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visionattention

VisionAttention

Base class for self-attention on input tensor.

The VisionAttention module is designed to perform self-attention on the input tensor. The module is part of the larger nn package in the PyTorch framework and can be applied to various neural network architectures that require attention mechanisms for vision-based tasks.

Overview and Introduction

Attention mechanisms are a vital component of modern deep learning architectures that require the model to focus on different parts of the input data differently. This is especially important in computer vision tasks where the model needs to pay greater attention to specific features within an image. The VisionAttention module enables self-attention, allowing the model to perform computationally-efficient weighting of inputs.

Class Definition and Parameters

The VisionAttention class requires the following parameters to be passed: - dim (int): The input dimension of the tensor. - heads (int, optional): The number of attention heads. Defaults to 8. - dim_head (int, optional): The dimension of each attention head. Defaults to 64. - dropout (float, optional): The dropout probability. Defaults to 0.0.

The data types and default values for the parameters are strictly enforced for creating an instance of the VisionAttention module.

Implementing VisionAttention

The forward function of the VisionAttention module is defined to perform the forward pass of the self-attention. It takes a tensor x as input and applies the self-attention mechanism, returning the output tensor after self-attention.

Usage and Examples

The VisionAttention module can be seamlessly integrated into various neural network architectures. Below are three examples demonstrating the usage of each instance:

Example 1: Single Tensor Input

import torch
from torch import nn

from zeta.nn import VisionAttention

# Create a sample input tensor
x = torch.randn(1, 3, 32, 32)

# Initialize the VisionAttention module
model = VisionAttention(dim=32, heads=8, dim_head=64, dropout=0.0)

# Perform self-attention on the input tensor
out = model(x)

# Print the output
print(out)

Example 2: Integrated with an Existing Model

import torch
from torch import nn

from zeta.nn import VisionAttention


# Define a custom neural network architecture
class CustomModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = VisionAttention(dim=64, heads=16, dim_head=128, dropout=0.1)
        self.decoder = nn.Linear(128, 10)

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x


# Create an instance of the custom model
custom_model = CustomModel()

# Generate a sample input
input_tensor = torch.randn(1, 64, 64, 3)

# Perform a forward pass through the model
output = custom_model(input_tensor)

# Print the output
print(output)

Example 3: Fine-Tuning Hyperparameters

import torch
import torch.nn as nn

# Create a sample input tensor
x = torch.randn(1, 3, 32, 32)

# Initialize the VisionAttention module with custom settings
model = VisionAttention(dim=32, heads=16, dim_head=128, dropout=0.2)

# Update the model with a new weight configuration
out = model(x)

# Print the output
print(out)

Conclusion

The VisionAttention module offers a flexible way to integrate self-attention mechanisms into various neural network architectures for vision-related tasks. By following the provided guidelines, using the module becomes straightforward and enables intuitive customization to best suit the specific needs of different models.

References and Resources