| title | disqus |
|---|---|
Digital Image Processing |
hackmd |
Digital Image Processing
Table of Contents
Requirements
- torch (version: 1.2.0)
- torchvision (version: 0.4.0)
- Pillow (version: 6.1.0)
- matplotlib (version: 3.1.1)
- yacs (version: >= 0.1.4)
Downloads
Clone or download the whole project
git clone https://github.com/peter0749/DIP_Final.git
Preparing for training data and some of pretrained models.
- MSCOCO train2014 is needed to train the network.
- Our pretrained model is released here.
- Also needs to download the pretrain embedding GloVe from here.
- Our exsample images and videos can be found here
Usage
Arguments
Global arguments
config_file: Path to the config filemode: Run the mode for the model,trainfor training the model, andinferencefor inference the image through a trained model.
Inference arguments
--imsize: Size for resizing input images (resize shorter side of the image)--cropsize: Size for crop input images (crop the image into squares)--cencrop: Flag for crop the center reigion of the image (default: randomly crop)--check-point: Check point path for loading trained network--content_path: Content image path to evalute the network--style_path: Style image path to evalute the network--mask_path: Mask image path for masked stylization--style-strength: Content vs Style interpolation weight (1.0: style, 0.0: content, default: 1.0)--interpolatoin-weights: Weights for multiple style interpolation--patch-size: Patch size of style decorator (default: 3)--patch-stride: Patch stride of style decorator (default: 1)
Configs
As you can in the configs/testing.yaml file, there are many hyperparameters used in training the model, so if you want to retrain the model with different settings from us, please feel free to rewrite it.
MODEL:
USE_DATAPARALLEL: True # Use multi-gpu for training
USABLE_GPUS: [0,1,2,3] # Specify the gpu device numbers you use
DATASET:
DATA_ROOT: /tmp2/Avatar-Net/dataset/train2014 # Change the data root to the folder you download MSCOCO dataset.
IMG_PROCESSING:
IMSIZE: 512 # Fix the image size for the first input to the model
CROPSIZE: 256 # Crop the image size for data augmentation
CENCROP: False # Whether center crop the image or not.
LOSS:
FEATURE_WEIGHT: 0.1 # The affect weight on content while training
TV_WEIGHT: 1.0 # The affect weight on generalizing the model
TRAINING:
MAX_ITER: 8000 # Training iterations
LEARNING_RATE: 0.001 # Learning rate while training
BATCH_SIZE: 16 # You can reduce the batch size to meet your gpu memory limitation.
USE_CUDA: True # Whether use cuda device for training
CHECK_PER_ITER: 100 # The time period for training, and do validation on each time step
OUTPUT:
CHECKPOINT_PREFIX: avatarnet_batch16_iter8000_1.0e-03 # Name for the checkpoint for your trainingTrain
python main.py configs/testing.yaml trainInference
Single style:
Only use one style image to transfer the style to the content image.
python main.py configs/testing.yaml inference --ckpt ./checkpoints/{checkpoint} --imsize 512 --cropsize 512 --cencrop --content_path {content image path} --style_path {style image path} --style-strength 1.0Multiple style
Use more than two style images to transfer the multi-style to the content image.
python main.py configs/testing.yaml inference --ckpt ./checkpoints/{checkpoint} --imsize 512 --cropsize 512 --content_path {content image path} --style_path {style image 1 path} {style image 2 path} --interpolation-weights 0.5 0.5Multiple style with Mask
Use more than two style images to transfer the corresponded style to the masked region from the original content image.
python main.py configs/testing.yaml inference --ckpt ./checkpoints/{checkpoint} --imsize 512 --cropsize 512 --content_path {content image path} --style_path {style image 1 path} {style image 2 path} --mask_path {mask image 1 path} {mask image 2 path} --interpolation-weights 1.0 1.0Appendix and FAQ
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