def get_sdf_3dcnn_binary(grid_idx, globalfeats, is_training, batch_size, num_point, bn, bn_decay, wd=None, FLAGS=None):
globalfeats_expand = tf.reshape(globalfeats, [batch_size, 1, 1, 1, -1])
print('globalfeats_expand', globalfeats_expand.get_shape())
net2 = tf_util.conv3d_transpose(globalfeats_expand, 128, [2, 2, 2], stride=[2, 2, 2],
bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, scope='3deconv1') # 2
net2 = tf_util.conv3d_transpose(net2, 128, [3, 3, 3], stride=[2, 2, 2],bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, scope='3deconv2') # 4
net2 = tf_util.conv3d_transpose(net2, 128, [3, 3, 3], stride=[2, 2, 2], bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, scope='3deconv3') # 8
net2 = tf_util.conv3d_transpose(net2, 64, [3, 3, 3], stride=[2, 2, 2], bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, scope='3deconv4') # 16
net2 = tf_util.conv3d_transpose(net2, 64, [3, 3, 3], stride=[2, 2, 2], bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, scope='3deconv5') # 32
net2 = tf_util.conv3d_transpose(net2, 32, [3, 3, 3], stride=[2, 2, 2], bn_decay=bn_decay, bn=bn,
is_training=is_training, weight_decay=wd, padding='VALID', scope='3deconv6') # 65
net2 = tf_util.conv3d(net2, 2, [1, 1, 1], stride=[1, 1, 1], bn_decay=bn_decay, bn=bn, activation_fn=None,
is_training=is_training, weight_decay=wd, padding='VALID', scope='3conv7_binary')
res_plus = FLAGS.sdf_res+1
full_inter = tf.reshape(net2, (batch_size, res_plus, res_plus, res_plus))
print("3d cnn net2 shape:", full_inter.get_shape())
pred = tf.reshape(full_inter, [batch_size, -1, 2])
return pred
def get_model(pointgrid, is_training):
# Args:
# pointgrid: of size B x N x N x N x NUM_FEATURES
# is_training: boolean tensor
# Returns:
# pred_cat: of size B x NUM_CATEGORY
# pred_seg: of size B x N x N x N x (K+1) x NUM_PART_SEG
# Encoder
batch_size = pointgrid.get_shape()[0].value
conv1 = tf_util.conv3d(pointgrid,
64, [5, 5, 5],
scope='conv1',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N
conv2 = tf_util.conv3d(conv1,
64, [5, 5, 5],
scope='conv2',
activation_fn=leak_relu,
stride=[2, 2, 2],
bn=True,
is_training=is_training) # N/2
conv3 = tf_util.conv3d(conv2,
64, [5, 5, 5],
scope='conv3',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/2
conv4 = tf_util.conv3d(conv3,
128, [3, 3, 3],
scope='conv4',
activation_fn=leak_relu,
stride=[2, 2, 2],
bn=True,
is_training=is_training) # N/4
conv5 = tf_util.conv3d(conv4,
128, [3, 3, 3],
scope='conv5',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/4
conv6 = tf_util.conv3d(conv5,
256, [3, 3, 3],
scope='conv6',
activation_fn=leak_relu,
stride=[2, 2, 2],
bn=True,
is_training=is_training) # N/8
conv7 = tf_util.conv3d(conv6,
256, [3, 3, 3],
scope='conv7',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/8
conv8 = tf_util.conv3d(conv7,
512, [3, 3, 3],
scope='conv8',
activation_fn=leak_relu,
stride=[2, 2, 2],
bn=True,
is_training=is_training) # N/16
conv9 = tf_util.conv3d(conv8,
512, [1, 1, 1],
scope='conv9',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/16
# Classification Network
conv9_flat = tf.reshape(conv9, [batch_size, -1])
fc1 = tf_util.fully_connected(conv9_flat,
512,
activation_fn=leak_relu,
bn=True,
is_training=is_training,
scope='fc1')
do1 = tf_util.dropout(fc1,
keep_prob=0.7,
is_training=is_training,
scope='do1')
fc2 = tf_util.fully_connected(do1,
256,
activation_fn=leak_relu,
bn=True,
is_training=is_training,
scope='fc2')
do2 = tf_util.dropout(fc2,
keep_prob=0.7,
is_training=is_training,
scope='do2')
pred_cat = tf_util.fully_connected(do2,
NUM_CATEGORY,
activation_fn=None,
bn=False,
scope='pred_cat')
# Segmentation Network
cat_features = tf.tile(
tf.reshape(tf.concat([fc2, pred_cat], axis=1),
[batch_size, 1, 1, 1, -1]), [1, N / 16, N / 16, N / 16, 1])
conv9_cat = tf.concat([conv9, cat_features], axis=4)
deconv1 = tf_util.conv3d_transpose(conv9_cat,
256, [3, 3, 3],
scope='deconv1',
activation_fn=leak_relu,
bn=True,
is_training=is_training,
stride=[2, 2, 2],
padding='SAME') # N/8
conv7_deconv1 = tf.concat(axis=4, values=[conv7, deconv1])
deconv2 = tf_util.conv3d(conv7_deconv1,
256, [3, 3, 3],
scope='deconv2',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/8
deconv3 = tf_util.conv3d_transpose(deconv2,
128, [3, 3, 3],
scope='deconv3',
activation_fn=leak_relu,
bn=True,
is_training=is_training,
stride=[2, 2, 2],
padding='SAME') # N/4
conv5_deconv3 = tf.concat(axis=4, values=[conv5, deconv3])
deconv4 = tf_util.conv3d(conv5_deconv3,
128, [3, 3, 3],
scope='deconv4',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/4
deconv5 = tf_util.conv3d_transpose(deconv4,
64, [3, 3, 3],
scope='deconv5',
activation_fn=leak_relu,
bn=True,
is_training=is_training,
stride=[2, 2, 2],
padding='SAME') # N/2
conv3_deconv5 = tf.concat(axis=4, values=[conv3, deconv5])
deconv6 = tf_util.conv3d(conv3_deconv5,
64, [5, 5, 5],
scope='deconv6',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N/2
deconv7 = tf_util.conv3d_transpose(deconv6,
64, [5, 5, 5],
scope='deconv7',
activation_fn=leak_relu,
bn=True,
is_training=is_training,
stride=[2, 2, 2],
padding='SAME') # N
conv1_deconv7 = tf.concat(axis=4, values=[conv1, deconv7])
deconv8 = tf_util.conv3d(conv1_deconv7,
64, [5, 5, 5],
scope='deconv8',
activation_fn=leak_relu,
bn=True,
is_training=is_training) # N
pred_seg = tf_util.conv3d(deconv8, (K + 1) * NUM_SEG_PART, [5, 5, 5],
scope='pred_seg',
activation_fn=None,
bn=False,
is_training=is_training)
pred_seg = tf.reshape(pred_seg, [batch_size, N, N, N, K + 1, NUM_SEG_PART])
return pred_cat, pred_seg
def get_model(point_cloud, is_training, bn_decay=None):
""" Classification PointNet, input is BxNx3, output Bx40 """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(point_cloud,
is_training,
bn_decay,
K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
net = tf_util.conv2d(input_image,
64, [1, 3],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
net_transformed = tf.expand_dims(net_transformed, [2])
net = tf_util.conv2d(net_transformed,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay)
# Symmetric function: max pooling
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
# net = tf.reshape(net, [batch_size, -1])
net = tf.reshape(net, [batch_size, 1, 1, 1, -1])
net = tf_util.conv3d_transpose(net,
64, [4, 4, 4],
padding='VALID',
stride=[1, 1, 1],
scope='deconv1',
bn=True,
is_training=is_training)
net = tf_util.conv3d_transpose(net,
32, [6, 6, 6],
padding='VALID',
stride=[2, 2, 2],
scope='deconv2',
bn=True,
is_training=is_training)
net = tf_util.conv3d_transpose(net,
1, [8, 8, 8],
padding='VALID',
stride=[2, 2, 2],
scope='deconv3',
bn=True,
is_training=is_training)
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=0.7, 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=0.7, is_training=is_training,
# scope='dp2')
# net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
# return net, end_points
return net, end_points