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# Implementation taken and modified from
# https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries, which was created
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# Implementation of causal CNNs partly taken and modified from
# https://github.com/locuslab/TCN/blob/master/TCN/tcn.py, originally created
# with the following license.
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import torch
import torch.nn
from .tcn import TCNBlock
from torch.distributions.normal import Normal
[docs]class DistributionalTCN(torch.nn.Module):
"""
Distributional Temporal Convolutional Network: a TCN to learn a time-
varying distribution.
Composed of a sequence of causal convolution blocks.
Takes as input a three-dimensional tensor (`B`, `C`, `L`) where `B` is the
batch size, `C` is the number of input channels, and `L` is the length of
the input. Outputs a three-dimensional tensor (`B`, `C_out`, `L`).
Args:
in_channels : Number of input channels, typically the dimensionality of the time series
out_channels : Number of output channels, typically the number of parameters in the time series distribution
kernel_size : Kernel size of the applied non-residual convolutions.
channels : Number of channels processed in the network and of output channels,
typically equal to out_channels for simplicity, expand for better performance.
layers : Depth of the network.
bias : If True, adds a learnable bias to the convolutions.
fwd_time : If True the network is the relation relation if from past to future (forward),
if False, the relation from future to past (backward).
output_distr: Distribution whose parameters will be specified by the network output
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
channels: int,
layers: int,
bias: bool = True,
fwd_time: bool = True,
output_distr=Normal(torch.tensor([0.0]), torch.tensor([1.0])),
):
super().__init__()
self.out_channels = out_channels
# Temporal Convolution Network
layers = int(layers)
net_layers = [] # List of sequential TCN blocks
dilation_size = 1 # Initial dilation size
for i in range(layers):
in_channels_block = in_channels if i == 0 else channels
net_layers.append(
TCNBlock(
in_channels=in_channels_block,
out_channels=channels,
kernel_size=kernel_size,
dilation=dilation_size,
bias=bias,
fwd_time=fwd_time,
final=False,
)
)
dilation_size *= 2 # Doubles the dilation size at each step
# Last layer
net_layers.append(
TCNBlock(
in_channels=channels,
out_channels=self.out_channels,
kernel_size=kernel_size,
dilation=dilation_size,
bias=bias,
fwd_time=fwd_time,
final=True,
)
)
self.network = torch.nn.Sequential(*net_layers)
self.output_distr = output_distr
[docs] def forward(self, x):
net_out = self.network(x)
net_out_final = net_out[..., -1].squeeze()
self.output_distr(net_out_final)
return self.output_distr