neural-style | 🎨 | Machine Learning library
kandi X-RAY | neural-style Summary
kandi X-RAY | neural-style Summary
Neural style in TensorFlow!
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Blend a network
- Print progress
- Unprocess an image
- Preprocess an image
- Convert rgb to gray
- Construct a preloaded version of VGG19
- Convolution layer
- Get the loss values from loss_store
- Return the size of a tensor
- Pooling layer
- Convert a gray array to RGB
- Convert a number of seconds into a human readable string
- Loads the weights from a netCDF file
- Build the argument parser
- Read an image file
- Resize an image
- Format a format
- Save image to file
neural-style Key Features
neural-style Examples and Code Snippets
Lines of Code : 73License : Permissive (BSD-3-Clause)
Copyusage: neural_style.py [-h] {train,eval} ...
parser for fast-neural-style
optional arguments:
-h, --help show this help message and exit
subcommands:
{train,eval}
train parser for training arguments
eval parser for eval MIT License
Copyright (c) 2017 Blanyal D'Souza
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including python preprocess.py --source_font source_font.ttf \
--target_font target_font.otf \
--char_list charsets/urducharset.txt \
--save_dir bitmap_path
python transfer.py --mode=train \
Community Discussions
Trending Discussions on neural-style
QUESTION
I have some HTML:
...ANSWER
Answered 2020-Jun-30 at 13:47Try this:
QUESTION
I try to go through installation process Github on MacOs Catalina
The first step is to execute in Terminal:
...ANSWER
Answered 2019-Oct-11 at 09:09You don't need to install cask anymore, you just need homebrew. Try using any cask command
QUESTION
I am currently looking into CycleGAN and im using simontomaskarlssons github repository as my baseline. My problem arises when the training is done and I want to use the saved model to generate new samples. Here the model architecture for the loaded model are different from the initialized generator. The direct link for the saveModel function is here.
When I initialize the generator that does the translation from domain A to B the summary looks like the following (line in github). This is as expected since my input image is (140,140,1) and I am expecting an output image as (140,140,1):
...ANSWER
Answered 2019-Oct-22 at 16:27When you persist your architecture using model.to_json, the method get_config is called so that the layer attributes are saved as well. As you are using a custom class without that method, the default value for padding is being used when you call model_from_json.
Using the following code for ReflectionPadding2D should solve your problem, just run the training step again and reload the model.
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 trying to get Tensorflow GPU support going in Python under Windows 10.
What does work;
Download and install Python v3.7.3
...ANSWER
Answered 2019-Apr-11 at 22:37For all those with the „DLL load failed“ problem under Windows 10/Python 3.6.x/RTX20xx.
The combination of CUDA 10.0 (not 10.1!), cuDNN 7.5.0 works fine for me (as of 12 April 2019). I also have Visual Studio 2015 installed (but not sure if needed).
Don‘t forget to add the location of the cuDNN *.dll file (it‘s the /bin/ dir in your CUDA dir) to your PATH.
If you have CUDA 10.1, just uninstall it, install 10.0, add the cuDNN files to the 10.0 dir, and reboot.
Tensorflow can be installed using pip install tensorflow-gpu
QUESTION
I've been trying to recreate the work done in this blog post. The writeup is very comprehensive and code is shared via a collab.
What I'm trying to do is extract layers from the pretrained VGG19 network and create a new network with these layers as the outputs. However, when I assemble the new network, it highly resembles the VGG19 network and seems to contain layers that I didn't extract. An example is below.
...ANSWER
Answered 2018-Oct-03 at 03:47
- Why are layers that I didn't extract showing up in
new_model.
That's because when you create a model with models.Model(vgg.input, model_outputs) the "intermediate" layers between vgg.input and the output layers are included as well. This is the intended way as VGG is constructed this way.
For example if you were to create a model this way: models.Model(vgg.input, vgg.get_layer('block2_pool') every intermediate layer between the input_1 and block2_pool would be included since the input has to flow through them before reaching block2_pool. Below is a partial graph of VGG that could help with that.
Now, -if I've not misunderstood- if you want to create a model that doesn't include those intermediate layers (which would probably work poorly), you have to create one yourself. Functional API is very useful on this. There are examples on the documentation but the gist of what you want to do is as below:
QUESTION
I am running a convolutional neural network on AWS instance g2.2xlarge. The model runs fine with 30000 images of size 64x64. However, when I try to run it with images of size 128x128, it gives memory error (see below) even when I only input 1 image (which has 2 channels - real and imaginary).
Because the error mentions tensor of shape [32768,16384], I assume it happens during the first (fully-connected) layer, which takes input image with two channels 128*128*2 = 32768 and outputs 128*128 = 16384 vector.
I found recommendations to decrease the batch size, however, I already use 1 input image only.
Here it is written that using cudnn one could get up to 700-900px on the same AWS instance that I use (although, I do not know if they use fully-connected layers). I tried two different AMIs (1 and 2), both with cudnn installed, but still got memory error.
My questions are:
1. How do I calculate how much memory is needed for a [32768,16384] tensor? I am not a computer scientist, so I would appreciate a detailed reply.
2. I guess I am trying to understand whether the instance I use really has too little memory for my data (g2.2xlarge has 15 GiB) or I am just doing something wrong.
Error:
...ANSWER
Answered 2018-Jan-29 at 11:26The amount of memory you need depends indeed largely on the size of the Tensor but ALSO on the datatype you use (int32, int64, float16, float32, float64).
So to question 1: your Tensor will need 32768 x 16384 x memory_size_of_your_datatype memory (e.g. memory footprint of float_64 is 64 bits as the name suggests, which is 8 byte, so in this case your Tensor would need 4.3e9 bytes or 4.3 Gigabytes)
One easy way to reduce memory consumption is thus to just go from float64 to float32 or even float16 (1/2 and 1/4, respectively) if the loss in precision doesn't hurt your accuracy too much.
Also, you have to understand how the total memory of your AWS instance is made up, i.e. what is the GPU RAM of the GPUs that make up your instance, which is the critical piece of memory here.
Also, check out https://www.tensorflow.org/api_docs/python/tf/profiler/Profiler
Edit: You can pass a tf.ConfigProto() to your tf.Session(config=...) through which you can specify GPU usage.
Especially, look at the allow_growth, allow_soft_placement, per_process_gpu_memory_fraction options`
(especially the last one should help you)
QUESTION
Following this tutorial: https://www.digitalocean.com/community/tutorials/how-to-perform-neural-style-transfer-with-python-3-and-pytorch#step-2-%E2%80%94-running-your-first-style-transfer-experiment
When I run the example in Jupyter notebook, I get the following:
So, I've tried troubleshooting, which eventually got me to running it as per the github example (https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer) says to via command line:
...ANSWER
Answered 2017-Dec-04 at 16:45I think the reason maybe that you have an older version of PyTorch on your system. On my system, the pytorch version is 0.2.0, torch.nn has a module called Upsample.
You can uninstall your current version of pytorch and reinstall it.
QUESTION
I have been trying to run this Tensorflow style transfer implementation - https://github.com/anishathalye/neural-style on Windows (the GPU version), but I am getting this error:
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[64,239400] [[Node: gradients/MatMul_grad/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/truediv_2_grad/tuple/control_dependency, Reshape)]]
I am a complete beginner in both Tensorflow and Python so I don't really know how to fix this.
...ANSWER
Answered 2017-Apr-08 at 09:14This is an Out Of Memory error. You don't have enough GPU memory to run the deep network for this image.
You have 2 solutions :
- If you don't care about speed, use the CPU version, because you probably have more CPU memory (RAM) than GPU memory. You set CUDA_VISIBLE_DEVICES to disable GPU :
CUDA_VISIBLE_DEVICES= python neural_style.py --styles
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install neural-style
You can use neural-style 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
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
