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style_transfer.py
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style_transfer.py
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import argparse
import torch
from torchvision.utils import make_grid
from PIL import Image, ImageFile
from net import Net
from utils import DEVICE, test_transform
Image.MAX_IMAGE_PIXELS = None
ImageFile.LOAD_TRUNCATED_IMAGES = True
def style_transfer_method(content_image,style_img):
main_parser = argparse.ArgumentParser(description="main parser")
subparsers = main_parser.add_subparsers(title="subcommands", dest="subcommand")
main_parser.add_argument("--pretrained", type=bool, default=True,
help="whether to use the pre-trained checkpoints")
main_parser.add_argument("--requires_grad", type=bool, default=True,
help="set to True if the model requires model gradient")
train_parser = subparsers.add_parser("train", help="training mode parser")
train_parser.add_argument("--training", type=bool, default=True)
train_parser.add_argument("--iterations", type=int, default=60000,
help="total training epochs (default: 160000)")
train_parser.add_argument("--batch_size", type=int, default=2,
help="training batch size (default: 8)")
train_parser.add_argument("--num_workers", type=int, default=2,
help="iterator threads (default: 8)")
train_parser.add_argument("--lr", type=float, default=1e-4, help="the learning rate during training (default: 1e-4)")
train_parser.add_argument("--content_folder", type=str, required = True,
help="the root of content images, the path should point to a folder")
train_parser.add_argument("--style_folder", type=str, required = True,
help="the root of style images, the path should point to a folder")
train_parser.add_argument("--log_interval", type=int, default=10000,
help="number of images after which the training loss is logged (default: 20000)")
train_parser.add_argument("--w_content1", type=float, default=12, help="the stage1 content loss weight")
train_parser.add_argument("--w_content2", type=float, default=9, help="the stage2 content loss weight")
train_parser.add_argument("--w_content3", type=float, default=7, help="the stage3 content loss weight")
train_parser.add_argument("--w_remd1", type=float, default=2, help="the stage1 remd loss weight")
train_parser.add_argument("--w_remd2", type=float, default=2, help="the stage2 remd loss weight")
train_parser.add_argument("--w_remd3", type=float, default=2, help="the stage3 remd loss weight")
train_parser.add_argument("--w_moment1", type=float, default=2, help="the stage1 moment loss weight")
train_parser.add_argument("--w_moment2", type=float, default=2, help="the stage2 moment loss weight")
train_parser.add_argument("--w_moment3", type=float, default=2, help="the stage3 moment loss weight")
train_parser.add_argument("--color_on", type=str, default=True, help="turn on the color loss")
train_parser.add_argument("--w_color1", type=float, default=0.25, help="the stage1 color loss weight")
train_parser.add_argument("--w_color2", type=float, default=0.5, help="the stage2 color loss weight")
train_parser.add_argument("--w_color3", type=float, default=1, help="the stage3 color loss weight")
eval_parser = subparsers.add_parser("eval", help="evaluation mode parser")
eval_parser.add_argument("--training", type=bool, default=False)
eval_parser.add_argument("--run_folder", type=bool, default=False)
args = main_parser.parse_args()
args.training = False
model = Net(args)
model.eval()
model = model.to(DEVICE)
tf = test_transform()
Ic = tf(content_image).to(DEVICE)
Is = tf(Image.fromarray(style_img)).to(DEVICE)
Ic = Ic.unsqueeze(dim=0)
Is = Is.unsqueeze(dim=0)
with torch.no_grad():
Ics = model(Ic, Is)
grid = make_grid(Ics[0])
# Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
im = Image.fromarray(ndarr)
return im