pyradox

This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2

Installation

pip install git+https://github.com/Ritvik19/pyradox.git

Usage

Modules

Module	Description	Input Shape	Output Shape	Usage
Rescale	A layer that rescales the input: `x_out = (x_in -mu) / sigma`	Arbitrary	Same shape as input	check here
Convolution 2D	Applies 2D Convolution followed by Batch Normalization (optional) and Dropout (optional)	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Densely Connected	Densely Connected Layer followed by Batch Normalization (optional) and Dropout (optional)	2D tensor with shape (batch_size, input_dim)	2D tensor with shape (batch_size, n_units)	check here
DenseNet Convolution Block	A Convolution block for DenseNets	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
DenseNet Convolution Block	A Convolution block for DenseNets	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
DenseNet Transition Block	A Transition block for DenseNets	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Dense Skip Connection	Implementation of a skip connection for densely connected layer	2D tensor with shape (batch_size, input_dim)	2D tensor with shape (batch_size, n_units)	check here
VGG Module	Implementation of VGG Modules with slight modifications, Applies multiple 2D Convolution followed by Batch Normalization (optional), Dropout (optional) and MaxPooling	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inception Conv	Implementation of 2D Convolution Layer for Inception Net, Convolution Layer followed by Batch Normalization, Activation and optional Dropout	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inception Block	Implementation on Inception Mixing Block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Xception Block	A customised implementation of Xception Block (Depthwise Separable Convolutions)	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net Block	Implementation of Efficient Net Block (Depthwise Separable Convolutions)	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Conv Skip Connection	Implementation of Skip Connection for Convolution Layer	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net Block	Customized Implementation of ResNet Block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net V2 Block	Customized Implementation of ResNetV2 Block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res NeXt Block	Customized Implementation of ResNeXt Block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inception Res Net Conv 2D	Implementation of Convolution Layer for Inception Res Net: Convolution2d followed by Batch Norm	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inception Res Net Block	Implementation of Inception-ResNet block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	block 8 Block 17 Block 35
NAS Net Separable Conv Block	Adds 2 blocks of Separable Conv Batch Norm	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
NAS Net Adjust Block	Adjusts the input `previous path` to match the shape of the `input`
NAS Net Normal A Cell	Normal cell for NASNet-A
NAS Net Reduction A Cell	Reduction cell for NASNet-A
Mobile Net Conv Block	Adds an initial convolution layer with batch normalization and activation	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Mobile Net Depth Wise Conv Block	Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, activation, pointwise convolution, batch normalization and activation	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inverted Res Block	Adds an Inverted ResNet block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
SEBlock	Adds a Squeeze Excite Block	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here

ConvNets

Module	Description	Input Shape	Output Shape	Usage
Generalized Dense Nets	A generalization of Densely Connected Convolutional Networks (Dense Nets)	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Densely Connected Convolutional Network 121	A modified implementation of Densely Connected Convolutional Network 121	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Densely Connected Convolutional Network 169	A modified implementation of Densely Connected Convolutional Network 169	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Densely Connected Convolutional Network 201	A modified implementation of Densely Connected Convolutional Network 201	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Generalized VGG	A generalization of VGG network	4D tensor with shape (batch_shape, rows, cols, channels)	4D or 2D tensor	usage 1 usage 2
VGG 16	A modified implementation of VGG16 network	4D tensor with shape (batch_shape, rows, cols, channels)	2D tensor with shape (batch_shape, new_dim)	usage 1 usage 2
VGG 19	A modified implementation of VGG19 network	4D tensor with shape (batch_shape, rows, cols, channels)	2D tensor with shape (batch_shape, new_dim)	usage 1 usage 2
Inception V3	Customized Implementation of Inception Net	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Generalized Xception	Generalized Implementation of XceptionNet (Depthwise Separable Convolutions)	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Xception Net	A Customised Implementation of XceptionNet	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net	Generalized Implementation of Effiecient Net	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B0	Customized Implementation of Efficient Net B0	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B1	Customized Implementation of Efficient Net B1	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B2	Customized Implementation of Efficient Net B2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B3	Customized Implementation of Efficient Net B3	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B4	Customized Implementation of Efficient Net B4	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B5	Customized Implementation of Efficient Net B5	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B6	Customized Implementation of Efficient Net B6	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Efficient Net B7	Customized Implementation of Efficient Net B7	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net	Customized Implementation of Res Net	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 50	Customized Implementation of Res Net 50	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 101	Customized Implementation of Res Net 101	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 152	Customized Implementation of Res Net 152	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net V2	Customized Implementation of Res Net V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 50 V2	Customized Implementation of Res Net 50 V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 101 V2	Customized Implementation of Res Net 101 V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res Net 152 V2	Customized Implementation of Res Net 152 V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res NeXt	Customized Implementation of Res NeXt	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res NeXt 50	Customized Implementation of Res NeXt 50	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res NeXt 101	Customized Implementation of Res NeXt 101	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Res NeXt 152	Customized Implementation of Res NeXt 152	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
Inception Res Net V2	Customized Implementation of Inception Res Net V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
NAS Net	Generalised Implementation of NAS Net	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
NAS Net Mobile	Customized Implementation of NAS Net Mobile	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
NAS Net Large	Customized Implementation of NAS Net Large	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	check here
MobileNet	Customized Implementation of MobileNet	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	usage 1 usage 2
Mobile Net V2	Customized Implementation of Mobile Net V2	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	usage 1 usage 2
Mobile Net V3	Customized Implementation of Mobile Net V3	4D tensor with shape (batch_shape, rows, cols, channels)	4D tensor with shape (batch_shape, new_rows, new_cols, new_channels)	usage 1 usage 2

DenseNets

Module	Description	Input Shape	Output Shape	Usage
Densely Connected Network	Network of Densely Connected Layers followed by Batch Normalization (optional) and Dropout (optional)	2D tensor with shape (batch_size, input_dim)	2D tensor with shape (batch_size, new_dim)	check here
Densely Connected Resnet	Network of skip connections for densely connected layer	2D tensor with shape (batch_size, input_dim)	2D tensor with shape (batch_size, new_dim)	check here

State-of-the-art data augmentation search algorithms in PyTorch

MuarAugment Description MuarAugment is a package providing the easiest way to a state-of-the-art data augmentation pipeline. How to use You can instal

43 Dec 12, 2022

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A selection of State Of The Art research papers (and code) on human trajectory prediction (forecasting). Papers marked with [W] are workshop papers.

40 Nov 18, 2022

A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

54 Dec 21, 2022

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

213 Dec 27, 2022

Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

55 Oct 25, 2022

Fix for _pooling_factor of GeneralizedUNet

if len(encoder_config) > 1 an exception was raised in GeneralizedUNet._pooling_factor: only 1st iteration will be an array.

btw great repo, big thx for it!

opened by p4vv37 1

State of the Art Neural Networks for Deep Learning

Related tags

Overview

pyradox

Installation

Usage

Modules

ConvNets

DenseNets

You might also like...

State-of-the-art data augmentation search algorithms in PyTorch

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

Comments

Fix for _pooling_factor of GeneralizedUNet

Releases(v1.0.1)

v1.0.1(Apr 9, 2021)

v0.18.10(Apr 9, 2021)

v0.17.10(Jan 9, 2021)

Owner

Ritvik Rastogi

Program your own vulkan.gpuinfo.org query in Python. Used to determine baseline hardware for WebGPU.

Official PyTorch Implementation of paper EAN: Event Adaptive Network for Efficient Action Recognition

Recursive Bayesian Networks

Only valid pull requests will be allowed. Use python only and readme changes will not be accepted.

Notebook and code to synthesize complex and highly dimensional datasets using Gretel APIs.

This is the source code for the experiments related to the paper Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

Improving Factual Consistency of Abstractive Text Summarization

Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Code and models used in "MUSS Multilingual Unsupervised Sentence Simplification by Mining Paraphrases".

Implements MLP-Mixer: An all-MLP Architecture for Vision.

This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

Codes for CVPR2021 paper "PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization"

Combinatorial model of ligand-receptor binding

Benchmarks for the Optimal Power Flow Problem

This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Evaluation suite for large-scale language models.

MoCap-Solver: A Neural Solver for Optical Motion Capture Data

Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).