timesead.models.layers.conv_lstm

Classes

ConvLSTMCell	Base class for all neural network modules.
ConvLSTM	Base class for all neural network modules.

Module Contents

class timesead.models.layers.conv_lstm.ConvLSTMCell(in_channels: int, hid_channels: int, kernel_size: int | Tuple[int, int], spatial_size: Tuple[int, int])

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.

Parameters:

• in_channels (int)

• hid_channels (int)

• kernel_size (Union[int, Tuple[int, int]])

• spatial_size (Tuple[int, int])

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

hid_channels

x2h

h2h

c2c

reset_parameters()

forward(x, h, c)

class timesead.models.layers.conv_lstm.ConvLSTM(*args, **kwargs)

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.

lstm

forward(x, hidden=None, memory=None)

input shape: (T, B, C, H, W)