def forward_pass(frcs, bu_msg, graph, pool_shape):
"""
Forward pass inference using a tree-approximation (cf. Sec S4.2).
Parameters
----------
frcs : numpy.ndarray of numpy.int
Nx3 array of (feature idx, row, column), where each row represents a
single pool center.
bu_msg : 3D numpy.ndarray of float
The bottom-up messages from the preprocessing layer.
Shape is (num_feats, rows, cols)
graph : networkx.Graph
An undirected graph whose edges describe the pairwise constraints between
the pool centers.
The tightness of the constraint is in the 'perturb_radius' edge attribute.
pool_shape : (int, int)
Vertical and horizontal pool shapes.
Returns
-------
fp_score : float
Forward pass score.
"""
height, width = bu_msg.shape[-2:]
# Vertical and horizontal pool shapes
vps, hps = pool_shape
def _pool_slice(f, r, c):
assert (r - vps // 2 >= 0 and r + vps - vps // 2 < height and
c - hps // 2 >= 0 and c + hps - hps // 2 < width), \
"Some pools are out of the image boundaries. "\
"Consider increase image padding or reduce pool shapes."
return np.s_[f, r - vps // 2:r + vps - vps // 2,
c - hps // 2:c + hps - hps // 2]
# Find a schedule to compute the max marginal for the most constrained tree
tree_schedule = get_tree_schedule(frcs, graph)
# If we're sending a message out from x to y, it means x has received all
# incoming messages
incoming_msgs = {}
for source, target, perturb_radius in tree_schedule:
msg_in = bu_msg[_pool_slice(*frcs[source])]
if source in incoming_msgs:
msg_in = msg_in + incoming_msgs[source]
del incoming_msgs[source]
msg_in = dilate_2d(msg_in,
(2 * perturb_radius + 1, 2 * perturb_radius + 1))
if target in incoming_msgs:
incoming_msgs[target] += msg_in
else:
incoming_msgs[target] = msg_in
fp_score = np.max(incoming_msgs[tree_schedule[-1, 1]] +
bu_msg[_pool_slice(*frcs[tree_schedule[-1, 1]])])
return fp_score
def forward_pass(frcs, bu_msg, graph, pool_shape):
"""
Forward pass inference using a tree-approximation (cf. Sec S4.2).
Parameters
----------
frcs : numpy.ndarray of numpy.int
Nx3 array of (feature idx, row, column), where each row represents a
single pool center.
bu_msg : 3D numpy.ndarray of float
The bottom-up messages from the preprocessing layer.
Shape is (num_feats, rows, cols)
graph : networkx.Graph
An undirected graph whose edges describe the pairwise constraints between
the pool centers.
The tightness of the constraint is in the 'perturb_radius' edge attribute.
pool_shape : (int, int)
Vertical and horizontal pool shapes.
Returns
-------
fp_score : float
Forward pass score.
"""
height, width = bu_msg.shape[-2:]
# Vertical and horizontal pool shapes
vps, hps = pool_shape
def _pool_slice(f, r, c):
assert (r - vps // 2 >= 0 and r + vps - vps // 2 < height and
c - hps // 2 >= 0 and c + hps - hps // 2 < width), \
"Some pools are out of the image boundaries. "\
"Consider increase image padding or reduce pool shapes."
return np.s_[f,
r - vps // 2: r + vps - vps // 2,
c - hps // 2: c + hps - hps // 2]
# Find a schedule to compute the max marginal for the most constrained tree
tree_schedule = get_tree_schedule(frcs, graph)
# If we're sending a message out from x to y, it means x has received all
# incoming messages
incoming_msgs = {}
for source, target, perturb_radius in tree_schedule:
msg_in = bu_msg[_pool_slice(*frcs[source])]
if source in incoming_msgs:
msg_in = msg_in + incoming_msgs[source]
del incoming_msgs[source]
msg_in = dilate_2d(msg_in, (2 * perturb_radius + 1, 2 * perturb_radius + 1))
if target in incoming_msgs:
incoming_msgs[target] += msg_in
else:
incoming_msgs[target] = msg_in
fp_score = np.max(incoming_msgs[tree_schedule[-1, 1]] +
bu_msg[_pool_slice(*frcs[tree_schedule[-1, 1]])])
return fp_score
def compute_1pl_message(in_mess, pert_radius):
"""Compute the outgoing message of a lateral factor given the
perturbation radius and input message.
Parameters
----------
in_mess : numpy.array
Input BP messages to the factor. Each message has shape vps x hps.
pert_radius : int
Perturbation radius corresponding to the factor.
Returns
-------
out_mess : numpy.array
Output BP message (at the opposite end of the factor from the input message).
Shape is (vps, hps).
"""
pert_diameter = 2 * pert_radius + 1
out_mess = dilate_2d(in_mess, (pert_diameter, pert_diameter))
return out_mess - out_mess.max()
def compute_1pl_message(in_mess, pert_radius):
"""Compute the outgoing message of a lateral factor given the
perturbation radius and input message.
Parameters
----------
in_mess : numpy.array
Input BP messages to the factor. Each message has shape vps x hps.
pert_radius : int
Perturbation radius corresponding to the factor.
Returns
-------
out_mess : numpy.array
Output BP message (at the opposite end of the factor from the input message).
Shape is (vps, hps).
"""
pert_diameter = 2 * pert_radius + 1
out_mess = dilate_2d(in_mess, (pert_diameter, pert_diameter))
return out_mess - out_mess.max()