An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

Last update: Nov 09, 2021

Related tags

Deep Learning EquivariantSelfAttention

Overview

EquivariantSelfAttention

An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

This work extends and builds on previous work:

FR Classification using e2cnn, whose code is here.
Attention for FR Classificaion, whose code is here. This work makes extensive use of the e2cnn package: e2cnn.

Imports for General Use

import os
import torch
import e2cnn
import pandas as pd
import numpy as np
import configparser as ConfigParser

import utils

Loading a network:

To load a network import the network files, read in a config file, and load the network with the config file parameters:

from networks import AGRadGalNet, VanillaLeNet, testNet, DNSteerableLeNet, DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

config      = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)

net = locals()[config['model']['base']](**config['model']).to(device)

Alternatively, if you know which architecture you want to use, only import that and load in the config parameters (i.e. without using locals()):

from networks import DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

config      = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)

net = DNSteerableAGRadGalNet(**config['model']).to(device)

Loading a model:

To load the best performing model (according to early stopping) from a given model training session:

path_supliment = config['data']['augment']+'/'
model = utils.utils.load_model(config, load_model='best', device=device, path_supliment=path_supliment)

Testing a model:

Follow the example of evaluation.ipynb for model evaluation.

Defining a model:

To define your own model simply create a new config which follows the examples provided in configs.

Training a model:

To train a model use the train.py using your config file:

python3.8 train.py --config YOUR_CONFIG_NAME.cfg >& logs/YOUR_CONFIG_NAME.log

An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

Related tags

Overview

EquivariantSelfAttention

Imports for General Use

Loading a network:

Loading a model:

Testing a model:

Defining a model:

Training a model:

Owner

My solution for the 7th place / 245 in the Umoja Hack 2022 challenge

Multi-View Radar Semantic Segmentation

Code for our NeurIPS 2021 paper: Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch

Make a surveillance camera from your raspberry pi!

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Pytorch code for "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks".

Cossim - Sharpened Cosine Distance implementation in PyTorch

Face Alignment using python

Chunkmogrify: Real image inversion via Segments

HDMapNet: A Local Semantic Map Learning and Evaluation Framework

Impelmentation for paper Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing

Large-scale open domain KNOwledge grounded conVERsation system based on PaddlePaddle

Semantic Image Synthesis with SPADE

Contrastive Learning for Compact Single Image Dehazing, CVPR2021

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

codes for Image Inpainting with External-internal Learning and Monochromic Bottleneck

Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis

Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).