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CausalConv3d Documentation

Table of Contents

  1. Introduction
  2. Overview
  3. CausalConv3d Class
  4. Initialization Parameters
  5. Functionality and Usage
  6. Forward Method
  7. Utility Functions
  8. Examples
  9. Example 1: Creating a CausalConv3d Module
  10. Example 2: Using CausalConv3d for Causal Convolution
  11. Additional Information
  12. References and Resources

1. Introduction

Welcome to the documentation for the Zeta library. This comprehensive guide provides detailed information about the Zeta library and its components, focusing on the CausalConv3d class. Before we delve into the details, it's important to understand the purpose and significance of this library.

1.1 Purpose

The Zeta library is designed to simplify the development of deep learning models by offering modular components and utilities. One of these components is the CausalConv3d class, which plays a crucial role in performing causal convolutions on 3D tensors.

1.2 Key Features

  • Causal Convolution: The CausalConv3d class enables causal convolutions on 3D tensors, a vital operation in architectures like ResNet.

  • Seamless Integration: Zeta modules seamlessly integrate with popular deep learning frameworks like PyTorch, making it easy to incorporate them into your projects.

2. Overview

The Zeta library is built with the aim of providing essential building blocks for deep learning model development. One such block is the CausalConv3d class.

2.1 CausalConv3d Class

The CausalConv3d class is a module designed for performing causal convolutions on 3D tensors. It is particularly useful in scenarios where preserving the causality of data is essential, such as in ResNet architectures.

In the following sections, we will explore the CausalConv3d class's definition, initialization parameters, functionality, and usage.

3. CausalConv3d Class

The CausalConv3d class is at the core of Zeta, providing the ability to perform causal convolutions on 3D tensors.

3.1 Initialization Parameters

Here are the initialization parameters for the CausalConv3d class:

  • chan_in (int): The number of input channels in the tensor.

  • chan_out (int): The number of output channels in the tensor after convolution.

  • kernel_size (int or Tuple[int, int, int]): The size of the convolution kernel. It can be a single integer or a tuple specifying the size in three dimensions.

  • pad_mode (str): The padding mode used for the convolution operation.

  • **kwargs (dict): Additional arguments to be passed to the convolution layer.

3.2 Methods

The primary method of the CausalConv3d class is the forward method, which performs the causal convolution operation on input tensors.

4. Functionality and Usage

Let's explore the functionality and usage of the CausalConv3d class.

4.1 Forward Method

The forward method of the CausalConv3d class takes an input tensor and applies causal convolution using a 3D convolutional layer. Here is the parameter:

  • x (Tensor): The input tensor of shape (batch, channels, time, height, width).

The method returns a tensor after performing causal convolution, preserving causality in the temporal dimension.

4.2 Usage Examples

Example 1: Creating a CausalConv3d Module

In this example, we create an instance of the CausalConv3d class with default settings:

causal_conv = CausalConv3d(chan_in=64, chan_out=128, kernel_size=3)

Example 2: Using CausalConv3d for Causal Convolution

Here, we demonstrate how to use the CausalConv3d module for performing causal convolution on an input tensor:

causal_conv = CausalConv3d(chan_in=64, chan_out=128, kernel_size=3)
input_data = torch.randn(1, 64, 32, 32)
output = causal_conv(input_data)
print(output.shape)

5. Utility Functions

The Zeta library also provides a set of utility functions used within the modules. These utility functions, such as exists, default, identity, and more, enhance the modularity and flexibility of the library.

6. Additional Information

Here are some additional tips and information for using the Zeta library and the CausalConv3d class effectively:

  • Experiment with different values for chan_in, chan_out, and kernel_size to control the number of input and output channels and the convolution kernel size.

  • Ensure that the input tensor (x) has the appropriate shape (batch, channels, time, height, width) to perform causal convolution.

  • The pad_mode parameter allows you to specify the padding mode for the convolution operation.

7. References and Resources

For further information and resources related to the Zeta library and deep learning, please refer to the following:

This concludes the documentation for the Zeta library and the CausalConv3d class. You now have a comprehensive understanding of how to use this library and module for your deep learning projects. If you have any further questions or need assistance, please refer to the provided references and resources. Happy modeling with Zeta!