def __init__(self, in_channels, out_channels, activation='relu', dropout=0):
super().__init__()
channels = [in_channels] + out_channels
self.layers = nn.ModuleList()
for i in range(1, len(channels)):
if dropout > 0.001:
self.layers.append(nn.Dropout(p=dropout))
self.layers.append(nn.Conv2d(channels[i-1], channels[i], kernel_size=1))
self.layers.append(nn.BatchNorm2d(channels[i]))
self.layers.append(activation_factory(activation))
def __init__(self,
in_channels,
out_channels,
A_binary,
num_scales,
window_size,
disentangled_agg=True,
use_Ares=True,
residual=False,
dropout=0,
activation='relu'):
super().__init__()
self.num_scales = num_scales
self.window_size = window_size
self.use_Ares = use_Ares
A = self.build_spatial_temporal_graph(A_binary, window_size)
if disentangled_agg:
A_scales = [k_adjacency(A, k, with_self=True) for k in range(num_scales)]
A_scales = np.concatenate([normalize_adjacency_matrix(g) for g in A_scales])
else:
# Self-loops have already been included in A
A_scales = [normalize_adjacency_matrix(A) for k in range(num_scales)]
A_scales = [np.linalg.matrix_power(g, k) for k, g in enumerate(A_scales)]
A_scales = np.concatenate(A_scales)
self.A_scales = torch.Tensor(A_scales)
self.V = len(A_binary)
if use_Ares:
self.A_res = nn.init.uniform_(nn.Parameter(torch.randn(self.A_scales.shape)), -1e-6, 1e-6)
else:
self.A_res = torch.tensor(0)
self.mlp = MLP(in_channels * num_scales, [out_channels], dropout=dropout, activation='linear')
# Residual connection
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels):
self.residual = lambda x: x
else:
self.residual = MLP(in_channels, [out_channels], activation='linear')
self.act = activation_factory(activation)
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
dilations=[1, 2, 3, 4],
residual=True,
residual_kernel_size=1,
activation='relu'):
super().__init__()
assert out_channels % (
len(dilations) +
2) == 0, '# out channels should be multiples of # branches'
# Multiple branches of temporal convolution
self.num_branches = len(dilations) + 2
branch_channels = out_channels // self.num_branches
# Temporal Convolution branches
self.branches = nn.ModuleList([
nn.Sequential(
nn.Conv2d(in_channels,
branch_channels,
kernel_size=1,
padding=0),
nn.BatchNorm2d(branch_channels),
activation_factory(activation),
TemporalConv(branch_channels,
branch_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation),
) for dilation in dilations
])
# Additional Max & 1x1 branch
self.branches.append(
nn.Sequential(
nn.Conv2d(in_channels,
branch_channels,
kernel_size=1,
padding=0), nn.BatchNorm2d(branch_channels),
activation_factory(activation),
nn.MaxPool2d(kernel_size=(3, 1),
stride=(stride, 1),
padding=(1, 0)), nn.BatchNorm2d(branch_channels)))
self.branches.append(
nn.Sequential(
nn.Conv2d(in_channels,
branch_channels,
kernel_size=1,
padding=0,
stride=(stride, 1)),
nn.BatchNorm2d(branch_channels)))
# Residual connection
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels) and (stride == 1):
self.residual = lambda x: x
else:
self.residual = TemporalConv(in_channels,
out_channels,
kernel_size=residual_kernel_size,
stride=stride)
self.act = activation_factory(activation)