def test_group_initialization(self):
"""Group initialization of weights nodes"""
v1 = spn.IndicatorLeaf(num_vars=1, num_vals=2)
v2 = spn.IndicatorLeaf(num_vars=1, num_vals=4)
v3 = spn.IndicatorLeaf(num_vars=1, num_vals=2)
v4 = spn.IndicatorLeaf(num_vars=1, num_vals=2)
# Sum
s1 = spn.Sum(v1)
s1.generate_weights(tf.initializers.constant([0.2, 0.3]))
s2 = spn.Sum(v2)
s2.generate_weights(tf.initializers.constant(5))
# ParallelSums
s3 = spn.ParallelSums(*[v3, v4], num_sums=2)
s3.generate_weights(
tf.initializers.constant([0.1, 0.2, 0.3, 0.4, 0.4, 0.3, 0.2, 0.1]))
s4 = spn.ParallelSums(*[v1, v2, v3, v4], num_sums=3)
s4.generate_weights(tf.initializers.constant(2.0))
# Product
p = spn.Product(s1, s2, s3, s4)
init = spn.initialize_weights(p)
with self.test_session() as sess:
sess.run([init])
val1 = sess.run(s1.weights.node.get_value())
val2 = sess.run(s2.weights.node.get_value())
val3 = sess.run(s3.weights.node.get_value())
val4 = sess.run(s4.weights.node.get_value())
val1_log = sess.run(tf.exp(s1.weights.node.get_log_value()))
val2_log = sess.run(tf.exp(s2.weights.node.get_log_value()))
val3_log = sess.run(tf.exp(s3.weights.node.get_log_value()))
val4_log = sess.run(tf.exp(s4.weights.node.get_log_value()))
self.assertEqual(val1.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val2.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val3.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val4.dtype, spn.conf.dtype.as_numpy_dtype())
np.testing.assert_array_almost_equal(val1, [[0.4, 0.6]])
np.testing.assert_array_almost_equal(val2, [[0.25, 0.25, 0.25, 0.25]])
np.testing.assert_array_almost_equal(
val3, [[0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]])
np.testing.assert_array_almost_equal(
val4, [[0.1] * 10, [0.1] * 10, [0.1] * 10])
self.assertEqual(val1_log.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val2_log.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val3_log.dtype, spn.conf.dtype.as_numpy_dtype())
self.assertEqual(val4_log.dtype, spn.conf.dtype.as_numpy_dtype())
np.testing.assert_array_almost_equal(val1_log, [[0.4, 0.6]])
np.testing.assert_array_almost_equal(val2_log,
[[0.25, 0.25, 0.25, 0.25]])
np.testing.assert_array_almost_equal(
val3, [[0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]])
np.testing.assert_array_almost_equal(
val4, [[0.1] * 10, [0.1] * 10, [0.1] * 10])
def test_stochastic_argmax(self, argmax_zero):
spn.conf.argmax_zero = argmax_zero
N = 100000
s = spn.Sum()
x = tf.constant(
np.repeat([[0, 1, 1, 0, 1], [1, 0, 0, 1, 0]], repeats=N, axis=0))
argmax_op = tf.squeeze(s._reduce_argmax(tf.expand_dims(x, 1)))
with self.test_session() as sess:
argmax = sess.run(argmax_op)
hist_first, _ = np.histogram(argmax[:N], bins=list(range(6)))
hist_second, _ = np.histogram(argmax[N:], bins=list(range(6)))
if argmax_zero:
self.assertEqual(hist_first[1], N)
self.assertEqual(hist_second[0], N)
else:
[self.assertLess(hist_first[i], N / 3 + N / 6) for i in [1, 2, 4]]
[
self.assertGreater(hist_first[i], N / 3 - N / 6)
for i in [1, 2, 4]
]
[self.assertLess(hist_second[i], N / 2 + N / 6) for i in [0, 3]]
[self.assertGreater(hist_second[i], N / 2 - N / 6) for i in [0, 3]]
def test_sampling(self, sample_prob):
N = 100000
x = tf.expand_dims(tf.constant(np.repeat(
[[1, 2, 2, 1, 3], [3, 1, 1, 2, 1]], repeats=N, axis=0),
dtype=tf.float32),
axis=1)
s = spn.Sum()
probs = [[1 / 9, 2 / 9, 2 / 9, 1 / 9, 3 / 9],
[3 / 8, 1 / 8, 1 / 8, 2 / 8, 1 / 8]]
N_sampled = N * sample_prob
N_argmax = N - N_sampled
sample_op = tf.squeeze(
s._reduce_sample_log(tf.log(x), sample_prob=sample_prob))
with self.test_session() as sess:
sample_out = sess.run(sample_op)
for samples, prob, max_ind in zip(
np.split(sample_out, indices_or_sections=2), probs, [4, 0]):
hist, _ = np.histogram(samples, bins=list(range(6)))
for h, p, i in zip(hist, prob, range(5)):
if i == max_ind:
estimate = N_argmax + N_sampled * p
else:
estimate = N_sampled * p
self.assertLess(h, estimate + N / 6)
self.assertGreater(h, estimate - N / 6)
def test_dropconnect(self):
ivs = spn.IVs(num_vals=2, num_vars=4)
s = spn.Sum(ivs, dropconnect_keep_prob=0.5)
spn.generate_weights(s)
init = spn.initialize_weights(s)
mask = [[0., 1., 0., 1., 1., 1., 0., 1.],
[1., 0., 0., 0., 0., 0., 1., 0.]]
s._create_dropout_mask = MagicMock(
return_value=tf.expand_dims(tf.log(mask), 1))
val_op = tf.exp(s.get_log_value())
mask = tf.constant(mask, dtype=tf.float32)
truth = tf.reduce_mean(mask, axis=-1, keepdims=True)
with self.test_session() as sess:
sess.run(init)
dropconnect_out, truth_out = sess.run(
[val_op, truth],
feed_dict={ivs: -np.ones((2, 4), dtype=np.int32)})
self.assertAllClose(dropconnect_out, truth_out)
import libspn as spn
import tensorflow as tf
indicator_leaves = spn.IndicatorLeaf(num_vars=2,
num_vals=2,
name="indicator_x")
# Connect first two sums to indicators of first variable
sum_11 = spn.Sum((indicator_leaves, [0, 1]), name="sum_11")
sum_12 = spn.Sum((indicator_leaves, [0, 1]), name="sum_12")
# Connect another two sums to indicators of the second variable
sum_21 = spn.Sum((indicator_leaves, [2, 3]), name="sum_21")
sum_22 = spn.Sum((indicator_leaves, [2, 3]), name="sum_22")
# Connect three product nodes
prod_1 = spn.Product(sum_11, sum_21, name="prod_1")
prod_2 = spn.Product(sum_11, sum_22, name="prod_2")
prod_3 = spn.Product(sum_12, sum_22, name="prod_3")
# Connect a root sum
root = spn.Sum(prod_1, prod_2, prod_3, name="root")
# Connect a latent indicator
indicator_y = root.generate_latent_indicators(
name="indicator_y") # Can be added manually
# Generate weights
spn.generate_weights(
root,
initializer=tf.initializers.random_uniform()) # Can be added manually
for i in range(stack_size):
dilation_rate = 2**i
x = spn.ConvProductsDepthwise(x,
padding='full',
kernel_size=2,
strides=1,
dilation_rate=dilation_rate)
x = spn.LocalSums(x, num_channels=64)
# Create final layer of products
full_scope_prod = spn.ConvProductsDepthwise(x,
padding='wicker_top',
kernel_size=2,
strides=1,
dilation_rate=2**stack_size)
class_roots = spn.ParallelSums(full_scope_prod, num_sums=num_classes)
root = spn.Sum(class_roots)
# Add a IndicatorLeaf node to the root as a latent class variable
class_indicators = root.generate_latent_indicators()
# Generate the weights for the SPN rooted at `root`
spn.generate_weights(root,
log=True,
initializer=tf.initializers.random_uniform())
print("SPN depth: {}".format(root.get_depth()))
print("Number of products layers: {}".format(
root.get_num_nodes(node_type=spn.ConvProducts)))
print("Number of sums layers: {}".format(
root.get_num_nodes(node_type=spn.LocalSums)))
def test_generate_spn(self, num_decomps, num_subsets, num_mixtures,
num_input_mixtures, input_dims, input_dist, balanced,
node_type, log_weights):
"""A generic test for DenseSPNGenerator."""
if input_dist == spn.DenseSPNGenerator.InputDist.RAW \
and num_input_mixtures != 1:
# Redundant test case, so just return
return
# Input parameters
num_inputs = input_dims[0]
num_vars = input_dims[1]
num_vals = 2
printc("\n- num_inputs: %s" % num_inputs)
printc("- num_vars: %s" % num_vars)
printc("- num_vals: %s" % num_vals)
printc("- num_decomps: %s" % num_decomps)
printc("- num_subsets: %s" % num_subsets)
printc("- num_mixtures: %s" % num_mixtures)
printc("- input_dist: %s" %
("MIXTURE" if input_dist
== spn.DenseSPNGenerator.InputDist.MIXTURE else "RAW"))
printc("- balanced: %s" % balanced)
printc("- num_input_mixtures: %s" % num_input_mixtures)
printc("- node_type: %s" %
("SINGLE" if node_type == spn.DenseSPNGenerator.NodeType.SINGLE
else "BLOCK" if node_type
== spn.DenseSPNGenerator.NodeType.BLOCK else "LAYER"))
printc("- log_weights: %s" % log_weights)
# Inputs
inputs = [
spn.IndicatorLeaf(num_vars=num_vars,
num_vals=num_vals,
name=("IndicatorLeaf_%d" % (i + 1)))
for i in range(num_inputs)
]
gen = spn.DenseSPNGenerator(num_decomps=num_decomps,
num_subsets=num_subsets,
num_mixtures=num_mixtures,
input_dist=input_dist,
balanced=balanced,
num_input_mixtures=num_input_mixtures,
node_type=node_type)
# Generate Sub-SPNs
sub_spns = [
gen.generate(*inputs, root_name=("sub_root_%d" % (i + 1)))
for i in range(3)
]
# Generate random weights for the first sub-SPN
with tf.name_scope("Weights"):
spn.generate_weights(sub_spns[0],
tf.initializers.random_uniform(0.0, 1.0),
log=log_weights)
# Initialize weights of the first sub-SPN
sub_spn_init = spn.initialize_weights(sub_spns[0])
# Testing validity of the first sub-SPN
self.assertTrue(sub_spns[0].is_valid())
# Generate value ops of the first sub-SPN
sub_spn_v = sub_spns[0].get_value()
sub_spn_v_log = sub_spns[0].get_log_value()
# Generate path ops of the first sub-SPN
sub_spn_mpe_path_gen = spn.MPEPath(log=False)
sub_spn_mpe_path_gen_log = spn.MPEPath(log=True)
sub_spn_mpe_path_gen.get_mpe_path(sub_spns[0])
sub_spn_mpe_path_gen_log.get_mpe_path(sub_spns[0])
sub_spn_path = [sub_spn_mpe_path_gen.counts[inp] for inp in inputs]
sub_spn_path_log = [
sub_spn_mpe_path_gen_log.counts[inp] for inp in inputs
]
# Collect all weight nodes of the first sub-SPN
sub_spn_weight_nodes = []
def fun(node):
if node.is_param:
sub_spn_weight_nodes.append(node)
spn.traverse_graph(sub_spns[0], fun=fun)
# Generate an upper-SPN over sub-SPNs
products_lower = []
for sub_spn in sub_spns:
products_lower.append([v.node for v in sub_spn.values])
num_top_mixtures = [2, 1, 3]
sums_lower = []
for prods, num_top_mix in zip(products_lower, num_top_mixtures):
if node_type == spn.DenseSPNGenerator.NodeType.SINGLE:
sums_lower.append(
[spn.Sum(*prods) for _ in range(num_top_mix)])
elif node_type == spn.DenseSPNGenerator.NodeType.BLOCK:
sums_lower.append(
[spn.ParallelSums(*prods, num_sums=num_top_mix)])
else:
sums_lower.append([
spn.SumsLayer(*prods * num_top_mix,
num_or_size_sums=num_top_mix)
])
# Generate upper-SPN
root = gen.generate(*list(itertools.chain(*sums_lower)),
root_name="root")
# Generate random weights for the SPN
with tf.name_scope("Weights"):
spn.generate_weights(root,
tf.initializers.random_uniform(0.0, 1.0),
log=log_weights)
# Initialize weight of the SPN
spn_init = spn.initialize_weights(root)
# Testing validity of the SPN
self.assertTrue(root.is_valid())
# Generate value ops of the SPN
spn_v = root.get_value()
spn_v_log = root.get_log_value()
# Generate path ops of the SPN
spn_mpe_path_gen = spn.MPEPath(log=False)
spn_mpe_path_gen_log = spn.MPEPath(log=True)
spn_mpe_path_gen.get_mpe_path(root)
spn_mpe_path_gen_log.get_mpe_path(root)
spn_path = [spn_mpe_path_gen.counts[inp] for inp in inputs]
spn_path_log = [spn_mpe_path_gen_log.counts[inp] for inp in inputs]
# Collect all weight nodes in the SPN
spn_weight_nodes = []
def fun(node):
if node.is_param:
spn_weight_nodes.append(node)
spn.traverse_graph(root, fun=fun)
# Create a session
with self.test_session() as sess:
# Initializing weights
sess.run(sub_spn_init)
sess.run(spn_init)
# Generate input feed
feed = np.array(
list(
itertools.product(range(num_vals),
repeat=(num_inputs * num_vars))))
batch_size = feed.shape[0]
feed_dict = {}
for inp, f in zip(inputs, np.split(feed, num_inputs, axis=1)):
feed_dict[inp] = f
# Compute all values and paths of sub-SPN
sub_spn_out = sess.run(sub_spn_v, feed_dict=feed_dict)
sub_spn_out_log = sess.run(tf.exp(sub_spn_v_log),
feed_dict=feed_dict)
sub_spn_out_path = sess.run(sub_spn_path, feed_dict=feed_dict)
sub_spn_out_path_log = sess.run(sub_spn_path_log,
feed_dict=feed_dict)
# Compute all values and paths of the complete SPN
spn_out = sess.run(spn_v, feed_dict=feed_dict)
spn_out_log = sess.run(tf.exp(spn_v_log), feed_dict=feed_dict)
spn_out_path = sess.run(spn_path, feed_dict=feed_dict)
spn_out_path_log = sess.run(spn_path_log, feed_dict=feed_dict)
# Test if partition function of the sub-SPN and of the
# complete SPN is 1.0
self.assertAlmostEqual(sub_spn_out.sum(), 1.0, places=6)
self.assertAlmostEqual(sub_spn_out_log.sum(), 1.0, places=6)
self.assertAlmostEqual(spn_out.sum(), 1.0, places=6)
self.assertAlmostEqual(spn_out_log.sum(), 1.0, places=6)
# Test if the sum of counts for each value of each variable
# (6 variables, with 2 values each) = batch-size / num-vals
self.assertEqual(
np.sum(np.hstack(sub_spn_out_path), axis=0).tolist(),
[batch_size // num_vals] * num_inputs * num_vars * num_vals)
self.assertEqual(
np.sum(np.hstack(sub_spn_out_path_log), axis=0).tolist(),
[batch_size // num_vals] * num_inputs * num_vars * num_vals)
self.assertEqual(
np.sum(np.hstack(spn_out_path), axis=0).tolist(),
[batch_size // num_vals] * num_inputs * num_vars * num_vals)
self.assertEqual(
np.sum(np.hstack(spn_out_path_log), axis=0).tolist(),
[batch_size // num_vals] * num_inputs * num_vars * num_vals)
# Generates densely connected random SPNs
dense_generator = spn.DenseSPNGenerator(
node_type=spn.DenseSPNGenerator.NodeType.BLOCK,
num_subsets=num_subsets,
num_mixtures=num_mixtures,
num_decomps=num_decomps,
balanced=balanced,
input_dist=input_dist)
# Generate a dense SPN for each class
class_roots = [
dense_generator.generate(leaf_indicators) for _ in range(num_classes)
]
# Connect sub-SPNs to a root
root = spn.Sum(*class_roots, name="RootSum")
root = spn.convert_to_layer_nodes(root)
# Add an IVs node to the root as a latent class variable
class_indicators = root.generate_latent_indicators()
# Generate the weights for the SPN rooted at `root`
spn.generate_weights(root)
print("SPN depth: {}".format(root.get_depth()))
print("Number of products layers: {}".format(
root.get_num_nodes(node_type=spn.ProductsLayer)))
print("Number of sums layers: {}".format(
root.get_num_nodes(node_type=spn.SumsLayer)))
# Op for initializing all weights
def setup_learning(args, in_var, root):
no_op = tf.constant(0)
inference_type = spn.InferenceType.MARGINAL if args.value_inf_type == 'marginal' \
else spn.InferenceType.MPE
mpe_state = spn.MPEState(value_inference_type=inference_type,
matmul_or_conv=True)
if args.supervised:
# Root is provided with labels, p(x,y)
labels_node = root.generate_latent_indicators(name="LabelIndicators")
# Marginalized root, so without filling in labels, so p(x) = \sum_y p(x,y)
root_marginalized = spn.Sum(*root.values,
name="RootMarginalized",
weights=root.weights)
# A dummy node to get MPE state
labels_no_evidence_node = root_marginalized.generate_latent_indicators(
name="LabesNoEvidenceIndicators",
feed=-tf.ones([tf.shape(in_var.feed)[0], 1], dtype=tf.int32))
# Get prediction from dummy node
with tf.name_scope("Prediction"):
logger.info("Setting up MPE state")
if args.completion_by_marginal and isinstance(
in_var, ContinuousLeafBase):
in_var_mpe = in_var.impute_by_posterior_marginal(
labels_no_evidence_node)
class_mpe, = mpe_state.get_state(root_marginalized,
labels_no_evidence_node)
else:
class_mpe, in_var_mpe = mpe_state.get_state(
root_marginalized, labels_no_evidence_node, in_var)
correct = tf.squeeze(
tf.equal(class_mpe, tf.to_int64(labels_node.feed)))
else:
with tf.name_scope("Prediction"):
class_mpe = correct = no_op
labels_node = root_marginalized = None
if args.completion_by_marginal and isinstance(
in_var, ContinuousLeafBase):
in_var_mpe = in_var.impute_by_posterior_marginal(root)
else:
in_var_mpe, = mpe_state.get_state(root, in_var)
# Get the log likelihood
with tf.name_scope("LogLikelihoods"):
logger.info("Setting up log-likelihood")
val_gen = spn.LogValue(inference_type=inference_type)
labels_llh = val_gen.get_value(root)
no_labels_llh = val_gen.get_value(
root_marginalized) if args.supervised else labels_llh
if args.learning_algo == "em":
em_learning = spn.HardEMLearning(
root,
value_inference_type=inference_type,
initial_accum_value=args.initial_accum_value,
sample_winner=args.sample_path,
sample_prob=args.sample_prob,
use_unweighted=args.use_unweighted)
accumulate = em_learning.accumulate_updates()
with tf.control_dependencies([accumulate]):
update_op = em_learning.update_spn()
return correct, labels_node, labels_llh, no_labels_llh, update_op, class_mpe, no_op, \
no_op, in_var_mpe
logger.info("Setting up GD learning")
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(args.learning_rate,
global_step,
args.lr_decay_steps,
args.lr_decay_rate,
staircase=True)
learning_method = spn.LearningMethodType.DISCRIMINATIVE if args.learning_type == 'discriminative' else \
spn.LearningMethodType.GENERATIVE
learning = spn.GDLearning(
root, learning_task_type=spn.LearningTaskType.SUPERVISED if args.supervised else \
spn.LearningTaskType.UNSUPERVISED,
learning_method=learning_method, learning_rate=learning_rate,
marginalizing_root=root_marginalized, global_step=global_step)
optimizer = {
'adam': tf.train.AdamOptimizer,
'rmsprop': tf.train.RMSPropOptimizer,
'amsgrad': AMSGrad,
}[args.learning_algo]()
minimize_op, _ = learning.learn(optimizer=optimizer)
logger.info("Settting up test loss")
with tf.name_scope("DeterministicLoss"):
main_loss = learning.loss()
regularization_loss = learning.regularization_loss()
loss_per_sample = learning.loss(
reduce_fn=lambda x: tf.reshape(x, (-1, )))
return correct, labels_node, main_loss, no_labels_llh, minimize_op, class_mpe, \
regularization_loss, loss_per_sample, in_var_mpe
0, 1)) # Can be added manually
print(root.get_num_nodes())
print(root.get_scope())
print(root.is_valid())
SUM_CNT = 8
sum_ls = []
#iv_x = spn.IndicatorLeaf([[0,-1], [-1,-1]] ,num_vars=2, num_vals=2, name="iv_x")
for i in range(SUM_CNT):
iv_x = spn.IndicatorLeaf([[-1], [0]],
num_vars=1,
num_vals=2,
name="iv_x" + str(i))
#for i in range(2):
sum_x = spn.Sum((iv_x, [0, 1]), name="sum_" + str(i))
sum_x.generate_weights(
tf.initializers.constant([random.random(),
random.random()]))
sum_ls.append(sum_x)
#for i in range(2,4):
# sum_x = spn.Sum((iv_x, [0,1]), name="sum_" + str(i))
# sum_x.generate_weights(tf.initializers.constant([random.random(), random.random()]))
# sum_ls.append(sum_x)
LAST_CNT = SUM_CNT
last_node_ls = sum_ls
node_type = 'Sum'
LAYER_CNT = int(math.log(SUM_CNT / 2, 2))
print('Layer CNT', LAYER_CNT)
for l in range(LAYER_CNT):
name="indicator_x")
# Connect first two sums to indicators of first variable
sums_1 = spn.ParallelSums((indicator_leaves, [0, 1]),
num_sums=2,
name="sums_1")
# Connect another two sums to indicators of second variable
sums_2 = spn.ParallelSums((indicator_leaves, [2, 3]),
num_sums=2,
name="sums_2")
# Connect 2 * 2 == 4 product nodes
prods_1 = spn.PermuteProducts(sums_1, sums_2, name="prod_1")
# Connect a root sum
root = spn.Sum(prods_1, name="root")
# Connect a latent indicator
indicator_y = root.generate_latent_indicators(
name="indicator_y") # Can be added manually
# Generate weights
spn.generate_weights(
root,
initializer=tf.initializers.random_uniform()) # Can be added manually
## Inspect
# Inspect
print(root.get_num_nodes())
print(root.get_scope())
def dup_fun_up(inpt,
*args,
conc=None,
tmpl_num_vars=[0],
tmpl_num_vals=[0],
graph_num_vars=[0],
labels=[[]],
tspn=None):
"""
Purely for template spn copying only. Supports template with multiple types of IVs.
Requires that the template SPN contains only one concat node where all inputs go through.
labels: (2D list) variable's numerical label, used to locate the variable's position in the big IVs.
If there are multiple types of IVs, then this should be a 2D list, where each inner
list is the label (starting from 0) for one type of IVs, and each outer list represents
one type of IVs.
"""
# Know what range of indices each variable takes
node, indices = inpt
if node.is_op:
if isinstance(node, spn.Sum):
# [2:] is to skip the weights node and the explicit IVs node for this sum.
return spn.Sum(*args[2:], weights=args[0])
elif isinstance(node, spn.ParSums):
return spn.ParSums(*args[2:],
weights=args[0],
num_sums=tspn._num_mixtures)
elif isinstance(node, spn.Product):
return spn.Product(*args)
elif isinstance(node, spn.PermProducts):
return spn.PermProducts(*args)
elif isinstance(node, spn.Concat):
# The goal is to map from index on the template SPN's concat node to the index on
# the instance SPN's concat node.
# First, be able to tell which type of iv the index has
ranges_tmpl = [
0
] # stores the start (inclusive) index of the range of indices taken by a type of iv on template SPN
ranges_instance = [
0
] # stores the start (inclusive) index of the range of indices taken by a type of iv on instance SPN
for i in range(len(tmpl_num_vars)):
ranges_tmpl.append(ranges_tmpl[-1] +
tmpl_num_vars[i] * tmpl_num_vals[i])
ranges_instance.append(ranges_instance[-1] +
graph_num_vars[i] *
tmpl_num_vals[i])
big_indices = []
for indx in indices:
iv_type = -1
for i, start in enumerate(ranges_tmpl):
if indx < start + tmpl_num_vars[i] * tmpl_num_vals[i]:
iv_type = i
break
if iv_type == -1:
raise ValueError(
"Oops. Something wrong. Index out of range.")
# Then, figure out variable index and offset (w.r.t. template Concat node)
varidx = (indx -
ranges_tmpl[iv_type]) // tmpl_num_vals[iv_type]
offset = (indx - ranges_tmpl[iv_type]
) - varidx * tmpl_num_vals[iv_type]
# THIS IS the actual position of the variable's inputs in the big Concat.
varlabel = labels[iv_type][varidx]
big_indices.append(ranges_instance[iv_type] +
varlabel * tmpl_num_vals[iv_type] +
offset)
return spn.Input(conc, big_indices)
elif isinstance(node, spn.Weights):
return node
else:
raise ValueError(
"Unexpected node %s. We don't intend to deal with IVs here. Please remove them from the concat."
% node)
def _init_struct(self,
sess,
divisions=-1,
num_partitions=1,
partitions=None,
extra_partition_multiplyer=1):
"""
Initialize the structure for training. (private method)
sess: (tf.Session): a session that contains all weights.
**kwargs:
num_partitions (int): number of partitions (children for root node)
If template is EdgeTemplate, then:
divisions (int) number of views per place
extra_partition_multiplyer (int): Used to multiply num_partitions so that more partitions
are tried and ones with higher coverage are picked.
"""
for tspn, template in self._spns:
# remove inputs; this is necessary for the duplication to work - we don't want
# the indicator variables to the template spns because the instance spn has its
# own indicator variable inputs.
tspn._conc_inputs.set_inputs()
# Create vars and maps
self._catg_inputs = spn.IVs(num_vars=len(self._graph.nodes),
num_vals=self._num_vals)
self._conc_inputs = spn.Concat(self._catg_inputs)
self._template_nodes_map = {
} # map from template id to list of node lds
self._node_label_map = {
} # key: node id. Value: a number (0~num_nodes-1)
self._label_node_map = {
} # key: a number (0~num_nodes-1). Value: node id
_i = 0
for nid in self._graph.nodes:
self._node_label_map[nid] = _i
self._label_node_map[_i] = nid
_i += 1
if partitions is None:
"""Try partition the graph `extra_partition_multiplyer` times more than what is asked for. Then pick the top `num_partitions` with the highest
coverage of the main template."""
print(
"Partitioning the graph... (Selecting %d from %d attempts)" %
(num_partitions, extra_partition_multiplyer * num_partitions))
partitioned_results = {}
main_template = self._spns[0][1]
for i in range(extra_partition_multiplyer * num_partitions):
"""Note: here, we only partition with the main template. The results (i.e. supergraph, unused graph) are stored
and will be used later. """
unused_graph, supergraph = self._graph.partition(
main_template,
get_unused=True,
super_node_class=self._super_node_class,
super_edge_class=self._super_edge_class)
if self._template_mode == NodeTemplate.code(): ## NodeTemplate
coverage = len(
supergraph.nodes) * main_template.size() / len(
self._graph.nodes)
partitioned_results[(i, coverage)] = (supergraph,
unused_graph)
used_coverages = set({})
for i, coverage in sorted(partitioned_results,
reverse=True,
key=lambda x: x[1]):
used_coverages.add((i, coverage))
sys.stdout.write("%.3f " % coverage)
if len(used_coverages) >= num_partitions:
break
sys.stdout.write("\n")
"""Keep partitioning the used partitions, and obtain a list of partitions in the same format as the `partitions` parameter"""
partitions = []
for key in used_coverages:
supergraph, unused_graph = partitioned_results[key]
partition = {main_template: supergraph}
# Keep partitioning the unused_graph using smaller templates
for _, template in self._spns[1:]: # skip main template
unused_graph_2nd, supergraph_2nd = unused_graph.partition(
template,
get_unused=True,
super_node_class=self._super_node_class,
super_edge_class=self._super_edge_class)
partition[template] = supergraph_2nd
unused_graph = unused_graph_2nd
partitions.append(partition)
"""Building instance spn"""
print("Building instance spn...")
pspns = []
tspns = {}
for template_spn, template in self._spns:
tspns[template.__name__] = template_spn
"""Making an SPN"""
"""Now, partition the graph, copy structure, and connect self._catg_inputs appropriately to the network."""
# Main template partition
_k = 0
self._partitions = partitions
for _k, partition in enumerate(self._partitions):
print("Partition %d" % (_k + 1))
nodes_covered = set({})
template_spn_roots = []
for template_spn, template in self._spns:
supergraph = partition[template]
print("Will duplicate %s %d times." %
(template.__name__, len(supergraph.nodes)))
template_spn_roots.extend(
NodeTemplateInstanceSpn._duplicate_template_spns(
self, tspns, template, supergraph, nodes_covered))
## TEST CODE: COMMENT OUT WHEN ACTUALLY RUNNING
# original_tspn_root = tspns[template.__name__].root
# duplicated_tspn_root = template_spn_roots[-1]
# original_tspn_weights = sess.run(original_tspn_root.weights.node.get_value())
# duplicated_tspn_weights = sess.run(duplicated_tspn_root.weights.node.get_value())
# print(original_tspn_weights)
# print(duplicated_tspn_weights)
# print(original_tspn_weights == duplicated_tspn_weights)
# import pdb; pdb.set_trace()
assert nodes_covered == self._graph.nodes.keys()
p = spn.Product(*template_spn_roots)
assert p.is_valid()
pspns.append(p) # add spn for one partition
## End for loop ##
# Sum up all
self._root = spn.Sum(*pspns)
assert self._root.is_valid()
self._root.generate_weights(trainable=True)
# initialize ONLY the weights node for the root
sess.run(self._root.weights.node.initialize())
def test_compare_manual_conv(self, log_weights, inference_type):
spn.conf.argmax_zero = True
grid_dims = [2, 2]
nrows, ncols = grid_dims
num_vals = 4
batch_size = 256
num_vars = grid_dims[0] * grid_dims[1]
indicator_leaf = spn.IndicatorLeaf(num_vars=num_vars,
num_vals=num_vals)
num_sums = 32
weights = spn.Weights(num_weights=num_vals,
num_sums=num_sums * num_vars,
initializer=tf.initializers.random_uniform(),
log=log_weights)
weights_per_cell = tf.split(weights.variable,
num_or_size_splits=num_vars)
parsums = []
for row in range(nrows):
for col in range(ncols):
indices = list(
range(row * (ncols * num_vals) + col * num_vals,
row * (ncols * num_vals) + (col + 1) * num_vals))
parsums.append(
spn.ParallelSums((indicator_leaf, indices),
num_sums=num_sums))
parsum_concat = spn.Concat(*parsums, name="ParSumConcat")
convsum = spn.LocalSums(indicator_leaf,
num_channels=num_sums,
weights=weights,
spatial_dim_sizes=grid_dims)
prod00_conv = spn.PermuteProducts(
(convsum, list(range(num_sums))),
(convsum, list(range(num_sums, num_sums * 2))),
name="Prod00")
prod01_conv = spn.PermuteProducts(
(convsum, list(range(num_sums * 2, num_sums * 3))),
(convsum, list(range(num_sums * 3, num_sums * 4))),
name="Prod01")
sum00_conv = spn.ParallelSums(prod00_conv, num_sums=2)
sum01_conv = spn.ParallelSums(prod01_conv, num_sums=2)
prod10_conv = spn.PermuteProducts(sum00_conv,
sum01_conv,
name="Prod10")
conv_root = spn.Sum(prod10_conv)
prod00_pars = spn.PermuteProducts(
(parsum_concat, list(range(num_sums))),
(parsum_concat, list(range(num_sums, num_sums * 2))))
prod01_pars = spn.PermuteProducts(
(parsum_concat, list(range(num_sums * 2, num_sums * 3))),
(parsum_concat, list(range(num_sums * 3, num_sums * 4))))
sum00_pars = spn.ParallelSums(prod00_pars, num_sums=2)
sum01_pars = spn.ParallelSums(prod01_pars, num_sums=2)
prod10_pars = spn.PermuteProducts(sum00_pars, sum01_pars)
parsum_root = spn.Sum(prod10_pars)
node_pairs = [(sum00_conv, sum00_pars), (sum01_conv, sum01_pars),
(conv_root, parsum_root)]
self.assertTrue(conv_root.is_valid())
self.assertTrue(parsum_root.is_valid())
self.assertAllEqual(parsum_concat.get_scope(), convsum.get_scope())
spn.generate_weights(conv_root,
log=log_weights,
initializer=tf.initializers.random_uniform())
spn.generate_weights(parsum_root,
log=log_weights,
initializer=tf.initializers.random_uniform())
convsum.set_weights(weights)
copy_weight_ops = []
parsum_weight_nodes = []
for p, w in zip(parsums, weights_per_cell):
copy_weight_ops.append(tf.assign(p.weights.node.variable, w))
parsum_weight_nodes.append(p.weights.node)
for wc, wp in node_pairs:
copy_weight_ops.append(
tf.assign(wp.weights.node.variable, wc.weights.node.variable))
copy_weights_op = tf.group(*copy_weight_ops)
init_conv = spn.initialize_weights(conv_root)
init_parsum = spn.initialize_weights(parsum_root)
path_conv = spn.MPEPath(value_inference_type=inference_type)
path_conv.get_mpe_path(conv_root)
path_parsum = spn.MPEPath(value_inference_type=inference_type)
path_parsum.get_mpe_path(parsum_root)
indicator_counts_parsum = path_parsum.counts[indicator_leaf]
indicator_counts_convsum = path_conv.counts[indicator_leaf]
weight_counts_parsum = tf.concat(
[path_parsum.counts[w] for w in parsum_weight_nodes], axis=1)
weight_counts_conv = path_conv.counts[weights]
weight_parsum_concat = tf.concat(
[w.variable for w in parsum_weight_nodes], axis=0)
root_val_parsum = parsum_root.get_log_value(
) #path_parsum.value.values[parsum_root]
root_val_conv = conv_root.get_log_value(
) #path_conv.value.values[conv_root]
parsum_counts = path_parsum.counts[parsum_concat]
conv_counts = path_conv.counts[convsum]
indicator_feed = np.random.randint(-1, 2, size=batch_size * num_vars)\
.reshape((batch_size, num_vars))
with tf.Session() as sess:
sess.run([init_conv, init_parsum])
sess.run(copy_weights_op)
indicator_counts_conv_out, indicator_counts_parsum_out = sess.run(
[indicator_counts_convsum, indicator_counts_parsum],
feed_dict={indicator_leaf: indicator_feed})
root_conv_value_out, root_parsum_value_out = sess.run(
[root_val_conv, root_val_parsum],
feed_dict={indicator_leaf: indicator_feed})
weight_counts_conv_out, weight_counts_parsum_out = sess.run(
[weight_counts_conv, weight_counts_parsum],
feed_dict={indicator_leaf: indicator_feed})
weight_value_conv_out, weight_value_parsum_out = sess.run(
[convsum.weights.node.variable, weight_parsum_concat])
parsum_counts_out, conv_counts_out = sess.run(
[parsum_counts, conv_counts],
feed_dict={indicator_leaf: indicator_feed})
parsum_concat_val, convsum_val = sess.run(
[
path_parsum.value.values[parsum_concat],
path_conv.value.values[convsum]
],
feed_dict={indicator_leaf: indicator_feed})
self.assertAllClose(convsum_val, parsum_concat_val)
self.assertAllClose(weight_value_conv_out, weight_value_parsum_out)
self.assertAllClose(root_conv_value_out, root_parsum_value_out)
self.assertAllEqual(indicator_counts_conv_out,
indicator_counts_parsum_out)
self.assertAllEqual(parsum_counts_out, conv_counts_out)
self.assertAllEqual(weight_counts_conv_out, weight_counts_parsum_out)
