t-cnn | pytorch implement of Modeling and propagating cnns | Data Visualization library

by tzing Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | t-cnn Summary

t-cnn is a Python library typically used in Analytics, Data Visualization, Pytorch applications. t-cnn has no bugs, it has no vulnerabilities and it has low support. However t-cnn build file is not available. You can download it from GitHub.

pytorch implement of "Modeling and propagating cnns in a tree structure for visual tracking"

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Quality

Security

License

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Support

t-cnn has a low active ecosystem.

It has 6 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

t-cnn has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of t-cnn is current.

Quality

t-cnn has 0 bugs and 0 code smells.

Security

t-cnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

t-cnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

t-cnn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

t-cnn releases are not available. You will need to build from source code and install.

t-cnn has no build file. You will be need to create the build yourself to build the component from source.

It has 424 lines of code, 20 functions and 4 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed t-cnn and discovered the below as its top functions. This is intended to give you an instant insight into t-cnn implemented functionality, and help decide if they suit your requirements.

Fit the model to the given bounding box
Convert box coordinates toxywh
Ensure input is numpy
Ensures the shape of x
Check shape and bbox
Predict target Y
Converts xy coordinates to points

Get all kandi verified functions for this library.

t-cnn Key Features

No Key Features are available at this moment for t-cnn.

t-cnn Examples and Code Snippets

No Code Snippets are available at this moment for t-cnn.

Community Discussions

Trending Discussions on t-cnn

How to get Conv2D kernel values in Tensorflow 2

Is it possible to combine 2 neural networks?

Shared istio egress gateway in multi-cluster/multi-primary mesh

Egressgateway enable to see the requests in the log

How can I create a Tokio runtime inside another Tokio runtime without getting the error "Cannot start a runtime from within a runtime"?

ISTIO Egress gateway Flow

QUESTION

How to get Conv2D kernel values in Tensorflow 2

Asked 2021-Dec-19 at 17:58

Problem

I have a Conv2D layer:

...

ANSWER

Answered 2021-Dec-19 at 17:58

import tensorflow as tf

input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
model = tf.keras.layers.Conv2D(2, 3, activation='relu', input_shape=input_shape[1:])
y = model(x)
print(model.kernel)

Source https://stackoverflow.com/questions/70413900

QUESTION

Is it possible to combine 2 neural networks?

Asked 2021-Jun-13 at 00:55

I have a NET like (exemple from here)

...

ANSWER

Answered 2021-Jun-07 at 14:26

The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:

Source https://stackoverflow.com/questions/67872719

QUESTION

Shared istio egress gateway in multi-cluster/multi-primary mesh

Asked 2021-Jun-01 at 12:52

We are trying to setup an egress gateway in a multi-cluster/multi-primary mesh configuration where the egress gateway is located in only one cluster but used from both.

diagram of desired setup

The use case is that the clusters are in different network zones and we want to be able to route traffic to one zone transparently to the clients in the other zone.

We followed this guide in one cluster and it worked fine. However we have trouble setting up the VirtualService in the second cluster to use the egress gateway in the first cluster.

When deploying the following virtual service to the second cluster we get 503 with cluster_not_found.

...

ANSWER

Answered 2021-May-31 at 14:52

According to the comments the solution should works as below:

To create a multi-cluster deployment you can use this tutorial. In this situation cross cluster workload of normal services works fine. However, there is a problem with getting the traffic to the egress gateway routed via the eastwest gateway. This can be solved with this example. You should also change kind: VirtualService to kind: ServiceEntry in both clusters.

Like Tobias Henkel mentioned:

I got it to work fine with the service entry if I target the ingress gateway on ports 80/443 which then dispatches further to the mesh external services.

You can also use Admiral to automate traffic routing.

Vulnerabilities

No vulnerabilities reported

Install t-cnn

You can download it from GitHub.
You can use t-cnn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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