CycleGAN | Tensorflow implementation of CycleGAN | Machine Learning library

by hardikbansal Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | CycleGAN Summary

CycleGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Generative adversarial networks applications. CycleGAN has no bugs, it has no vulnerabilities and it has low support. However CycleGAN build file is not available. You can download it from GitHub.

Tensorflow implementation of CycleGAN.

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CycleGAN has a low active ecosystem.

It has 347 star(s) with 216 fork(s). There are 18 watchers for this library.

It had no major release in the last 6 months.

There are 13 open issues and 8 have been closed. On average issues are closed in 26 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CycleGAN is current.

Quality

CycleGAN has 0 bugs and 0 code smells.

Security

CycleGAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CycleGAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CycleGAN 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

CycleGAN releases are not available. You will need to build from source code and install.

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

CycleGAN saves you 130 person hours of effort in developing the same functionality from scratch.

It has 326 lines of code, 18 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Calculate the loss
Build a resnet block
Lrelu layer
Instance normalization
Builds the discriminator layer
Save training images
General convolutional convolution layer
Generalized convolution layer
Create a fake image pool
Performs training
Builds the resnet block
Reads the input tensors
Setup the model
Test the model
Build the resnet block
Patch the discriminator

Get all kandi verified functions for this library.

CycleGAN Key Features

No Key Features are available at this moment for CycleGAN.

CycleGAN Examples and Code Snippets

No Code Snippets are available at this moment for CycleGAN.

Community Discussions

Trending Discussions on CycleGAN

Is it possible to train except for a specific area when implementing cycleGAN?

RuntimeError: Input type and weight type should be the same

Reading back a custom dataset TFRecords

Load generator model from Gan

pytorch cyclegann gives a Missing key error when testing

How can torch.cat only have one tensor?

OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: (error in tf Cycle GAN)

Converting a pytorch model to nn.Module for exporting to onnx for lens studio

mlmodel's output shape is empty. Why is shape empty?

"ignoring potentially dangerous server-supplied filename" in pscp

QUESTION

Is it possible to train except for a specific area when implementing cycleGAN?

Asked 2022-Feb-23 at 18:19

Recently, I am conducting research to create fake images using cycleGAN. The figure below is an example of the cycleGAN result. It can be seen that all areas are changed as in the image. However, I want to change the image only for a specific area. Is it possible to change only the image of the part except for the red marked part (disease part) using cycleGAN?

enter image description here

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ANSWER

Answered 2022-Feb-23 at 18:19

I think what you describe is possible. You would need to alter the cycleGan pipeline. To focus the representational capacity of the network on everything other than the red marked part you could replace the red marked part with the same part from the original image, this would enforce a focus on the other regions. When taking the cycle consistency pipeline shown below, I'm referring to adding the original image parts after e.g. Generator A2B.

Cycle consistency loss pipeline

Source https://stackoverflow.com/questions/71216162

QUESTION

RuntimeError: Input type and weight type should be the same

Asked 2022-Jan-24 at 07:04

I’m trying to swap resNet blocks with resNext blocks in my current model. All worked and I even trained the model for 1000+ epochs with the resNet blocks but when I added the following class to the model, it returned this error. (ran without errors in my local CPU but got the error when running in colab)

Added Class :

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ANSWER

Answered 2022-Jan-24 at 07:04

Your problem in your new class GroupConv1D is that you store all your convolution modules in a regular python list self.conv_list instead of using nn Containers.
All methods that affect nn.Modules (e.g., .to(device), .eval(), etc.) are applied recursively to all relevant members of the "root" nn.Module.
However, how can pytorch tell which are the relevant members?
For this you have containers: they group together sub-modules, registers and parameters such that pytorch can recursively apply all relevant nn.Module's methods to them.

See, e.g., this answer.

Source https://stackoverflow.com/questions/70829410

QUESTION

Reading back a custom dataset TFRecords

Asked 2022-Jan-04 at 17:01

I am trying to create a custom dataset in TFRecords for a CycleGAN model. The model requires a new type of dataset which is not available so I need to create one. I have a few JPG images of 256x256. Following this link, I created TFrecords file for my images, below code:

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ANSWER

Answered 2022-Jan-04 at 10:24

I updated the read_tfrecord function like below, see the commented out line and that solves this particular error.

Source https://stackoverflow.com/questions/70576891

QUESTION

Load generator model from Gan

Asked 2021-Nov-10 at 08:10

I train à CycleGan and now I want to eval it.

At the end of the cycleGan training, I was able to find the following file : latest_net_G_A.pth

When I try to load the model and evaluate it I have some issues :

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ANSWER

Answered 2021-Nov-10 at 08:10

Could you please share your training code and fit method? Seemingly you have no evaluate method or eval decorator which is why you are getting this error.

Source https://stackoverflow.com/questions/69766202

QUESTION

pytorch cyclegann gives a Missing key error when testing

Asked 2021-May-26 at 11:04

I have trained a model using the pix2pix pytorch implementation and would like to test it.

However when I test it I get the error

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ANSWER

Answered 2021-May-26 at 11:04

I think the problem here is some layer the bias=None but in testing the model required this, you should check the code for details.

After I check your config in train and test, the norm is different. For the code in GitHub, the norm difference may set the bias term is True or False.

Source https://stackoverflow.com/questions/67701029

QUESTION

How can torch.cat only have one tensor?

Asked 2021-Mar-27 at 17:12

I think there is an error in line 53 of the following code:

CycleGan, Buffer for Images

It says:

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ANSWER

Answered 2021-Mar-27 at 17:12

torch.cat's first argument is expected to be a sequence of tensors rather than a single tensor. So you pass in like:

Source https://stackoverflow.com/questions/66833911

QUESTION

OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: (error in tf Cycle GAN)

Asked 2020-Aug-12 at 09:00

I am trying to code along with Cycle_gan Tensorflow

I received an error saying:

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ANSWER

Answered 2020-Aug-12 at 09:00

You are correct. Take a look.

Source https://stackoverflow.com/questions/63372487

QUESTION

Converting a pytorch model to nn.Module for exporting to onnx for lens studio

Asked 2020-Aug-06 at 22:20

I am trying to convert pix2pix to a pb or onnx that can run in Lens Studio. Lens studio has strict requirements for the models. I am trying to export this pytorch model to onnx using this guide provided by lens studio. The issue is the pytorch model found here uses its own base class, when in the example it uses Module.nn, and therefore doesnt have methods/variables that the torch.onnx.export function needs to run. So far Ive run into its missing a variable called training and a method called train

Would it be worth it to try to modify the base model, or should I try to build it from scratch using nn.Module? Is there a way to make the pix2pix model inherit from both the abstract base class and nn.module? Am I not understanding the situation? The reason why I want to do it using the lens studio tutorial is because I have gotten it to export onnx different ways but Lens Studio wont accept those for various reasons.

Also this is my first time asking a SO question (after 6 years of coding), let me know if I make any mistakes and I can correct them. Thank you.

This is the important code from the tutorial creating a pytorch model for Lens Studio:

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ANSWER

Answered 2020-Aug-06 at 22:08

You can definitely have your model inherit from both the base class and torch.nn.Module (python allows multiple inheritance). However you should take care about the conflicts if both inherited class have functions with identical names (I can see at least one : their base provide the eval function and so to nn.module).

However since you do not need the CycleGan, and a lot of the code is compatibility with their training environment, you'd probably better just re-implement the pix2pix. Just steal the code, have it inherit from nn.Module, copy-paste useful/mandatory functions from the base class, and have everything translated into clean pytorch code. You already have the forward function (which is the only requirement for a pytorch module).

All the subnetworks they use (like the resnet blocks) seem to inherit from nn.Module already so there is nothing to change here (double check that though)

Source https://stackoverflow.com/questions/63291760

QUESTION

mlmodel's output shape is empty. Why is shape empty?

Asked 2020-Jun-12 at 09:28

I study this tutorial https://www.tensorflow.org/tutorials/generative/cyclegan and the completed model is well worked on windows. Then I convert this tf model to mlmodel, but the model's output(MultyArray) has empty shape. How do i solve this problem...? (This model's EPOCH is 1)

computer::: windows10 / tensorflow and -gpu 2.2 / tfcoreml 1.1

this is convert code

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ANSWER

Answered 2020-Jun-12 at 09:28

This is usually not a problem. When you run the model, you still get a multi-array of the correct shape.

If you already know the shape, you can fill it in so that it shows up in the mlmodel file, but this is more for documentation purposes than anything else.

Source https://stackoverflow.com/questions/62325752

QUESTION

"ignoring potentially dangerous server-supplied filename" in pscp

Asked 2020-May-29 at 16:59

I want to copy some file from a remote Linux system to my Windows PC using pscp (from PuTTY). I wrote a small script that should copy all .png files in a directory on my server:

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ANSWER

Answered 2020-May-28 at 14:25

-unsafe won't help with this.

The problem is that your file names contain colons. Colons are not allowed in Windows file names.

Source https://stackoverflow.com/questions/62066908

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CycleGAN

You can download it from GitHub.
You can use CycleGAN 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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