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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 time
import net
from function import adaptive_instance_normalization, coral
from function import adaptive_mean_normalization
from function import adaptive_std_normalization
from function import exact_feature_distribution_matching, histogram_matching
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, style_type='adain'):
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)
if style_type == 'adain':
base_feat = adaptive_instance_normalization(content_f, style_f)
elif style_type == 'adamean':
base_feat = adaptive_mean_normalization(content_f, style_f)
elif style_type == 'adastd':
base_feat = adaptive_std_normalization(content_f, style_f)
elif style_type == 'efdm':
base_feat = exact_feature_distribution_matching(content_f, style_f)
elif style_type == 'hm':
feat = histogram_matching(content_f, style_f)
else:
raise NotImplementedError
for i, w in enumerate(interpolation_weights):
feat = feat + w * base_feat[i:i + 1]
content_f = content_f[0:1]
else:
if style_type == 'adain':
feat = adaptive_instance_normalization(content_f, style_f)
elif style_type == 'adamean':
feat = adaptive_mean_normalization(content_f, style_f)
elif style_type == 'adastd':
feat = adaptive_std_normalization(content_f, style_f)
elif style_type == 'efdm':
feat = exact_feature_distribution_matching(content_f, style_f)
elif style_type == 'hm':
feat = histogram_matching(content_f, style_f)
else:
raise NotImplementedError
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='pretrained/vgg_normalised.pth')
parser.add_argument('--decoder', type=str, default='pretrained/efdm_decoder_iter_160000.pth.tar')
parser.add_argument('--style_type', type=str, default='adain', help='adain | adamean | adastd | efdm')
parser.add_argument('--test_style_type', type=str, default='', help='adain | adamean | adastd | efdm')
# 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)')
parser.add_argument('--photo', action='store_true',
help='apply on the photo style transfer')
# 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()
if not args.test_style_type:
args.test_style_type = args.style_type
print('Note: the style type: %s and the pre-trained model: %s should be consistent' % (args.style_type, args.decoder))
print('The test style type is:', args.test_style_type)
do_interpolation = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
output_dir = Path(args.output + '_' + args.style_type + '_' + args.test_style_type)
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)
timer = []
for content_path in content_paths:
if do_interpolation:
# one content image, 4 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)
list = []
steps = [1, 0.75, 0.5, 0.25, 0]
for i in steps:
for j in steps:
list.append([i*j, i*(1-j), (1-i)*j, (1-i)*(1-j)])
count = 1
for interpolation_weights in list:
with torch.no_grad():
output = style_transfer(vgg, decoder, content, style,
args.alpha, interpolation_weights, style_type=args.test_style_type)
output = output.cpu()
output_name = output_dir / '{:s}_interpolate_{:s}_{:s}'.format(
content_path.stem, str(count), args.save_ext)
save_image(output, str(output_name))
count+=1
#### content & style trade-off.
# alpha = [0.0, 0.25, 0.5, 0.75, 1.0]
# 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)
# ## replace the style image with Gaussian noise
# # style.normal_(0,1)
# # style = torch.rand(style.size()).to(device)
# ### for paired images.
# if args.photo:
# if content_path.stem[2:] == style_path.stem[3:]:
# for sample_alpha in alpha:
# with torch.no_grad():
# output = style_transfer(vgg, decoder, content, style,
# sample_alpha, style_type=args.test_style_type)
# output = output.cpu()
# output_name = output_dir / '{:s}_stylized_{:s}{:s}{:s}'.format(
# content_path.stem, style_path.stem, str(sample_alpha), args.save_ext)
# save_image(output, str(output_name))
# else:
# for sample_alpha in alpha:
# with torch.no_grad():
# output = style_transfer(vgg, decoder, content, style,
# sample_alpha, style_type=args.test_style_type)
# output = output.cpu()
# output_name = output_dir / '{:s}_stylized_{:s}{:s}{:s}'.format(
# content_path.stem, style_path.stem, str(sample_alpha), 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)
## replace the style image with Gaussian noise
# style.normal_(0,1)
# style = torch.rand(style.size()).to(device)
### for paired images.
if args.photo:
if content_path.stem[2:] == style_path.stem[3:]:
with torch.no_grad():
start_time = time.time()
output = style_transfer(vgg, decoder, content, style,
args.alpha, style_type=args.test_style_type)
timer.append(time.time() - start_time)
print(timer)
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))
else:
with torch.no_grad():
output = style_transfer(vgg, decoder, content, style,
args.alpha, style_type=args.test_style_type)
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))
print(torch.FloatTensor(timer).mean())
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