ai-dawang/PlugNPlay-Modules

Features

• Attention Mechanisms - Provides a comprehensive library of attention mechanism implementations, including spatial, channel, and deformable variants.
• Hybrid Spatial-Channel Attention - Integrates spatial and channel-wise weighting blocks to selectively emphasize important features.
• Computer Vision - Provides a comprehensive toolkit of reusable PyTorch libraries for deep learning image processing and computer vision tasks.
• Channel Attention - Weights feature maps by learning importance across channels using global average pooling and linear transformations.
• Frequency-Domain Attention - Implements a channel attention mechanism that leverages Discrete Cosine Transforms to capture frequency-domain features.
• Hybrid Attention - Ships a combination of channel-wise and spatial attention blocks to adaptively weight image information.
• Integrated Attention Blocks - Implements a single block combining spatial, channel, and gating attention to refine feature representations.
• Linear-Complexity Attention - Implements linear-complexity attention by approximating the attention mechanism through weighted summations.
• Standardized Convolutional Sequences - Offers standardized convolutional sequences including batch normalization and activation to simplify model definition.
• Depthwise Separable Convolutions - Implements depthwise separable convolutions that split operations into depthwise and pointwise layers for efficiency.
• Deep Learning Architectures - Offers standardized building blocks for constructing deep neural networks with multi-scale feature extraction and normalization.
• Feature Fusion Architectures - Provides architectural blocks for aggregating multi-modal, multi-scale, and bitemporal data into unified representations.
• Cross-Resolution Fusion - Provides mechanisms to merge feature maps from different hierarchy levels using upsampling and attention-based bagging.
• Laplacian Pyramid Decompositions - Provides a process that generates a Laplacian pyramid by iteratively downsampling and subtracting tensors.
• Multi-Scale Feature Aggregation - Combines representations from different resolutions using parallel convolutional branches and adaptive weighting.
• Computer Vision Architectures - Provides structural designs and neural network layouts specifically engineered for processing and interpreting visual data.
• 1D Convolutional Layers - Provides a set of reusable 1D convolutional architectural components for processing sequential signals.
• Configurable Convolutional Blocks - Implements modular convolutional blocks with configurable normalization and activation functions.
• Deformable Convolutions - Implements convolutional layers with learnable offsets to adapt receptive fields to input geometry.
• Partial Convolutions - Implements partial convolutions that apply filters to a subset of channels to reduce computational overhead.
• Fast Fourier Convolutions - Processes image features in the frequency domain using Fast Fourier Transforms to capture global dependencies.
• Multi-Head Attention Mechanisms - Implements the standard multi-head scaled dot-product attention mechanism using queries, keys, and values.
• Global Frequency Filters - Provides a process that filters image features in the frequency domain using 2D Real Fast Fourier Transforms.
• Spectral Feature Filters - Implements frequency-domain transformations on images and time-series data to extract spectral features and apply dynamic filtering.
• Signal Processing - Implements discrete wavelet transforms and frequency-domain filtering for signal analysis within neural networks.
• Wavelet Signal Decompositions - Implements discrete wavelet transforms to split signals into high and low frequency components for spatial analysis.
• Inverse Wavelet Reconstruction - Implements a process that restores original signals from decomposed wavelet components using inverse discrete wavelet transforms.
• Neural Signal Processing Layers - Implements neural network modules for wavelet transforms and frequency-domain filtering for signal analysis.
• Wavelet-Based Downsampling - Provides a process that reduces the sampling rate of input data using wavelet transforms to extract essential features.
• Activation-Free Blocks - Implements blocks that replace traditional nonlinear activation functions with depthwise convolutions and channel attention.
• Attention Masking - Implements a gated mask to control information flow and refine feature representations within attention layers.
• Attention Gating - Refines feature maps by combining gating signals with input tensors through grouped convolutions.
• Bottleneck Channel Attention - Calculates channel-wise importance using global average pooling and a bottleneck structure.
• Convolutional Attention - Uses average and max pooling to enhance important information specifically for image segmentation tasks.
• Deformable Attention - Provides an attention mechanism with linear complexity and deformable offsets to focus on relevant spatial locations.
• Double Attention - Captures long-range spatial dependencies by gathering and redistributing global descriptors across the feature map.
• Dual Attention - Integrates spatial and channel attention mechanisms to capture global dependencies and channel-wise relationships.
• Dynamic Attention - Computes channel, filter, spatial, and kernel attention weights to adaptively refine feature representations.
• Fine-Grained Channel Attention - Computes adaptive weights for feature channels using global average pooling and 1D convolutions.
• Global Context Attention - Computes attention across global features using orthogonal projection matrices and generalized kernels.
• Hybrid Global-Local Attention - Combines local convolutional features with global transformer representations for efficient image patch processing.
• Parallel Attention Structures - Implements a parallel combination of pixel-level, channel-level, and adaptive weight attention mechanisms.
• Parameter-Free Attention - Calculates attention weights based on feature map variance to emphasize regions without learnable parameters.
• Pixel-Level Attention - Refines feature maps by weighting channels and spatial pixels to emphasize important regions.
• Position-Aware Attention - Combines global channel-wise weighting and local spatial attention to emphasize important descriptors.
• Spatial Attention Weighting - Provides a spatial attention weighting module that uses average and max pooling to highlight critical image regions.
• Temporal Attention - Provides temporal attention modules that use 1D convolutions and global average pooling to weight time-based features.
• Temporal Attention Sequences - Implements temporal attention sequences using shared multi-layer perceptrons and adaptive pooling.
• Squeeze-and-Excitation Attention - Combines multi-scale convolutions and squeeze-and-excitation blocks to emphasize spatial and channel information.
• Strip Attention - Processes spatial features through horizontal and vertical attention to capture long-range dependencies for image restoration.
• Wavelet-Domain Attention - Implements wavelet-domain attention that combines dual spatial-channel attention with wavelet transforms.
• Spatial-Channel Attention - Implements spatial-channel attention layers that refine multi-dimensional data using convolutional filters.
• Poly-Kernel Inception Modules - Provides Poly Kernel Inception Blocks that process features through parallel depthwise convolutions with varying dilations.
• Inverted Residual Blocks - Provides inverted residual MLP structures with expansion phases using depthwise-separable convolutions.
• Feature Recalibration Modules - Implements combined channel and spatial attention mechanisms to recalibrate multiscale feature representations.
• Dynamic Feature Fusion - Combines two input feature maps using global average pooling and learned attention weights.
• Hierarchical Feature Fusion - Combines pooling, attention mechanisms, and spatial convolutions to refine multi-scale representations for classification.
• Global Filter Networks - Implements a global filter network combining patch embedding and global filtering blocks for image classification.
• Patch Embedding Modules - Provides patch embedding modules that convert 2D image data into flattened sequences for transformer architectures.
• Multi-Scale Feature Pyramids - Implements a feature pyramid network that aggregates hierarchical image representations to improve object detection accuracy.
• Adaptive Multi-Scale Fusion - Combines multiple intermediate representation levels using adaptive weighting to integrate multi-scale information.
• Self-Modulated Aggregators - Provides a system for combining multi-scale feature information using adaptive pooling and variance-based modulation.
• Spatially-Varying Convolutions - Provides spatially-varying convolutions that modulate feature maps using coordinate-based gating and bilinear coefficient maps.
• Layer Parameter Optimization - Implements computational efficiency improvements through separable and partial convolutions and stochastic depth.
• Modular Layer Compositions - Provides a system for constructing neural architectures by stacking standardized modular blocks of convolutions and activations.
• Spatially-Adaptive Modulators - Provides a system using multi-scale spatial weighting and interpolation to enhance image super-resolution.
• Variance-Based Spatial Modulators - Implements a process using adaptive pooling and variance-based modulation to emphasize relevant spatial information.
• Additive Attention - Provides an efficient attention mechanism combining query-weighted sums with adaptive average pooling.
• Axis-Based Attention - Uses grouped Hadamard products and depth-wise convolutions across multiple axes to capture spatial and channel dependencies.
• Cross-Region Attention - Implements a self-attention mechanism across partitioned image regions using multi-head processing.
• Cross-Scale Attention - Captures dependencies across different vision-based scales using a multi-head self-attention mechanism.
• Sparse Attention Routing - Provides a gating mechanism that routes input tokens to a sparse subset of attention heads.
• Window-Based Attention - Provides a window-based multi-head self-attention mechanism with relative position bias for spatial relationship capture.
• Positional Embedding Layers - Creates sine and cosine positional encodings to provide spatial or sequential awareness to network layers.
• Multi-Scale Self-Attention - Provides a polarized multi-scale feature self-attention mechanism to refine features across spatial scales.
• Polarized Self-Attention - Computes channel-wise and spatial-wise attention weights for high-quality pixel-wise regression.
• Separable Self-Attention - Implements a computationally efficient separable self-attention mechanism that condenses input sequences into context vectors.
• Transposed Self-Attention - Provides a transposed self-attention mechanism that reduces complexity by computing attention across channel dimensions.
• Image Feature Engineering - Extracts and refines spatial and spectral representations using wavelets and Fourier transforms.

Star-Verlauf