https://github.com/voldien/superresolution
Machine Learning - Super Resolution
https://github.com/voldien/superresolution
machine-learning model super-resolution tensorflow
Last synced: 6 months ago
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Machine Learning - Super Resolution
- Host: GitHub
- URL: https://github.com/voldien/superresolution
- Owner: voldien
- License: gpl-3.0
- Created: 2023-05-26T15:41:38.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-10-24T09:58:50.000Z (12 months ago)
- Last Synced: 2025-02-03T18:16:41.176Z (8 months ago)
- Topics: machine-learning, model, super-resolution, tensorflow
- Language: Python
- Homepage: https://www.codeintrinsic.com/machine-learning-super-resolution-stylized-anime-artsyle/
- Size: 245 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Super Resolution - Machine Learning
[](https://github.com/voldien/SuperResolution/actions/workflows/ci.yaml)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://github.com/voldien/SuperResolution/releases)
A SuperResolution training program for creating/training upscaling machine learning model, developed for educational purposes only. The result may vary between training data and hyperparameter, the example are from own trained model.
## Features
### Model Architecture
* **EDSR** - Enhanced Deep Residual Networks for Single Image Super-Resolution
* **VDR** - Very Deep Convolutional Network
* **AE** - AutoEncoder Super Resolution
* **DCNN** - Deep Convolutional Super Resolution Neural Network
* **Resnet** - Residual Network Deep Convolutional Super Resolution Neural Network
* **SRGAN** - GAN (Generative Adversarial Network) based Super Resolution Network
### Loss/Cost Function
* **SSIM** - Structural similarity index measure
* **MSA** - Mean Square Absolute
* **MSE** - Mean Square Error
* **VGG16** - Perceptible Loss Error Function
* **VGG19** - Perceptible Loss Error Function
## Basic Program Command Line
The most basic command line. Using the default option.
```bash
python superresolution/SuperResolution.py --data-set-directory /path_to_training_data/
```
### EDSR - Enhanced Deep Residual Networks for Single Image Super-Resolution
```basha
python superresolution/SuperResolution.py --batch-size 16 --epochs 10 --image-size 128 128 --model edsr --learning-rate 0.0003 --decay-rate 0.9 --decay-step 10000 --color-space rgb --loss-fn msa --shuffle-data-set-size 1024 --show-psnr --data-set-directory /path_to_training_data/ --output-dir image-super-resolution-result/
```
### VDR - Very Deep Convolutional Network
```bash
python superresolution/SuperResolution.py --batch-size 16 --epochs 10 --image-size 128 128 --model vdr --learning-rate 0.0003 --color-space rgb --loss-fn msa --shuffle-data-set-size 512 --show-psnr --data-set-directory /path_to_training_data/ --output-dir image-super-resolution-result/
```
### AE - AutoEncoder Super Resolution
```bash
python superresolution/SuperResolution.py --batch-size 16 --epochs 10 --image-size 128 128 --model dcsr-ae --learning-rate 0.0003 --color-space rgb --loss-fn msa --shuffle-data-set-size 512 --show-psnr --data-set-directory /path_to_training_data/ --output-dir image-super-resolution-result/
```
### DCNN - Deep Convolutional Super Resolution Neural Network
```bash
python superresolution/SuperResolution.py --batch-size 16 --epochs 10 --image-size 128 128 --model cnnsr --learning-rate 0.002 --color-space rgb --loss-fn msa --shuffle-data-set-size 512 --show-psnr --data-set-directory /path_to_training_data/ --output-dir image-super-resolution-result/
```
### Resnet - Residual Network Deep Convolutional Super Resolution Neural Network
```bash
python superresolution/SuperResolution.py --batch-size 16 --epochs 10 --image-size 128 128 --model dcsr-resnet --learning-rate 0.0003 --color-space rgb --loss-fn msa --shuffle-data-set-size 512 --show-psnr --data-set-directory /path_to_training_data/ --output-dir image-super-resolution-result/
```
### SuperResolution Training Program Argument
```bash
usage: SuperResolution [-h] [--epochs EPOCHS] [--batch-size BATCH_SIZE] [--use-checkpoint] [--checkpoint-filepath CHECKPOINT_DIR] [--checkpoint-every-epoch CHECKPOINT_EVERY_NTH_EPOCH] [--learning-rate LEARNING_RATE] [--device DEVICES] [--cpu] [--gpu]
[--distribute-strategy {mirror}] [--verbosity VERBOSITY] [--use-float16] [--cache-ram] [--cache-file CACHE_PATH] [--shuffle-data-set-size DATASET_SHUFFLE_SIZE] [--data-set-directory TRAIN_DIRECTORY_PATHS]
[--validation-data-directory VALIDATION_DIRECTORY_PATHS] [--test-data-directory TEST_DIRECTORY_PATHS] [--image-size INPUT_IMAGE_SIZE INPUT_IMAGE_SIZE] [--output-image-size OUTPUT_IMAGE_SIZE OUTPUT_IMAGE_SIZE] [--seed SEED]
[--color-space {rgb,lab}] [--color-channels {1,3,4}] [--optimizer {adam,rmsprop,sgd,adadelta}] [--disable-validation] [--config CONFIG] [--model-filename MODEL_FILEPATH] [--output-dir OUTPUT_DIR] [--example-batch EXAMPLE_NTH_BATCH]
[--example-batch-grid-size EXAMPLE_NTH_BATCH_GRID_SIZE] [--show-psnr] [--metrics {psnr,ssim}] [--decay-rate LEARNING_RATE_DECAY] [--decay-step LEARNING_RATE_DECAY_STEP] [--model {dcsr,dscr-post,dscr-pre,edsr,dcsr-ae,dcsr-resnet,vdsr,srgan}]
[--loss-fn {mse,ssim,msa,psnr,vgg16,vgg19,none}]
Super Resolution Training Model Program
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS Set the number of passes that the training set will be trained against.
--batch-size BATCH_SIZE
number of training element per each batch, during training.
--use-checkpoint Set the path the checkpoint will be saved/loaded.
--checkpoint-filepath CHECKPOINT_DIR
Set the path the checkpoint will be saved/loaded.
--checkpoint-every-epoch CHECKPOINT_EVERY_NTH_EPOCH
Set how often the checkpoint will be update, per epoch.
--learning-rate LEARNING_RATE
Set the initial Learning Rate
--device DEVICES Select the device explicitly that will be used.
--cpu Explicit use the CPU as the compute device.
--gpu Explicit use of GPU
--distribute-strategy {mirror}
Select Distribute Strategy.
--verbosity VERBOSITY
Set the verbosity level of the program
--use-float16 Hint the usage of Float 16 (FP16) in the model.
--cache-ram Use System Memory (RAM) as Cache storage.
--cache-file CACHE_PATH
Set the cache file path that will be used to store dataset cached data.
--shuffle-data-set-size DATASET_SHUFFLE_SIZE
Set the size of the shuffle buffer size, zero disables shuffling.
--data-set-directory TRAIN_DIRECTORY_PATHS
Directory path where the images are located dataset images
--validation-data-directory VALIDATION_DIRECTORY_PATHS
Directory path where the images are located dataset images
--test-data-directory TEST_DIRECTORY_PATHS
Directory path where the images are located dataset images
--image-size INPUT_IMAGE_SIZE INPUT_IMAGE_SIZE
Set the size of the images in width and height for the model.
--output-image-size OUTPUT_IMAGE_SIZE OUTPUT_IMAGE_SIZE
Set the size of the images in width and height for the model.
--seed SEED Set the random seed
--color-space {rgb,lab}
Select Color Space used in the model.
--color-channels {1,3,4}
Select Number of channels in the color space. GrayScale, RGB and RGBA.
--optimizer {adam,rmsprop,sgd,adadelta}
Select optimizer to be used
--disable-validation Select if use data validation step.
--config CONFIG Config File - Json.
--model-filename MODEL_FILEPATH
Define file path that the generator model will be saved at.
--output-dir OUTPUT_DIR
Set the output directory that all the models and results will be stored at
--example-batch EXAMPLE_NTH_BATCH
Set the number of train batches between saving work in progress result.
--example-batch-grid-size EXAMPLE_NTH_BATCH_GRID_SIZE
Set the grid size of number of example images.
--show-psnr Set the grid size of number of example images.
--metrics {psnr,ssim}
Set what metric to capture.
--decay-rate LEARNING_RATE_DECAY
Set Learning rate Decay.
--decay-step LEARNING_RATE_DECAY_STEP
Set Learning rate Decay Step.
--model {dcsr,dscr-post,dscr-pre,edsr,dcsr-ae,dcsr-resnet,vdsr,srgan}
Set which model type to use.
--loss-fn {mse,ssim,msa,psnr,vgg16,vgg19,none}
Set Loss Function
```
## Upscale Image
Upscaling images using pre-trained upscale model.
### Upscale Single Image
The following allows to upscale a single image.
```bash
python3 superresolution/UpScaleUtil.py --model super-resolution-model-2113109.h5 --input-file low_res.png --save-output high_res.png --batch 32 --color-space rgb
```
### Upscale Directory
The following allows to upscale a whole directory.
```bash
python3 superresolution/UpScaleUtil.py --model super-resolution-model-2113109.h5 --save-output high_output_dir/ --input-file low_input_dir/ --batch 32 --color-space rgb
```
### Evolution Program - HyperParameter
The Evolution Program allow sto try multiple variable combination in order to find a good set of variable configuration. Simliar to hyperparameter testing.
```bash
python3 superresolution/super-resolution-evolution-test.py --epochs 8 --batch 32 rgb --image-size 128 128 --data-set-directory /path_to_training_data/ --validation-data-directory /path_to_validation_data/ --output-dir evolution_test/
```
Argument options
```bash
usage: SuperResolution Model Evolution [-h] [--epochs EPOCHS] [--batch-size BATCH_SIZE] [--use-checkpoint] [--checkpoint-filepath CHECKPOINT_DIR] [--checkpoint-every-epoch CHECKPOINT_EVERY_NTH_EPOCH] [--learning-rate LEARNING_RATE]
[--device DEVICES] [--cpu] [--gpu] [--distribute-strategy {mirror}] [--verbosity VERBOSITY] [--use-float16] [--cache-ram] [--cache-file CACHE_PATH] [--shuffle-data-set-size DATASET_SHUFFLE_SIZE]
[--data-set-directory TRAIN_DIRECTORY_PATHS] [--validation-data-directory VALIDATION_DIRECTORY_PATHS] [--test-data-directory TEST_DIRECTORY_PATHS] [--image-size INPUT_IMAGE_SIZE INPUT_IMAGE_SIZE]
[--output-image-size OUTPUT_IMAGE_SIZE OUTPUT_IMAGE_SIZE] [--seed SEED] [--color-space {rgb,lab}] [--color-channels {1,3,4}] [--optimizer {adam,rmsprop,sgd,adadelta}] [--disable-validation] [--config CONFIG]
[--output-dir OUTPUT_DIR] [--models [{cnnsr,dcsr,edsr,dcsr-ae,dcsr-resnet,vdsr,srgan,esrgan} ...]] [--loss-functions [{mse,ssim,msa,vgg16,vgg19} ...]] [--optimizer-evolution [{adam,rmsprop,sgd,adadelta} ...]]
Super Resolution Training Model Evolution Program
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS Set the number of passes that the training set will be trained against.
--batch-size BATCH_SIZE
number of training element per each batch, during training.
--use-checkpoint Set the path the checkpoint will be saved/loaded.
--checkpoint-filepath CHECKPOINT_DIR
Set the path the checkpoint will be saved/loaded.
--checkpoint-every-epoch CHECKPOINT_EVERY_NTH_EPOCH
Set how often the checkpoint will be update, per epoch.
--learning-rate LEARNING_RATE
Set the initial Learning Rate
--device DEVICES Select the device explicitly that will be used.
--cpu Explicit use the CPU as the compute device.
--gpu Explicit use of GPU
--distribute-strategy {mirror}
Select Distribute Strategy.
--verbosity VERBOSITY
Set the verbosity level of the program
--use-float16 Hint the usage of Float 16 (FP16) in the model.
--cache-ram Use System Memory (RAM) as Cache storage.
--cache-file CACHE_PATH
Set the cache file path that will be used to store dataset cached data.
--shuffle-data-set-size DATASET_SHUFFLE_SIZE
Set the size of the shuffle buffer size, zero disables shuffling.
--data-set-directory TRAIN_DIRECTORY_PATHS
Directory path where the images are located dataset images
--validation-data-directory VALIDATION_DIRECTORY_PATHS
Directory path where the images are located dataset images
--test-data-directory TEST_DIRECTORY_PATHS
Directory path where the images are located dataset images
--image-size INPUT_IMAGE_SIZE INPUT_IMAGE_SIZE
Set the input training images size. Low Resolution (LR).
--output-image-size OUTPUT_IMAGE_SIZE OUTPUT_IMAGE_SIZE
Set the size of the images in width and height for the model (HR).
--seed SEED Set the random seed
--color-space {rgb,lab}
Select Color Space used in the model.
--color-channels {1,3,4}
Select Number of channels in the color space. GrayScale, RGB and RGBA.
--optimizer {adam,rmsprop,sgd,adadelta}
Select optimizer to be used
--disable-validation Disable validation if validation data is present.
--config CONFIG Config File - Json.
--output-dir OUTPUT_DIR
Set the output directory that all the models and results will be stored at
--models [{cnnsr,dcsr,edsr,dcsr-ae,dcsr-resnet,vdsr,srgan,esrgan} ...]
Overide what Model to include in training evolution.
--loss-functions [{mse,ssim,msa,vgg16,vgg19} ...]
Overide what Loss functions to include in training evolution.
--optimizer-evolution [{adam,rmsprop,sgd,adadelta} ...]
Select optimizer to be used
```
## Installation Instructions
### Setup Virtual Environment
python3.9 or higher
```bash
python3 -m venv venv
source venv/bin/activate
```
## Installing Required Packages
### Nvidia - CUDA
```bash
pip install -r requirements.txt requirements_cuda.txt
pip install tensorflow[and-cuda]==2.14.1
```
### AMD - ROCM
```bash
pip install -r requirements.txt requirements_rocm.txt
```
## Docker
### AMD - ROCM
```bash
docker build -t super-resolution-rocm -f Dockerfile.rocm .
docker run --network=host --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --name sr-rocm super-resolution-rocm
```
### Nvidia - CUDA
```bash
sudo apt-get install -y nvidia-container-toolkit
```
```bash
docker build -t super-resolution-cuda -f Dockerfile.cuda .
docker run --network=host --gpus all --name sr-cuda super-resolution-cuda
```
# Convert Keras Model to TensorLite
```bash
python3 superresolution/generate_tflite.py --model super-resolution-model.keras --output model-lite.tflite
```
# Convert to ONNX
```bash
python3 -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx
```
## Run Python In Background (Server)
When running python script as background process, it will still be terminated if closing the terminal window. However, with the **nohup** it can be run in the background as well close the terminal window.
```bash
nohup python3 superresolution/SuperResolution.py ...your arguments... &
```
## License
This project is licensed under the GPL+3 License - see the [LICENSE](LICENSE) file for details.