=True)
attns.append(edge_feature)
local_features.append(locals)
neighbors_features = tf.concat(attns, axis=-1)
neighbors_features = tf.concat(
[tf.expand_dims(point_cloud_transformed, -2), neighbors_features],
axis=-1)
locals_max2 = tf.reduce_max(tf.concat(local_features, axis=-1),
axis=-2,
keep_dims=True)
net = tf_util.conv2d(neighbors_features,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet4',
bn_decay=bn_decay,
is_dist=True)
net4 = net
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet5',
bn_decay=bn_decay,
is_dist=True)
net5 = net
net = tf_util.conv2d(net,
512, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet6',
bn_decay=bn_decay,
is_dist=True)
net6 = net
net = tf_util.conv2d(tf.concat([net3, net6, locals_max1, locals_max2],
axis=-1),
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='gapnet8',
bn_decay=bn_decay,
is_dist=True)
net8 = net
out_max = tf_util.max_pool2d(net8, [num_point, 1],
padding='VALID',
scope='maxpool')
one_hot_label_expand = tf.reshape(input_label, [batch_size, 1, 1, cat_num])
one_hot_label_expand = tf_util.conv2d(one_hot_label_expand,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='one_hot_label_expand',
bn_decay=bn_decay,
is_dist=True)
out_max = tf.concat(axis=3, values=[out_max, one_hot_label_expand])
expand = tf.tile(out_max, [1, num_point, 1, 1])
concat = tf.concat(axis=3, values=[expand, net8])
net9 = tf_util.conv2d(concat,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn_decay=bn_decay,
bn=True,
is_training=is_training,
scope='seg/conv1',
weight_decay=weight_decay,
is_dist=True)
net9 = tf_util.dropout(net9,
keep_prob=0.6,
is_training=is_training,
scope='seg/dp1')
net9 = tf_util.conv2d(net9,
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)
net9 = tf_util.dropout(net9,
keep_prob=0.6,
is_training=is_training,
scope='seg/dp2')
net9 = tf_util.conv2d(net9,
128, [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)
net9 = tf_util.conv2d(net9,
part_num, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
bn=False,
scope='seg/conv4',
weight_decay=weight_decay,
is_dist=True)
net9 = tf.reshape(net9, [batch_size, num_point, part_num])
return net9