This tutorial introduces a new deep learning method that integrates multi-head latent attention with detailed expert segmentation. By leveraging latent attention, the model refines expert features to efficiently capture both high-level context and spatial details, leading to accurate per-pixel segmentation. In this guide, we will show an end-to-end implementation using PyTorch on Google Colab, walking through key components like a basic convolutional encoder and attention mechanisms that collate critical features for segmentation. This practical guide aims to assist you in comprehending and experimenting with advanced segmentation techniques, starting with synthetic data.
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
torch.manual_seed(42)
Here, essential libraries such as PyTorch for deep learning, numpy for numerical computations, and matplotlib for visualization are imported to create a powerful environment for building neural networks. The command torch.manual_seed(42) ensures consistent results by fixing the random seed for all torch-based random number generators.
class SimpleEncoder(nn.Module):
"""
A basic CNN encoder that extracts feature maps from an input image.
Two convolutional layers with ReLU activations and max-pooling are used
to reduce spatial dimensions.
"""
def __init__(self, in_channels=3, feature_dim=64):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, feature_dim, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
return x
The SimpleEncoder class defines a basic convolutional neural network that extracts feature maps from input images. It uses two convolutional layers along with ReLU activations and max-pooling, reducing the spatial dimensions and simplifying the image representation for further processing.
class LatentAttention(nn.Module):
"""
This module learns a set of latent vectors (the experts) and refines them
using multi-head attention on the input features.
Input:
x: A flattened feature tensor of shape [B, N, feature_dim],
where N is the number of spatial tokens.
Output:
latent_output: The refined latent expert representations of shape [B, num_latents, latent_dim].
"""
def __init__(self, feature_dim, latent_dim, num_latents, num_heads):
super().__init__()
self.num_latents = num_latents
self.latent_dim = latent_dim
self.latents = nn.Parameter(torch.randn(num_latents, latent_dim))
self.key_proj = nn.Linear(feature_dim, latent_dim)
self.value_proj = nn.Linear(feature_dim, latent_dim)
self.query_proj = nn.Linear(latent_dim, latent_dim)
self.attention = nn.MultiheadAttention(embed_dim=latent_dim, num_heads=num_heads, batch_first=True)
def forward(self, x):
B, N, _ = x.shape
keys = self.key_proj(x)
values = self.value_proj(x)
queries = self.latents.unsqueeze(0).expand(B, -1, -1)
queries = self.query_proj(queries)
latent_output, _ = self.attention(query=queries, key=keys, value=values)
return latent_output
The LatentAttention module includes a latent attention mechanism where a fixed set of latent expert vectors is refined using multi-head attention. This refinement uses the input features’ projection as keys and values, resulting in enriched expert representations that capture feature dependencies.
class ExpertSegmentation(nn.Module):
"""
For fine-grained segmentation, each pixel (or patch) feature first projects into the latent space.
Then, it attends over the latent experts (the output of the LatentAttention module) to obtain a refined representation.
Finally, a segmentation head projects the attended features to per-pixel class logits.
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