timesead.models.layers.causal_conv

Classes

CausalConv1d

Base class for all neural network modules.

Module Contents

class timesead.models.layers.causal_conv.CausalConv1d(in_channels: int, out_channels: int, kernel_size: torch.nn.common_types._size_1_t, stride: torch.nn.common_types._size_1_t = 1, dilation: torch.nn.common_types._size_1_t = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', device=None, dtype=None)

Bases: torch.nn.Conv1d

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.

Parameters:

• in_channels (int)

• out_channels (int)

• kernel_size (torch.nn.common_types._size_1_t)

• stride (torch.nn.common_types._size_1_t)

• dilation (torch.nn.common_types._size_1_t)

• groups (int)

• bias (bool)

• padding_mode (str)

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

causal_padding

forward(input: torch.Tensor) → torch.Tensor

Parameters:

input (torch.Tensor)

Return type:

torch.Tensor