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https://github.com/jef1056/metalearning-neural-style
Version 2 of Reconstruction-Style
https://github.com/jef1056/metalearning-neural-style
caffe metalearning neural-style neural-style-transfer
Last synced: 3 days ago
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Version 2 of Reconstruction-Style
- Host: GitHub
- URL: https://github.com/jef1056/metalearning-neural-style
- Owner: JEF1056
- License: mit
- Created: 2019-05-20T15:11:11.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-05-23T00:39:35.000Z (over 5 years ago)
- Last Synced: 2024-11-22T19:25:22.737Z (2 months ago)
- Topics: caffe, metalearning, neural-style, neural-style-transfer
- Language: Makefile
- Size: 34.2 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MetaLearning Neural Style
This is Version 2 of the original [Reconstruction-Style](https://github.com/JEF1056/Reconstruction-Style), and uses similar methodologies to acheive faster and more accurate results. It is designed based off of similar archetectures, but for better memory managemnt, is built on primarily C++ and CAFFE. Other noteworthy changes include better aherence to the original loss function as defined by [Gatys' paper](https://arxiv.org/abs/1508.06576) for better style adaptation, and also single-iteration adaptation.
***There currently is no method to build on Windows, it will be added in the future***
## To Build on Linux
Download only `install_deps.sh` to any folder, and then run
```
sudo chmod +x install_deps.sh
sudo install_deps.sh
```
It will install all dependencies and the reopsitory, along with building the repository. For any issues compiling, try checking out how to do so manually below.
Detailed/Manual install
Make sure CAFFE dependencies (listed below) are installed using `sudo apt-get` or equivalent method on your distributuion of linux
```
libopenblas-dev
libopencv-dev
libboost-dev
libboost-system-dev
libboost-filesystem-dev
libboost-regex-dev
libboost-thread-dev
libboost-python-dev
libprotobuf-dev
protobuf-compiler
libgflags-dev
libgoogle-glog-dev
python-numpy
python-opencv
libmpich-dev
```
then run
```
sudo apt-get update
sudo apt-get install -y $buildDeps
```
Next, install CUDA, NCCL, and CUDNN
Install CUDA
```
sudo apt-get install cuda
```
**DON'T FORGET TO SET LD_LIBRRY_PATH and PATH**
Install CUDNN
download .deb package from the [official site](https://developer.nvidia.com/rdp/cudnn-download)
```
sudo dpkg -i {PATH TO CUDNN .deb LOCATION}
```
Install NCCL
```
wget http://github.com/NVIDIA/nccl/archive/master.zip
unzip master.zip
cd nccl-master
make
make install
rm master.zip
rm -rf nccl
rm -r nccl-master
```
Finally, build the repo using the makefile
```
git clone https://github.com/JEF1056/MetaLearning-Neural-Style.git
mv MetaLearning-Neural-Style styletransfer
rm -r styletransfer/build
cd styletransfer
make
make pycaffe
```
If the build succeds, then the rest of the code can be done on python.
Download the Metalearned pretrained files here:
[Train_8](https://drive.google.com/file/d/1DxTIo8wVLTjEPLrin00ifgOSbr5HKBwK/view?usp=sharing)
[Train_32](https://drive.google.com/file/d/1jI07ubQBsudvcDV8hNXM5L8n_Oog0dsc/view?usp=sharing)
## Testing Results
All models were tested on a Tesla T4 GPU, with a 1920x1080x3 content image and a 1024x1024x3 style image.
[Train_8](https://drive.google.com/file/d/1DxTIo8wVLTjEPLrin00ifgOSbr5HKBwK/view?usp=sharing) creates model files of about 447kb, and can be evaluated in 0.23 seconds, and a video speed of 4.62 frames/second.
[Train_32](https://drive.google.com/file/d/1jI07ubQBsudvcDV8hNXM5L8n_Oog0dsc/view?usp=sharing) creates model files of about 7mb, and can be evaluated in 0.56 seconds, and a video speed of 3.02 frames/second.
Both models will use ~11.56gb of vram, which scales with style image size
Speed scales inversely with content image size.
Comparison 1:
SOURCE: [Arbitrary Style](https://github.com/jcjohnson/neural-style)
LEFT: Style ||| RIGHT: Content
LEFT: Justin Johnson's implementation ||| RIGHT: This Implementation
Comaprison 2:
SOURCE: [Fast-Style](https://github.com/jcjohnson/fast-neural-style)
LEFT: Style ||| RIGHT: Content
LEFT: Justin Johnson's implementation ||| RIGHT: This Implementation
## Usage
Usage is simple, and should take no more than running a single command. This is an overview of all the commands. Individual (basic) examples can be seen below.
```
python3 demo.py \
--model "train_32.caffemodel" \
--prototxt "prototxt/32/" \
--content "{CONTENT LOCATION}.jpg" \
--style "{STYLE LOCATION}.jpg" \
--out "{OUTPUT LOCATION}.jpg" \
--cr {CONTENT RESIZE RATIO} \
--sr {STYLE RESIZE RATIO} \
--oc \ (for original colors)
--video (for if your file is a video)
--realtime (for realtime camera styling)
```
Image
```
python3 demo.py \
--content "{CONTENT LOCATION}.jpg" \
--style "{STYLE LOCATION}.jpg" \
--out "{OUTPUT LOCATION}.jpg"
```
Video
```
python3 demo.py \
--content "{CONTENT LOCATION}.mp4" \
--style "{STYLE LOCATION}.jpg" \
--out "{OUTPUT LOCATION}.mp4" \
--video
```
Realtime
```
python3 demo.py \
--style "{STYLE LOCATION}.jpg" \
--realtime
```