stargan | StarGAN - Official PyTorch Implementation | Machine Learning library
kandi X-RAY | stargan Summary
kandi X-RAY | stargan Summary
This repository provides the official PyTorch implementation of the following paper:. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation Yunjey Choi1,2, Minje Choi1,2, Munyoung Kim2,3, Jung-Woo Ha2, Sung Kim2,4, Jaegul Choo1,2 1Korea University, 2Clova AI Research, NAVER Corp. 3The College of New Jersey, 4Hong Kong University of Science and Technology Abstract: Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.
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Top functions reviewed by kandi - BETA
- Train the model
- Updates the gradients of the optimizer
- Resets gradient of optimizer
- Creates labels for each color
- Adds a scalar summary
- Normalize x
- Convert labels to onehot array
- Compute classification loss
- Restore the trained models
- Compute the gradient of the autogorrelation function
- Build the model
- Prints the network
- Forward computation
- Main function
- Performs a test
- Run the test
- Get data loader
stargan Key Features
stargan Examples and Code Snippets
Lines of Code : 28License : Permissive (Apache-2.0)
Copy$ cd gan
$ python train.py
$ python infer.py
$ cd wgan
$ python train.py
$ python infer.py
$ cd wgan-gp
$ python train.py
$ python infer.py
$ cd dcgan
$ python train.py
$ python infer.py
$ cd cgan
$ python train.py
$ python infer.py
$ cd context # Training a single CycleGAN model for MNIST-Correlation
python -m augmentation.methods.cyclegan.train --config augmentation/configs/stage-1/mnist-correlation/config.yaml
# Training CycleGAN models on Waterbirds
python -m augmentation.methods.cycleg 
License : No LicenseCopycd data
./download_data.sh
conda create -n fashion_gan python=3.6
source activate fashion_gan
conda install --file conda_requirements.txt
pip install -r pip_requirements.txt
source activate fashion_gan
jupyter notebook
source deactivate
conda env Community Discussions
Trending Discussions on stargan
QUESTION
I'm using Tensorflow 2.5 to train a starGAN network for generating images (128x128 jpeg). I am using tf.keras.preprocessing.image_dataset_from_directory to load the images from the subfolders.
Additionally I am using arguments to maximize loading performance as suggested in various posts and threads such as loadedDataset.cache().repeat.prefetch
I'm also using the num_parallel_calls=tf.data.AUTOTUNE for the mapping functions for post-processing the images after loading.
While training the network on GPU the performance I am getting for GPU Utilization is in the picture attached below.
My question regarding this are:
- Is the GPU utlization normal or is it not supposed to be so erratic for traning GANs?
- Is there any way to make this performance more consistent?
- Is there any way to improve the training performance to fully utlize the GPU?
Note that Ive logged my disk I/O also and there is no bottleneck reading/writing from the disk (nvme ssd). The system has 32GB RAM and a RTX3070 with 8GB Vram. I have tried the running it on colab also; but the performance was similarly erratic.
...ANSWER
Answered 2021-Sep-28 at 01:05It is fairly normal for utilization to be erratic like for any kind of parallelized software, including training GANs. Of course, it would be better if you could fully utilize your GPU, but writing software that does this is challenging and becomes virtually impossible when you are talking about complex applications like GANs.
Let me try to demonstrate with a trivial example. Say you have two threads, threadA and threadB. threadA is running the following python code:
QUESTION
I have a bunch of text samples. Each sample has a different length, but all of them consist of >200 characters. I need to split each sample into approx 50 chara ters length substrings. To do so, I found this approach:
...ANSWER
Answered 2021-Aug-04 at 14:49You can use .{0,50}\S* in order to keep matching eventual further non-space characters (\S).
I specified 0 as lowerbound since otherwise you'd risk missing the last substring.
See a demo here.
EDIT:
For excluding the trailing empty chunk, use .{1,50}\S*, in order to force it to match at least one character.
If you also want to automatically strip the side spaces, use \s*(.{1,50}\S*).
QUESTION
I have run StarGAN Code from github, this code generate all the generated images in one picture.
How I can save all the generated images separated to single folder? I do not want to save all the images in one picture.
this is how it generate the output (sample image
I want to save the generated images from the trained model, not as samples with all the images in one picture but just a file that contains all the generated images.
This is the part of the code i want to change it
...ANSWER
Answered 2021-Feb-01 at 07:38The generator self.G is called on each element of c_fixed_list to generate images. All results are concatenated, then saved using torchvision.utils.save_image.
I don't see what's holding you from saving the images inside the loop. Something that would resemble:
QUESTION
I am trying to replicate a GAN study. So, I want to train a model (using less data) in Google Colab. But, I got this problem:
...ANSWER
Answered 2020-Nov-16 at 16:30If you aren't using the Pro version of Google Colab, then you're going to run into somewhat restrictive maximums for your memory allocation. From the Google Colab FAQ...
The amount of memory available in Colab virtual machines varies over time (but is stable for the lifetime of the VM)... You may sometimes be automatically assigned a VM with extra memory when Colab detects that you are likely to need it. Users interested in having more memory available to them in Colab, and more reliably, may be interested in Colab Pro.
You already have a good grasp of this issue, since you understand that lowering batch_size is a good way to get around it for a little while. Ultimately, though, if you want to replicate this study, you'll have to switch to a training method that can accommodate for the amount of data you seem to need.
QUESTION
I am using latex to write a report. However, when I want to add a Gantt chart into it, I found that the font of all the content after this Gantt chart changed. So can anybody tell me how to get it back? Thanks! This is the code of the chapter includes Gantt chart
...ANSWER
Answered 2020-May-18 at 14:48You explicitly tell latex to change the font of the remaining document with \sffamily. If you want this to affect only the chart, use it inside a group or switch back to \rmfamily afterwards.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install stargan
You can use stargan 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.
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