Simulation to reality style transfer using CycleGan

Installation

Clone and install requirements

$ git clone https://github.com/Glebbs/cycle_gan_pytorch.git
$ cd  cycle_gan_pytorch/
$ poetry install

Download dataset

# example: snow_dataset
$ python3 get_dataset.py

Test

The following commands can be used to test the whole test.

# Example: snow_dataset
$ python3 test.py --dataset snow_dataset --cuda

For single image processing, use the following command.

$ python3 test_image.py --file test_photo.png --model-name weights/perfect/netG_A2B.pth --cuda

Train

usage: train.py [-h] [--dataroot DATAROOT] [--dataset DATASET] [--epochs N]
                [--decay_epochs DECAY_EPOCHS] [-b N] [--lr LR] [-p N] [--cuda]
                [--netG_A2B NETG_A2B] [--netG_B2A NETG_B2A] [--netD_A NETD_A]
                [--netD_B NETD_B] [--image-size IMAGE_SIZE] [--outf OUTF]
                [--manualSeed MANUALSEED]


optional arguments:
  -h, --help            show this help message and exit
  --dataroot DATAROOT   path to datasets. (default:./data)
  --dataset DATASET     dataset name. (default:`horse2zebra`)Option:
                        [apple2orange, summer2winter_yosemite, horse2zebra,
                        monet2photo, cezanne2photo, ukiyoe2photo,
                        vangogh2photo, maps, facades, selfie2anime,
                        iphone2dslr_flower, ae_photos, ]
  --epochs N            number of total epochs to run
  --decay_epochs DECAY_EPOCHS
                        epoch to start linearly decaying the learning rate to
                        0. (default:100)
  -b N, --batch-size N  mini-batch size (default: 1), this is the total batch
                        size of all GPUs on the current node when using Data
                        Parallel or Distributed Data Parallel
  --lr LR               learning rate. (default:0.0002)
  -p N, --print-freq N  print frequency. (default:100)
  --cuda                Enables cuda
  --netG_A2B NETG_A2B   path to netG_A2B (to continue training)
  --netG_B2A NETG_B2A   path to netG_B2A (to continue training)
  --netD_A NETD_A       path to netD_A (to continue training)
  --netD_B NETD_B       path to netD_B (to continue training)
  --image-size IMAGE_SIZE
                        size of the data crop (squared assumed). (default:256)
  --outf OUTF           folder to output images. (default:`./outputs`).
  --manualSeed MANUALSEED
                        Seed for initializing training. (default:none)

Example

# Example: snow_dataset
$ python3 train.py --dataset snow_dataset --cuda

Results

Train losses: