style-transfer | Neural Algorithm of Artistic Style | Machine Learning library
kandi X-RAY | style-transfer Summary
kandi X-RAY | style-transfer Summary
This repository contains a pyCaffe-based implementation of "A Neural Algorithm of Artistic Style" by L. Gatys, A. Ecker, and M. Bethge, which presents a method for transferring the artistic style of one input image onto another. You can read the paper here: Neural net operations are handled by Caffe, while loss minimization and other miscellaneous matrix operations are performed using numpy and scipy. L-BFGS is used for minimization.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Transfer the style representation of the image
- Create a noisy noise input
- Compute the repr of the graph
- Create progress bar
- Resize the net
- Compute style objective function
- Compute the loss and gradient for a given layer
- Compute the loss and gradient of the layer
- Helper function for streaming style
- Return the generated image
style-transfer Key Features
style-transfer Examples and Code Snippets
Lines of Code : 23License : Non-SPDX (NOASSERTION)
Copybash run.sh
python style.py --style images/YOURIMAGE.jpg \
--checkpoint-dir checkpoints/ \
--model-dir models/ \
--test images/violetaparra.jpg \
--test-dir tests/ \
--content-weight 1.5e1 \
--checkpoint-iterations 1000 \
--batch-size 
License : No LicenseCopyCUDA_VISIBLE_DEVICES=0 python UnetTTS_syn.py
from UnetTTS_syn import UnetTTS
models_and_params = {"duration_param": "train/configs/unetts_duration.yaml",
"duration_model": "models/duration4k.h5",
"acous_param import codecs
import json
# SAVE DATA
write_train_file = codecs.open('/data/yelp/train.txt', "w", "utf-8")
dict = {"review": line.strip(), "score": score, "other_field_you_want": xxx}
string_ = json.dumps(dict)
write_train_file.write(string_ + '\n') import paddle
import paddlehub as hub
from paddlehub.finetune.trainer import Trainer
from paddlehub.datasets.minicoco import MiniCOCO
import paddlehub.vision.transforms as T
if __name__ == "__main__":
model = hub.Module(name='msgnet')
trans import paddle
import paddlehub as hub
if __name__ == '__main__':
model = hub.Module(name='msgnet', load_checkpoint="/PATH/TO/CHECKPOINT")
result = model.predict(origin=["venice-boat.jpg"], style="candy.jpg", visualization=True, save_path='st Community Discussions
Trending Discussions on style-transfer
QUESTION
I am trying to use remote theme on Github Pages for the first time. Although the theme works fine on the local server, it is not being deployed on the Github Pages server. I can open the page but the theme is not being loaded correctly.
I already tried the modifications to _config.yml mentioned here, here.
Here's the link to my repo. Any help will be much appreciated.
...ANSWER
Answered 2021-May-28 at 17:30Problem was the remote_theme name primarily, it was incorrectly mentioned on the theme page.
Updated remote_theme: samarsault/texture from thelehhman/texture and baseurl:'' to baseurl:
QUESTION
I have some HTML:
...ANSWER
Answered 2020-Jun-30 at 13:47Try this:
QUESTION
Suppose you're searching for a pretrained model for e.g. human gender recognition, or age estimation (Transfer Learning). So, you'd want a net that is trained on, ideally, human faces and not on stuff like the ImageNet dataset.
I know that there are two big starting points for the search:
- Keras applications
- TensorHub
Now, the best I've found is to use the search tool of the TensorHub website, like here.
That gives me some models trained on the CelebA-HQ dataset, which is something I was searching for.
But, it didn't give any results for e.g. the keywords "sport", "food" or "gun".
So, what is a good way to find pretrained models for a desired "topic"?
...ANSWER
Answered 2020-Jun-02 at 10:46It's hard to find a model for each topic at a single place.
The general strategy could be searching in GitHub with the relevant tags ["tensorflow", "sport"].
You can generally find many models on model-zoo websites: https://modelzoo.co/
This is also useful: https://github.com/tensorflow/models
If you need code (probably with pre-trained weights): paperswithcode.com is a good place to search.
QUESTION
I am trying to use this OpenCV class of text detector to find out if I can use it for my project. I am loaging image like this:
...ANSWER
Answered 2020-Apr-21 at 11:50Your problem is that you don't create an instance of your text detector. See this:
QUESTION
I'm very new to TensorFlow 2.0.
I wrote a code for Cyclic GAN as follows (I extract code only for building generator neural network):
...ANSWER
Answered 2020-Jan-13 at 15:22Lambda layers are stateless, that is, you cannot define variables within them. Instead, you could rather write a custom layer. Something along the lines of:
QUESTION
I am looking into various style transfer models and I noted that they all have limited resolution (when running on Pixel 3, for example, I couldn't go beyond 1,024x1,024, OOM otherwise).
I've noticed a few apps (eg this app) which appear to be doing style transfer for up to ~10MP images, these apps also show progress bar which I guess means that they don't just call a single tensorflow "run" method for entire image as otherwise they won't know how much was processed.
I would guess they are using some sort of tiling, but naively splitting the image into 256x256 produces inconsistent style (not just on the borders).
As this seems like an obvious problem I tried to find any publications about this, but I couldn't find any. Am I missing something?
Thanks!
...ANSWER
Answered 2019-Dec-28 at 15:01I would guess people split the model into multiple ones (for VGG it is easy to do manually, eg. via layers) and then use model_summary Keras function (or benchmarks) to estimate relative time it takes for each step and thus guide progress bar. Such separation probably also saves memory as tensorflow lite might not be clever enough to reuse memory storing intermediate activations from lower layers once they are not needed.
QUESTION
I'm trying to run distributed python job through azure ML pipelines using MPIStep pipeline class, by referring to the below example link - https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
I tried implemented the same but even I change the node count parameter in MpiStep class, while running the script the it shows size (i.e comm.Get_size()) as 1 always. Can you please help me in what I'm missing here. Is there any specific setup required on the cluster?
Code snippets:
Pipeline code snippet:
...ANSWER
Answered 2019-Oct-04 at 02:06Got to know the issue is due to package version, earlier it is installed via conda with conda_packages=["mpi4py==3.0.2"], it worked after changing the install through pip - pip_packages=["mpi4py"]
QUESTION
My end-goal is to create a script for neural style transfer, however, during writing code for said task, I stumbled upon a certain problem: the texture synthesis part of the algorithm seemed to have some problems with reproducing the artistic style. In order to solve this, I decided to create another script where I'd try to solve the task of texture synthesis using a neural network on its own.
TL;DR ... even after tackling the problem on its own, my script still produced blocky / noisy, non-sensible output.
I've tried having a look at how other people have solved this task, but most of what I found were more sophisticated solutions ("fast neural-style-transfer", etc.). Also, I couldn't find too many PyTorch implementations.
Since I've already spent the past couple of days on trying to fix this issue and considering that I'm new to the PyTorch-Framework, I have decided to ask the StackOverflow community for help and advice.
I use the VGG16 network for my model ...
...ANSWER
Answered 2019-Sep-06 at 20:00Hurrah!
After yet another day of researching and testing, I've finally discovered the bug in my code.
The problem doesn't lie with the training process or the model itself, but rather with the lines responsible for loading the style image. (this article helped me discover the issue)
So... I added the following two functions to my script ...
QUESTION
I am currently getting familiar with TensorFlow and machine learning. I am doing some tutorials on style transfer and now I have a part of an example code that I somehow can not comprehend.
I think I get the main idea: there are three images, the content image, the style image and the mixed image. Let's just talk about the content loss first, because if I can understand that, I will also understand the style loss. So I have the content image and the mixed image (starting from some distribution with some noise), and the VGG16 model.
As far as I can understand, I should now feed the content image into the network to some layer, and see what is the output (feature map) of that layer for the content image input.
After that I also should feed the network with the mixed image to the same layer as before, and see what is the output (feature map) of that layer for the mixed image input.
I then should calculate the loss function from these two output, because I would like the mixed image to have a similar feature map to the content image.
My problem is that I do not understand how this is done in the example codes that I could find online.
The example code can be the following: http://gcucurull.github.io/tensorflow/style-transfer/2016/08/18/neural-art-tf/
But nearly all of the examples used the same approach.
The content loss is defined like this:
...ANSWER
Answered 2019-Apr-08 at 11:42The loss forces the networks to have similar activation on the layer you have chosen.
Let us call one convolutional map/pixel from target_out[layer] and corresponding map from cont_out . You want their difference to be as small as possible, i.e., the absolute value of their difference. For the sake of numerical stability, we use the square function instead of absolute value because it is a smooth function and more tolerant of small errors.
We thus get , which is: tf.square(tf.sub(target_out[layer], cont_out)).
Finally, we want to minimize the difference for each map and each example in the batch. This is why we sum all the difference into a single scalar using tf.reduce_sum.
QUESTION
I'm setting up a conda environment on Windows 10 Pro x64 using Miniconda 4.5.12 and have done a pip install of azureml-sdk inside the environment but get a ModuleNotFoundError when attempting to execute the following code:
...ANSWER
Answered 2018-Dec-24 at 06:38It's probably because the name if your python file is the same as a module name you are trying import. In this case, rename the file to something other than azureml.py.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install style-transfer
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
