def test_simple_resnet_graph():
simple_archi: Architecture = Architecture(
preprocess=lambda x: x,
layers=[
LinearLayer(4, 4),
LinearLayer(4, 4),
LinearLayer(4, 4),
LinearLayer(4, 10),
SoftMaxLayer(),
],
layer_links=[(-1, 0), (0, 1), (1, 2), (1, 3), (2, 3), (3, 4)],
)
simple_archi.build_matrices()
simple_example = torch.ones(4)
graph = Graph.from_architecture_and_data_point(simple_archi, simple_example)
adjacency_matrix = graph.get_adjacency_matrix().todense()
assert np.shape(adjacency_matrix) == (26, 26)
assert len(graph.get_edge_list()) == 128
assert len(np.where(adjacency_matrix > 0)[0]) == 128 * 2
print(graph.get_edge_list())
print(simple_archi.get_pre_softmax_idx())
),
BatchNorm2d(channels=512, activ=F.relu),
ConvLayer(
in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
BatchNorm2d(channels=512),
ReluLayer(),
# End part
AvgPool2dLayer(kernel_size=4),
LinearLayer(512, 10),
SoftMaxLayer(),
# Layer to reduce dimension in residual blocks
ConvLayer(
in_channels=64,
out_channels=128,
kernel_size=1,
stride=2,
padding=0,
bias=False,
),
ConvLayer(
in_channels=128,
out_channels=256,
kernel_size=1,
stride=2,
padding=0,