`TimeUpSample2x` Documentation¶

Table of Contents¶

Introduction
Overview
TimeUpSample2x Class
Initialization Parameters
Functionality and Usage
Forward Method
Utility Functions
Examples
Example 1: Creating a TimeUpSample2x Module
Example 2: Using TimeUpSample2x for Upsampling
Additional Information
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 TimeUpSample2x 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 TimeUpSample2x class, which plays a crucial role in upscaling the time dimension of tensors.

1.2 Key Features¶

Time Dimension Upsampling: The TimeUpSample2x class allows you to efficiently increase the temporal resolution of your data, which is particularly valuable in various sequential data tasks.
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 TimeUpSample2x class.

2.1 `TimeUpSample2x` Class¶

The TimeUpSample2x class is a module designed for upscaling the time dimension of 3D tensors. It is useful in scenarios where increasing the temporal resolution of the data is required.

In the following sections, we will explore the TimeUpSample2x class's definition, initialization parameters, functionality, and usage.

3. TimeUpSample2x Class ¶

The TimeUpSample2x class is at the core of Zeta, providing the ability to increase the temporal resolution of tensors.

3.1 Initialization Parameters ¶

Here are the initialization parameters for the TimeUpSample2x class:

dim (int): The number of input channels in the tensor.
dim_out (int, optional): The number of output channels in the tensor after upsampling. If not specified, it defaults to the same as dim.

3.2 Methods¶

The primary method of the TimeUpSample2x class is the forward method, which performs the time dimension upsampling operation on input tensors.

4. Functionality and Usage ¶

Let's explore the functionality and usage of the TimeUpSample2x class.

4.1 Forward Method ¶

The forward method of the TimeUpSample2x class takes an input tensor and applies time dimension upsampling using a convolution operation. Here is the parameter:

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

The method returns an upsampled tensor of shape (batch, output_channels, time, height, width).

4.2 Usage Examples ¶

Example 1: Creating a TimeUpSample2x Module ¶

In this example, we create an instance of the TimeUpSample2x class with default settings:

upsample = TimeUpSample2x(dim=64)

Example 2: Using TimeUpSample2x for Upsampling ¶

Here, we demonstrate how to use the TimeUpSample2x module for upsampling an input tensor:

upsample = TimeUpSample2x(dim=64)
input_data = torch.randn(1, 64, 32, 32)
output = upsample(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, identity, divisible_by, 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 TimeUpSample2x class effectively:

Experiment with different values for the dim and dim_out parameters to control the number of channels in the output tensor.
Ensure that the input tensor (x) has the appropriate shape (batch, channels, time, height, width).

7. References and Resources ¶

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

Zeta GitHub Repository: The official Zeta repository for updates and contributions.
PyTorch Official Website: The official website for PyTorch, the deep learning framework used in Zeta.

This concludes the documentation for the Zeta library and the TimeUpSample2x 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!

TimeUpSample2x Documentation¶