AdaIN Style Transfer-PyTorch

A Pytorch implementation of Style Transfer with Adaptive Instance Normalization based on the paper Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization .

Requirement

• Argparse
• Numpy
• Pillow
• Python 3.7
• PyTorch
• TorchVision
• tqdm

Usage

Training

Download the data to the ./data/ folder. The network is trained using MSCOCO and wikiart dataset. Download the weight of the vggnet to build the encoder. Run the script train.py

$ python train.py --trainset_dir $TRAINDIR --cuda

usage: train.py [-h] [--content_dir CONTENT_DIR] [--style_dir STYLE_DIR]
                [--epochs EPOCHS] [--resume RESUME] [--cuda]

optional arguments:
  -h, --help            show this help message and exit
  --content_dir CONTENT_DIR
                        content data set path
  --style_dir STYLE_DIR
                        style data set path
  --epochs EPOCHS       training epoch number
  --resume RESUME       continues from epoch number
  --cuda                Using GPU to train

Testing

Download the decoder weight.

Run the script test_image.py

$ python test_image.py --input_image $IMG --style_image $STYLE --weight $WEIGHT --cuda

usage: test_style_transfer.py [-h] [--input_image INPUT_IMAGE]
                              [--style_image STYLE_IMAGE] [--weight WEIGHT]
                              [--alpha {Alpha Range}] [--cuda]

optional arguments:
  -h, --help            show this help message and exit
  --input_image INPUT_IMAGE
                        test image
  --style_image STYLE_IMAGE
                        style image
  --weight WEIGHT       decoder weight file
  --alpha {Alpha Range}
                        Level of style transfer, value between 0 and 1
  --cuda                Using GPU to train

Useful Tool

Make grid of images from the selected images in a directory

$ python make_grid.py --r $NUM_ROW --c $NUM_COL --dir $DIR

usage: make_grid.py [-h] [--dir DIR] [--r R] [--c C] [--size SIZE]

optional arguments:
  -h, --help   show this help message and exit
  --dir DIR    The directory for the images
  --r R        Numer of rows
  --c C        Numer of columns
  --size SIZE  Size per thumbnail

Sample Results

Test Output

Different level of Blend