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| 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)
net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='maxpool')
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
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