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test.py
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test.py
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import argparse
from pathlib import Path
import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms
from torchvision.utils import save_image
import net
from function import adaptive_instance_normalization, coral
def test_transform(size, crop):
transform_list = []
if size != 0:
transform_list.append(transforms.Resize(size))
if crop:
transform_list.append(transforms.CenterCrop(size))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
def style_transfer(vgg, decoder, content, style, alpha=1.0,
interpolation_weights=None):
assert (0.0 <= alpha <= 1.0)
content_f = vgg(content)
style_f = vgg(style)
if interpolation_weights:
_, C, H, W = content_f.size()
feat = torch.FloatTensor(1, C, H, W).zero_().to(device)
base_feat = adaptive_instance_normalization(content_f, style_f)
for i, w in enumerate(interpolation_weights):
feat = feat + w * base_feat[i:i + 1]
content_f = content_f[0:1]
else:
feat = adaptive_instance_normalization(content_f, style_f)
feat = feat * alpha + content_f * (1 - alpha)
return decoder(feat)
parser = argparse.ArgumentParser()
# Basic options
parser.add_argument('--content', type=str,
help='File path to the content image')
parser.add_argument('--content_dir', type=str,
help='Directory path to a batch of content images')
parser.add_argument('--style', type=str,
help='File path to the style image, or multiple style \
images separated by commas if you want to do style \
interpolation or spatial control')
parser.add_argument('--style_dir', type=str,
help='Directory path to a batch of style images')
parser.add_argument('--vgg', type=str, default='models/vgg_normalised.pth')
parser.add_argument('--decoder', type=str, default='models/decoder.pth')
# Additional options
parser.add_argument('--content_size', type=int, default=512,
help='New (minimum) size for the content image, \
keeping the original size if set to 0')
parser.add_argument('--style_size', type=int, default=512,
help='New (minimum) size for the style image, \
keeping the original size if set to 0')
parser.add_argument('--crop', action='store_true',
help='do center crop to create squared image')
parser.add_argument('--save_ext', default='.jpg',
help='The extension name of the output image')
parser.add_argument('--output', type=str, default='output',
help='Directory to save the output image(s)')
# Advanced options
parser.add_argument('--preserve_color', action='store_true',
help='If specified, preserve color of the content image')
parser.add_argument('--alpha', type=float, default=1.0,
help='The weight that controls the degree of \
stylization. Should be between 0 and 1')
parser.add_argument(
'--style_interpolation_weights', type=str, default='',
help='The weight for blending the style of multiple style images')
args = parser.parse_args()
do_interpolation = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
output_dir = Path(args.output)
output_dir.mkdir(exist_ok=True, parents=True)
# Either --content or --contentDir should be given.
assert (args.content or args.content_dir)
if args.content:
content_paths = [Path(args.content)]
else:
content_dir = Path(args.content_dir)
content_paths = [f for f in content_dir.glob('*')]
# Either --style or --styleDir should be given.
assert (args.style or args.style_dir)
if args.style:
style_paths = args.style.split(',')
if len(style_paths) == 1:
style_paths = [Path(args.style)]
else:
do_interpolation = True
assert (args.style_interpolation_weights != ''), \
'Please specify interpolation weights'
weights = [int(i) for i in args.style_interpolation_weights.split(',')]
interpolation_weights = [w / sum(weights) for w in weights]
else:
style_dir = Path(args.style_dir)
style_paths = [f for f in style_dir.glob('*')]
decoder = net.decoder
vgg = net.vgg
decoder.eval()
vgg.eval()
decoder.load_state_dict(torch.load(args.decoder))
vgg.load_state_dict(torch.load(args.vgg))
vgg = nn.Sequential(*list(vgg.children())[:31])
vgg.to(device)
decoder.to(device)
content_tf = test_transform(args.content_size, args.crop)
style_tf = test_transform(args.style_size, args.crop)
for content_path in content_paths:
if do_interpolation: # one content image, N style image
style = torch.stack([style_tf(Image.open(str(p))) for p in style_paths])
content = content_tf(Image.open(str(content_path))) \
.unsqueeze(0).expand_as(style)
style = style.to(device)
content = content.to(device)
with torch.no_grad():
output = style_transfer(vgg, decoder, content, style,
args.alpha, interpolation_weights)
output = output.cpu()
output_name = output_dir / '{:s}_interpolation{:s}'.format(
content_path.stem, args.save_ext)
save_image(output, str(output_name))
else: # process one content and one style
for style_path in style_paths:
content = content_tf(Image.open(str(content_path)))
style = style_tf(Image.open(str(style_path)))
if args.preserve_color:
style = coral(style, content)
style = style.to(device).unsqueeze(0)
content = content.to(device).unsqueeze(0)
with torch.no_grad():
output = style_transfer(vgg, decoder, content, style,
args.alpha)
output = output.cpu()
output_name = output_dir / '{:s}_stylized_{:s}{:s}'.format(
content_path.stem, style_path.stem, args.save_ext)
save_image(output, str(output_name))