timesead.models.layers.fourier_correlation

Classes

FourierBlock	Base class for all neural network modules.
FourierCrossAttention	Base class for all neural network modules.

Functions

get_frequency_modes(seq_len[, modes, mode_select_method])

get modes on frequency domain:

Module Contents

timesead.models.layers.fourier_correlation.get_frequency_modes(seq_len, modes=64, mode_select_method='random')

get modes on frequency domain: ‘random’ means sampling randomly; ‘else’ means sampling the lowest modes;

class timesead.models.layers.fourier_correlation.FourierBlock(in_channels, out_channels, seq_len, num_heads=8, modes=0, mode_select_method='random')

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

index

scale

weights1

weights2

compl_mul1d(order, x, weights)

forward(q, k, v, mask)

class timesead.models.layers.fourier_correlation.FourierCrossAttention(in_channels, out_channels, seq_len_q, seq_len_kv, modes=64, mode_select_method='random', activation='tanh', policy=0, num_heads=8)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

activation = 'tanh'

in_channels

out_channels

index_q

index_kv

scale

weights1

weights2

compl_mul1d(order, x, weights)

forward(q, k, v, mask)