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

Last update: Nov 9, 2021

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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

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Comments

CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

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:

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