def vec_transformationByMat(poses,
input_capsule_dim,
input_capsule_num,
output_capsule_dim,
output_capsule_num,
shared=True):
inputs_poses_shape = poses.get_shape().as_list()
poses = poses[..., tf.newaxis, :]
poses = tf.tile(poses, [1, 1, output_capsule_num, 1])
if shared:
kernel = capsule_utils._get_weights_wrapper(name='weights',
shape=[
1, 1,
output_capsule_num,
output_capsule_dim,
input_capsule_dim
],
weights_decay_factor=0.0)
kernel = tf.tile(kernel,
[inputs_poses_shape[0], input_capsule_num, 1, 1, 1])
else:
kernel = capsule_utils._get_weights_wrapper(name='weights',
shape=[
1, input_capsule_num,
output_capsule_num,
output_capsule_dim,
input_capsule_dim
],
weights_decay_factor=0.0)
kernel = tf.tile(kernel, [inputs_poses_shape[0], 1, 1, 1, 1])
tf.logging.info('poses: {}'.format(poses[..., tf.newaxis].get_shape()))
tf.logging.info('kernel: {}'.format(kernel.get_shape()))
u_hat_vecs = tf.squeeze(tf.matmul(kernel, poses[..., tf.newaxis]), axis=-1)
u_hat_vecs = tf.transpose(u_hat_vecs, (0, 2, 1, 3))
return u_hat_vecs
def vec_transformationByConv(poses, input_capsule_dim, input_capsule_num,
output_capsule_dim, output_capsule_num):
kernel = capsule_utils._get_weights_wrapper(
name='weights',
shape=[1, input_capsule_dim, output_capsule_dim * output_capsule_num],
weights_decay_factor=0.0)
tf.logging.info('poses: {}'.format(poses.get_shape()))
tf.logging.info('kernel: {}'.format(kernel.get_shape()))
u_hat_vecs = tf.nn.conv1d(poses, kernel, stride=1, padding="VALID")
u_hat_vecs = tf.reshape(
u_hat_vecs,
(-1, input_capsule_num, output_capsule_num, output_capsule_dim))
u_hat_vecs = tf.transpose(u_hat_vecs, (0, 2, 1, 3))
return u_hat_vecs
def capsules_init(inputs, shape, strides, padding, pose_shape, add_bias, name):
with tf.variable_scope(name):
poses = capsule_utils._conv2d_wrapper(inputs,
shape=shape[0:-1] +
[shape[-1] * pose_shape],
strides=strides,
padding=padding,
add_bias=add_bias,
activation_fn=None,
name='pose_stacked')
poses_shape = poses.get_shape().as_list()
poses = tf.reshape(
poses, [-1, poses_shape[1], poses_shape[2], shape[-1], pose_shape])
beta_a = capsule_utils._get_weights_wrapper(name='beta_a',
shape=[1, shape[-1]])
poses = squash_v1(poses, axis=-1)
activations = tf.sqrt(tf.reduce_sum(tf.square(poses),
axis=-1)) + beta_a
tf.logging.info("prim poses dimension:{}".format(poses.get_shape()))
return poses, activations
def capsule_fc_layer(nets, output_capsule_num, iterations, name):
with tf.variable_scope(name):
poses, i_activations = nets
input_pose_shape = poses.get_shape().as_list()
u_hat_vecs = vec_transformationByConv(
poses,
input_pose_shape[-1],
input_pose_shape[1],
input_pose_shape[-1],
output_capsule_num,
)
tf.logging.info('votes shape: {}'.format(u_hat_vecs.get_shape()))
beta_a = capsule_utils._get_weights_wrapper(
name='beta_a', shape=[1, output_capsule_num])
poses, activations = routing(u_hat_vecs, beta_a, iterations,
output_capsule_num, i_activations)
tf.logging.info('capsule fc shape: {}'.format(poses.get_shape()))
return poses, activations
def capsule_conv_layer(nets, shape, strides, iterations, name):
with tf.variable_scope(name):
poses, i_activations = nets
inputs_poses_shape = poses.get_shape().as_list()
hk_offsets = [[
(h_offset + k_offset) for k_offset in range(0, shape[0])
] for h_offset in range(0, inputs_poses_shape[1] + 1 -
shape[0], strides[1])]
wk_offsets = [[
(w_offset + k_offset) for k_offset in range(0, shape[1])
] for w_offset in range(0, inputs_poses_shape[2] + 1 -
shape[1], strides[2])]
inputs_poses_patches = tf.transpose(tf.gather(
tf.gather(poses,
hk_offsets,
axis=1,
name='gather_poses_height_kernel'),
wk_offsets,
axis=3,
name='gather_poses_width_kernel'),
perm=[0, 1, 3, 2, 4, 5, 6],
name='inputs_poses_patches')
tf.logging.info('i_poses_patches shape: {}'.format(
inputs_poses_patches.get_shape()))
inputs_poses_shape = inputs_poses_patches.get_shape().as_list()
inputs_poses_patches = tf.reshape(
inputs_poses_patches,
[-1, shape[0] * shape[1] * shape[2], inputs_poses_shape[-1]])
i_activations_patches = tf.transpose(tf.gather(
tf.gather(i_activations,
hk_offsets,
axis=1,
name='gather_activations_height_kernel'),
wk_offsets,
axis=3,
name='gather_activations_width_kernel'),
perm=[0, 1, 3, 2, 4, 5],
name='inputs_activations_patches')
tf.logging.info('i_activations_patches shape: {}'.format(
i_activations_patches.get_shape()))
i_activations_patches = tf.reshape(
i_activations_patches, [-1, shape[0] * shape[1] * shape[2]])
u_hat_vecs = vec_transformationByConv(
inputs_poses_patches,
inputs_poses_shape[-1],
shape[0] * shape[1] * shape[2],
inputs_poses_shape[-1],
shape[3],
)
tf.logging.info('capsule conv votes shape: {}'.format(
u_hat_vecs.get_shape()))
beta_a = capsule_utils._get_weights_wrapper(name='beta_a',
shape=[1, shape[3]])
poses, activations = routing(u_hat_vecs, beta_a, iterations, shape[3],
i_activations_patches)
poses = tf.reshape(poses, [
inputs_poses_shape[0], inputs_poses_shape[1],
inputs_poses_shape[2], shape[3], inputs_poses_shape[-1]
])
activations = tf.reshape(activations, [
inputs_poses_shape[0], inputs_poses_shape[1],
inputs_poses_shape[2], shape[3]
])
nets = poses, activations
tf.logging.info("capsule conv poses dimension:{}".format(
poses.get_shape()))
tf.logging.info("capsule conv activations dimension:{}".format(
activations.get_shape()))
return nets