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Add universal convolution units that can handle both sparse and dense inputs.

master
Stanislaw Adaszewski 4 years ago
parent
commit
69e8ce5081
2 changed files with 67 additions and 0 deletions
  1. +43
    -0
      src/decagon_pytorch/convolve.py
  2. +24
    -0
      tests/decagon_pytorch/test_convolve.py

+ 43
- 0
src/decagon_pytorch/convolve.py View File

@@ -301,3 +301,46 @@ class DenseMultiDGCA(torch.nn.Module):
out += f(x[i])
out = torch.nn.functional.normalize(out, p=2, dim=1)
return out


class GraphConv(torch.nn.Module):
"""Convolution layer for sparse AND dense inputs."""
def __init__(self, in_channels: int, out_channels: int,
adjacency_matrix: torch.Tensor, **kwargs) -> None:
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.weight = init_glorot(in_channels, out_channels)
self.adjacency_matrix = adjacency_matrix


def forward(self, x: torch.Tensor) -> torch.Tensor:
x = torch.sparse.mm(x, self.weight) \
if x.is_sparse \
else torch.mm(x, self.weight)
x = torch.sparse.mm(self.adjacency_matrix, x) \
if self.adjacency_matrix.is_sparse \
else torch.mm(self.adjacency_matrix, x)
return x


class DropoutGraphConvActivation(torch.nn.Module):
def __init__(self, input_dim: int, output_dim: int,
adjacency_matrix: torch.Tensor, keep_prob: float=1.,
activation: Callable[[torch.Tensor], torch.Tensor]=torch.nn.functional.relu,
**kwargs) -> None:
super().__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.adjacency_matrix = adjacency_matrix
self.keep_prob = keep_prob
self.activation = activation
self.graph_conv = GraphConv(input_dim, output_dim, adjacency_matrix)

def forward(self, x: torch.Tensor) -> torch.Tensor:
x = dropout_sparse(x, self.keep_prob) \
if x.is_sparse \
else dropout(x, self.keep_prob)
x = self.graph_conv(x)
x = self.activation(x)
return x

+ 24
- 0
tests/decagon_pytorch/test_convolve.py View File

@@ -199,6 +199,18 @@ def teardown_function(fun):
setup_function.old_dropout
def flexible_dropout_graph_conv_activation_torch(keep_prob=1.):
torch.random.manual_seed(0)
latent, adjacency_matrices = prepare_data()
latent = torch.tensor(latent).to_sparse()
adj_mat = adjacency_matrices[0]
adj_mat = torch.tensor(adj_mat).to_sparse()
conv = decagon_pytorch.convolve.DropoutGraphConvActivation(10, 10,
adj_mat, keep_prob=keep_prob)
latent = conv(latent)
return latent
def test_dropout_graph_conv_activation():
for i in range(11):
keep_prob = i/10.
@@ -214,10 +226,22 @@ def test_dropout_graph_conv_activation():
latent_sparse = latent_sparse.detach().numpy()
print('latent_sparse:', latent_sparse)
latent_flex = flexible_dropout_graph_conv_activation_torch(keep_prob)
latent_flex = latent_flex.detach().numpy()
print('latent_flex:', latent_flex)
nonzero = (latent_dense != 0) & (latent_sparse != 0)
assert np.all(latent_dense[nonzero] == latent_sparse[nonzero])
nonzero = (latent_dense != 0) & (latent_flex != 0)
assert np.all(latent_dense[nonzero] == latent_flex[nonzero])
nonzero = (latent_sparse != 0) & (latent_flex != 0)
assert np.all(latent_sparse[nonzero] == latent_flex[nonzero])
def test_multi_dgca():
keep_prob = .5


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