def test_spn_sampling(self):
# Define SPN
leaf = distributions.Normal(in_features=2 ** 3, out_channels=5, num_repetitions=1)
sum_1 = layers.Sum(in_channels=5, in_features=2 ** 3, out_channels=20, num_repetitions=1)
prd_1 = layers.Product(in_features=2 ** 3, cardinality=2, num_repetitions=1)
sum_2 = layers.Sum(in_channels=20, in_features=2 ** 2, out_channels=20, num_repetitions=1)
prd_2 = layers.Product(in_features=2 ** 2, cardinality=2, num_repetitions=1)
sum_3 = layers.Sum(in_channels=20, in_features=2 ** 1, out_channels=20, num_repetitions=1)
prd_3 = layers.Product(in_features=2 ** 1, cardinality=2, num_repetitions=1)
sum_4 = layers.Sum(in_channels=20, in_features=2 ** 0, out_channels=1, num_repetitions=1)
# Test forward pass
x_test = torch.randn(1, 2 ** 3)
x_test = leaf(x_test)
x_test = sum_1(x_test)
x_test = prd_1(x_test)
x_test = sum_2(x_test)
x_test = prd_2(x_test)
x_test = sum_3(x_test)
x_test = prd_3(x_test)
res = sum_4(x_test)
# Sampling pass
ctx = sum_4.sample(n=1000)
prd_3.sample(context=ctx)
sum_3.sample(context=ctx)
prd_2.sample(context=ctx)
sum_2.sample(context=ctx)
prd_1.sample(context=ctx)
sum_1.sample(context=ctx)
samples = leaf.sample(context=ctx)
def test_sum_as_intermediate_node(self):
"""Check that sum node returns the correct sample indices when used as indermediate node."""
# Some values for the sum layer
in_features = 10
in_channels = 3
out_channels = 5
num_repetitions = 7
n = 2
parent_indices = torch.randint(out_channels, size=(n, in_features))
# Create sum layer
sum_layer = layers.Sum(
in_features=in_features, in_channels=in_channels, out_channels=out_channels, num_repetitions=num_repetitions
)
# Choose `in_features` number of random indexes from 0 to in_channels-1 which will have probability of 1.0 in
# the sum layer weight tensor
rand_indxs = torch.randint(in_channels, size=(in_features, num_repetitions))
rep_idxs = torch.randint(num_repetitions, size=(n,))
# Artificially set sum weights (probabilities) to 1.0
weights = torch.zeros(in_features, in_channels, out_channels, num_repetitions)
for r in range(num_repetitions):
weights[range(in_features), rand_indxs[:, r], :, r] = 1.0
sum_layer.weights = nn.Parameter(torch.log(weights))
# Perform sampling
ctx = SamplingContext(n=n, parent_indices=parent_indices, repetition_indices=rep_idxs)
sum_layer.sample(context=ctx)
# Assert that the sample indexes are those where the weights were set to 1.0
for i in range(n):
self.assertTrue((rand_indxs[:, rep_idxs[i]] == ctx.parent_indices[i, :]).all())
def create_pytorch_spn(n_feats):
# Create SPN layers
gauss = Normal(multiplicity=2, in_features=n_feats, in_channels=1)
prod1 = layers.Product(in_features=n_feats, cardinality=2)
sum1 = layers.Sum(in_features=n_feats / 2, in_channels=2, out_channels=1)
prod2 = layers.Product(in_features=n_feats / 2, cardinality=n_feats // 2)
# Stack SPN layers
device = torch.device("cuda:0")
pytorch_spn = nn.Sequential(gauss, prod1, sum1, prod2).to(device)
return pytorch_spn
def test_sum_shape_as_root_node(self):
"""Check that the sum node has the correct sampling shape when used as root."""
n = 5
num_repetitions = 1
for in_channels in [1, 5, 10]:
for in_features in [1, 5, 10]:
sum_layer = layers.Sum(
in_channels=in_channels, out_channels=1, in_features=in_features, num_repetitions=num_repetitions
)
ctx = sum_layer.sample(n=n)
self.assertTrue(ctx.parent_indices.shape[0] == n)
self.assertTrue(ctx.parent_indices.shape[1] == in_features)
def test_sum_layer(self):
"""Test the forward pass of a sum layer"""
# Setup layer
in_channels = 8
out_channels = 7
in_features = 3
num_repetitions = 5
sum_layer = layers.Sum(in_channels=in_channels,
out_channels=out_channels,
in_features=in_features,
num_repetitions=num_repetitions)
w = torch.rand(in_features, in_channels, out_channels, num_repetitions)
# Set the sum layer parameters
sum_layer.weights = nn.Parameter(w)
# Apply softmax once again since Sum forward pass uses F.log_softmax internally to project random weights
# back into valid ranges
w = F.softmax(w, dim=1)
# Setup test input
batch_size = 16
x = torch.rand(size=(batch_size, in_features, in_channels,
num_repetitions))
# Expected outcome
expected_result = torch.zeros(batch_size, in_features, out_channels,
num_repetitions)
for n in range(batch_size):
for d in range(in_features):
for oc in range(out_channels):
for r in range(num_repetitions):
expected_result[n, d, oc,
r] = x[n, d, :, r] @ w[d, :, oc, r]
# Do forward pass: apply log as sum layer operates in log space. Exp() afterwards to make it comparable to the
# expected result
result = sum_layer(x.log()).exp()
# Run assertions
self.assertTrue(result.shape[0] == batch_size)
self.assertTrue(result.shape[1] == in_features)
self.assertTrue(result.shape[2] == out_channels)
self.assertTrue(result.shape[3] == num_repetitions)
self.assertTrue(((result - expected_result).abs() < 1e-6).all())
def test_spn_mpe(self):
# Define SPN
leaf = distributions.Normal(in_features=2**3,
out_channels=5,
num_repetitions=1)
sum_1 = layers.Sum(in_channels=5,
in_features=2**3,
out_channels=20,
num_repetitions=1)
prd_1 = layers.Product(in_features=2**3,
cardinality=2,
num_repetitions=1)
sum_2 = layers.Sum(in_channels=20,
in_features=2**2,
out_channels=20,
num_repetitions=1)
prd_2 = layers.Product(in_features=2**2,
cardinality=2,
num_repetitions=1)
sum_3 = layers.Sum(in_channels=20,
in_features=2**1,
out_channels=20,
num_repetitions=1)
prd_3 = layers.Product(in_features=2**1,
cardinality=2,
num_repetitions=1)
sum_4 = layers.Sum(in_channels=20,
in_features=2**0,
out_channels=1,
num_repetitions=1)
sum_1._enable_input_cache()
sum_2._enable_input_cache()
sum_3._enable_input_cache()
sum_4._enable_input_cache()
# Test forward pass
x_test = torch.randn(1, 2**3)
x_test = leaf(x_test)
x_test = sum_1(x_test)
x_test = prd_1(x_test)
x_test = sum_2(x_test)
x_test = prd_2(x_test)
x_test = sum_3(x_test)
x_test = prd_3(x_test)
res = sum_4(x_test)
ctx = SamplingContext(n=x_test.shape[0], is_mpe=True)
sum_4.sample(context=ctx)
prd_3.sample(context=ctx)
sum_3.sample(context=ctx)
prd_2.sample(context=ctx)
sum_2.sample(context=ctx)
prd_1.sample(context=ctx)
sum_1.sample(context=ctx)
# Should be the same
mpe_1 = leaf.sample(context=ctx)
mpe_2 = leaf.sample(context=ctx)
mpe_3 = leaf.sample(context=ctx)
self.assertTrue(((mpe_1 - mpe_2).abs() < 1e-6).all())
self.assertTrue(((mpe_2 - mpe_3).abs() < 1e-6).all())
