def while_body(i, est_pose, est_inputs):
with tf.variable_scope('encoder_0', reuse=tf.AUTO_REUSE):
features = mlp_conv(est_inputs, [128, 256], bn=self.bn)
features_global = tf.reduce_max(features, axis=1, keep_dims=True, name='maxpool_0')
features = tf.concat([features, tf.tile(features_global, [1, tf.shape(inputs)[1], 1])], axis=2)
with tf.variable_scope('encoder_1', reuse=tf.AUTO_REUSE):
features = mlp_conv(features, [512, 1024], bn=self.bn)
features = tf.reduce_max(features, axis=1, name='maxpool_1')
with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
if self.rot_representation == 'quat':
est_pose_rep_i = mlp(features, [1024, 1024, 512, 512, 256, 7], bn=self.bn)
elif self.rot_representation == '6dof':
est_pose_rep_i = mlp(features, [1024, 1024, 512, 512, 256, 9], bn=self.bn)
with tf.variable_scope('est', reuse=tf.AUTO_REUSE):
if self.rot_representation == 'quat':
t = tf.expand_dims(est_pose_rep_i[:, 4:], axis=2)
q = est_pose_rep_i[:, 0:4]
R = quat2rotm_tf(q)
elif self.rot_representation == '6dof':
t = tf.expand_dims(est_pose_rep_i[:, 6:], axis=2)
mat6d = est_pose_rep_i[:, 0:6]
R = mat6d2rotm_tf(mat6d)
est_pose_T_i = tf.concat([
tf.concat([R, t], axis=2),
tf.concat([tf.zeros([B, 1, 3]), tf.ones([B, 1, 1])], axis=2)],
axis=1)
est_inputs = transform_tf(est_inputs, est_pose_T_i)
est_pose = tf.linalg.matmul(est_pose_T_i, est_pose)
return [tf.add(i, 1), est_pose, est_inputs]
def test_identity(self):
rotm = euldeg2rotm(0, 0, 0)
rotm = tf.convert_to_tensor(
np.tile(np.expand_dims(rotm, axis=0), (24, 1, 1)))
mat6d_gt = rotm2mat6d_tf(rotm)
rotm_est = mat6d2rotm_tf(mat6d_gt)
assert np.all(np.isclose(rotm, rotm_est))
def test_rand(self):
rotm = euldeg2rotm(23, 82, 13)
rotm = tf.convert_to_tensor(np.expand_dims(rotm, axis=0))
mat6d_gt = rotm2mat6d_tf(rotm)
rotm_est = mat6d2rotm_tf(mat6d_gt)
assert np.all(np.isclose(rotm, rotm_est))