def test_log_vector_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(2, size=30),
)).astype("float64")
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler(maxTaskSize=5).log_likelihood(spn, inputs, supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(np.isclose(np.exp(results), np.exp(reference)))
def test_log_vector_categorical():
# Construct a minimal SPN
c1 = Categorical(p=[0.35, 0.55, 0.1], scope=0)
c2 = Categorical(p=[0.25, 0.625, 0.125], scope=1)
c3 = Categorical(p=[0.5, 0.2, 0.3], scope=2)
c4 = Categorical(p=[0.6, 0.15, 0.25], scope=3)
c5 = Categorical(p=[0.7, 0.11, 0.19], scope=4)
c6 = Categorical(p=[0.8, 0.14, 0.06], scope=5)
p = Product(children=[c1, c2, c3, c4, c5, c6])
# Randomly sample input values.
inputs = np.column_stack((
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
)).astype("int32")
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler().log_likelihood(p, inputs, supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(p, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(np.isclose(np.exp(results), np.exp(reference)))
def test_cpu_gaussian():
# Construct a minimal SPN using two Gaussian leaves.
g1 = Gaussian(mean=0.5, stdev=1, scope=0)
g2 = Gaussian(mean=0.125, stdev=0.25, scope=1)
g3 = Gaussian(mean=0.345, stdev=0.24, scope=2)
g4 = Gaussian(mean=0.456, stdev=0.1, scope=3)
g5 = Gaussian(mean=0.94, stdev=0.48, scope=4)
g6 = Gaussian(mean=0.56, stdev=0.42, scope=5)
g7 = Gaussian(mean=0.76, stdev=0.14, scope=6)
g8 = Gaussian(mean=0.32, stdev=0.8, scope=7)
g9 = Gaussian(mean=0.58, stdev=0.9, scope=8)
g10 = Gaussian(mean=0.14, stdev=0.2, scope=9)
p = Product(children=[g1, g2, g3, g4, g5, g6, g7, g8, g9, g10])
# Randomly sample input values from the two Gaussian (normal) distributions.
inputs = np.column_stack(
(np.random.normal(0.5, 1, 30), np.random.normal(0.125, 0.25, 30),
np.random.normal(0.345, 0.24, 30), np.random.normal(0.456, 0.1, 30),
np.random.normal(0.94, 0.48, 30), np.random.normal(0.56, 0.42, 30),
np.random.normal(0.76, 0.14, 30), np.random.normal(0.32, 0.8, 30),
np.random.normal(0.58, 0.9,
30), np.random.normal(0.14, 0.2,
30))).astype("float64")
# Insert some NaN in random places into the input data.
inputs.ravel()[np.random.choice(inputs.size, 10, replace=False)] = np.nan
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler(computeInLogSpace=False,
vectorize=False).log_likelihood(p,
inputs,
supportMarginal=True,
batchSize=10)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(p, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(
np.isclose(np.exp(results), np.exp(reference)))
def test_vector_fashion_mnist():
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
model = BinaryDeserializer(
os.path.join(
scriptPath,
"nltcs_100_200_2_10_8_8_1_True.bin")).deserialize_from_file()
spn = model.root
inputs = np.genfromtxt(os.path.join(scriptPath, "input.csv"),
delimiter=",",
dtype="float64")
reference = np.genfromtxt(os.path.join(
scriptPath, "nltcs_100_200_2_10_8_8_1_True_output.csv"),
delimiter=",",
dtype="float64")
reference = reference.reshape(10000)
# Compile the kernel.
compiler = CPUCompiler(vectorize=True, computeInLogSpace=True)
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
# This file is part of the SPNC project under the Apache License v2.0 by the
# Embedded Systems and Applications Group, TU Darmstadt.
# For the full copyright and license information, please view the LICENSE
# file that was distributed with this source code.
# SPDX-License-Identifier: Apache-2.0
# ==============================================================================
import numpy as np
import os
import pytest
import time
from spnc.cpu import CPUCompiler
from xspn.serialization.binary.BinarySerialization import BinaryDeserializer
@pytest.mark.skipif(not CPUCompiler.isVectorizationSupported(),
reason="CPU vectorization not supported")
def test_vector_slp_fashion_mnist():
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
model = BinaryDeserializer(
os.path.join(scriptPath, "fashion_mnist_200_100_4_5_10_9_1_True.bin")
).deserialize_from_file()
spn = model.root
inputs = np.genfromtxt(os.path.join(scriptPath, "input.csv"),
delimiter=",",
dtype="float64")
reference = np.genfromtxt(os.path.join(
# file that was distributed with this source code.
# SPDX-License-Identifier: Apache-2.0
# ==============================================================================
import numpy as np
from spn.structure.Base import Product, Sum
from spn.structure.leaves.histogram.Histograms import Histogram
from spn.algorithms.Inference import log_likelihood
from spnc.cpu import CPUCompiler
import pytest
@pytest.mark.skipif(not CPUCompiler.isVectorizationSupported(), reason="CPU vectorization not supported")
def test_log_vector_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(2, size=30),
)).astype("float64")
