`PatchEmbeddings` Documentation

1. Introduction

Welcome to the Zeta documentation! In this documentation, we will explore the PatchEmbeddings class, which is part of the Zeta library. This class plays a crucial role in the field of computer vision, particularly in the context of image processing and deep learning. This documentation aims to provide a comprehensive understanding of its purpose, functionality, and usage.

2. Purpose and Functionality

The PatchEmbeddings class serves as a fundamental component for processing images in deep learning models, such as transformers and convolutional neural networks (CNNs). Its primary functionalities include:

Patch Embedding

Image Segmentation: It segments an input image into smaller patches, which are then individually processed by the neural network.
Dimensionality Transformation: It transforms the dimensionality of each patch, preparing them for further processing.
Normalization: It applies layer normalization to the patch embeddings for improved training stability.

Image-to-Sequence Transformation

Reshaping: The class reshapes the image patches into a sequence of vectors suitable for input to models like transformers.
Linear Projection: It uses a linear layer to project the patch embeddings into the desired output dimension.

Versatility

Configurability: You can configure the input and output dimensions, allowing flexibility for various model architectures.
Normalization Control: It provides control over the layer normalization applied to the embeddings.

3. Class: `PatchEmbeddings`

The PatchEmbeddings class has the following signature:

class PatchEmbeddings(nn.Module):
    def __init__(
        self, 
        dim_in, 
        dim_out, 
        seq_len
    )

    def forward(self, x)

Parameters

dim_in (int): The input dimension of the image patches.
dim_out (int): The output dimension after embedding the patches.
seq_len (int): The length of the sequence after patching the image.

4. Usage Examples

Let's explore how to use the PatchEmbeddings class effectively in various scenarios.

Using the `PatchEmbeddings` Class

Here's how to use the PatchEmbeddings class to embed image patches:

import torch

from zeta.vision import PatchEmbeddings

# Define the input image properties
dim_in = 3  # Input dimension of image patches (e.g., 3 for RGB images)
dim_out = 64  # Output dimension after embedding
seq_len = 16  # Length of the sequence after patching the image

# Create an instance of PatchEmbeddings
patch_embed = PatchEmbeddings(dim_in, dim_out, seq_len)

# Create a random input image tensor (batch_size, channels, height, width)
image = torch.randn(32, dim_in, 224, 224)  # Example input image with 32 samples

# Apply patch embedding
embedded_patches = patch_embed(image)

# Print the embedded patches
print(embedded_patches)

5. Additional Information

Here are some additional notes and tips related to the PatchEmbeddings class:

Image Patching: Patching images is a common technique used to process large images in deep learning models.
Normalization: The application of layer normalization helps stabilize training and improve convergence.
Dimensionality Transformation: Patch embeddings are essential for converting spatial information in images into sequences suitable for neural networks.
Versatile Usage: The PatchEmbeddings class can be used in various vision-based deep learning architectures.

6. References

For further information on image patching, layer normalization, and related concepts, you can refer to the following resources:

Vision Transformers (ViT) - Paper - The original research paper introducing Vision Transformers.
PyTorch Documentation - Official PyTorch documentation for related functions and modules.

This documentation provides a comprehensive overview of the Zeta library's PatchEmbeddings class. It aims to help you understand the purpose, functionality, and usage of this class for image patch embedding, which is a crucial step in various computer vision applications and deep learning models.

PatchEmbeddings Documentation

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