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Add test_decagon_layer_04().

master
Stanislaw Adaszewski 4 years ago
parent
commit
02bbfc4958
2 changed files with 43 additions and 9 deletions
  1. +4
    -3
      src/decagon_pytorch/layer.py
  2. +39
    -6
      tests/decagon_pytorch/test_layer.py

+ 4
- 3
src/decagon_pytorch/layer.py View File

@@ -21,7 +21,7 @@
import torch
from .convolve import SparseDropoutGraphConvActivation
from .convolve import DropoutGraphConvActivation
from .data import Data
from typing import List, \
Union, \
@@ -125,7 +125,7 @@ class DecagonLayer(Layer):
for (nt_row, nt_col), relation_types in self.data.relation_types.items():
for rel in relation_types:
conv = SparseDropoutGraphConvActivation(self.input_dim[nt_col],
conv = DropoutGraphConvActivation(self.input_dim[nt_col],
self.output_dim[nt_row], rel.adjacency_matrix,
self.keep_prob, self.rel_activation)
self.next_layer_repr[nt_row].append((conv, nt_col))
@@ -133,7 +133,7 @@ class DecagonLayer(Layer):
if nt_row == nt_col:
continue
conv = SparseDropoutGraphConvActivation(self.input_dim[nt_row],
conv = DropoutGraphConvActivation(self.input_dim[nt_row],
self.output_dim[nt_col], rel.adjacency_matrix.transpose(0, 1),
self.keep_prob, self.rel_activation)
self.next_layer_repr[nt_col].append((conv, nt_row))
@@ -149,6 +149,7 @@ class DecagonLayer(Layer):
self.next_layer_repr[i]
]
next_layer_repr[i] = sum(next_layer_repr[i])
next_layer_repr[i] = torch.nn.functional.normalize(next_layer_repr[i], p=2, dim=1)
print('next_layer_repr:', next_layer_repr)
# next_layer_repr = list(map(sum, next_layer_repr))


+ 39
- 6
tests/decagon_pytorch/test_layer.py View File

@@ -4,7 +4,9 @@ from decagon_pytorch.layer import InputLayer, \
from decagon_pytorch.data import Data
import torch
import pytest
from decagon_pytorch.convolve import SparseDropoutGraphConvActivation
from decagon_pytorch.convolve import SparseDropoutGraphConvActivation, \
SparseMultiDGCA, \
DropoutGraphConvActivation
def _some_data():
@@ -136,9 +138,9 @@ def test_decagon_layer_03():
assert len(d_layer.next_layer_repr) == 2
assert len(d_layer.next_layer_repr[0]) == 2
assert len(d_layer.next_layer_repr[1]) == 4
assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
assert all(map(lambda a: isinstance(a[0], DropoutGraphConvActivation),
d_layer.next_layer_repr[0]))
assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
assert all(map(lambda a: isinstance(a[0], DropoutGraphConvActivation),
d_layer.next_layer_repr[1]))
assert all(map(lambda a: a[0].output_dim == 32,
d_layer.next_layer_repr[0]))
@@ -147,7 +149,38 @@ def test_decagon_layer_03():
def test_decagon_layer_04():
d = _some_data_with_interactions()
# check if it is equivalent to MultiDGCA, as it should be
d = Data()
d.add_node_type('Dummy', 100)
d.add_relation_type('Dummy Relation', 0, 0,
torch.rand((100, 100), dtype=torch.float32).round().to_sparse())
in_layer = OneHotInputLayer(d)
d_layer = DecagonLayer(d, in_layer, output_dim=32)
_ = d_layer()
multi_dgca = SparseMultiDGCA([10], 32,
[r.adjacency_matrix for r in d.relation_types[0, 0]],
keep_prob=1., activation=lambda x: x)
d_layer = DecagonLayer(d, in_layer, output_dim=32,
keep_prob=1., rel_activation=lambda x: x,
layer_activation=lambda x: x)
assert isinstance(d_layer.next_layer_repr[0][0][0],
DropoutGraphConvActivation)
weight = d_layer.next_layer_repr[0][0][0].graph_conv.weight
assert isinstance(weight, torch.Tensor)
assert len(multi_dgca.sparse_dgca) == 1
assert isinstance(multi_dgca.sparse_dgca[0], SparseDropoutGraphConvActivation)
multi_dgca.sparse_dgca[0].sparse_graph_conv.weight = weight
out_d_layer = d_layer()
out_multi_dgca = multi_dgca(in_layer())
assert isinstance(out_d_layer, list)
assert len(out_d_layer) == 1
assert torch.all(out_d_layer[0] == out_multi_dgca)

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