def evaluate(args):
content_image = utils.tensor_load_rgbimage(args.content_image,
size=args.content_size,
keep_asp=True)
content_image = content_image.unsqueeze(0)
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
style = style.unsqueeze(0)
style = utils.preprocess_batch(style)
style_model = Net(ngf=args.ngf)
style_model.load_state_dict(torch.load(args.model))
if args.cuda:
style_model.cuda()
content_image = content_image.cuda()
style = style.cuda()
style_v = Variable(style, volatile=True)
content_image = Variable(utils.preprocess_batch(content_image),
volatile=True)
style_model.setTarget(style_v)
output = style_model(content_image)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def evaluate(args):
content_image = utils.tensor_load_rgbimage(args.content_image,
size=args.content_size,
keep_asp=True)
content_image = content_image.unsqueeze(0)
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
style = style.unsqueeze(0)
style = utils.preprocess_batch(style)
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
style_model = Net()
style_model.load_state_dict(torch.load(args.model))
if args.cuda:
style_model.cuda()
vgg.cuda()
content_image = content_image.cuda()
style = style.cuda()
style_v = Variable(style, volatile=True)
style_v = utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
content_image = Variable(utils.preprocess_batch(content_image),
volatile=True)
style_model.setTarget(gram_style[2].data)
output = style_model(content_image)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def optimize(args):
""" Gatys et al. CVPR 2017
ref: Image Style Transfer Using Convolutional Neural Networks
"""
# load the content and style target
content_image = utils.tensor_load_rgbimage(args.content_image,
size=args.content_size,
keep_asp=True)
content_image = content_image.unsqueeze(0)
content_image = Variable(utils.preprocess_batch(content_image),
requires_grad=False)
content_image = utils.subtract_imagenet_mean_batch(content_image)
style_image = utils.tensor_load_rgbimage(args.style_image,
size=args.style_size)
style_image = style_image.unsqueeze(0)
style_image = Variable(utils.preprocess_batch(style_image),
requires_grad=False)
style_image = utils.subtract_imagenet_mean_batch(style_image)
# load the pre-trained vgg-16 and extract features
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
content_image = content_image.cuda()
style_image = style_image.cuda()
vgg.cuda()
features_content = vgg(content_image)
f_xc_c = Variable(features_content[1].data, requires_grad=False)
features_style = vgg(style_image)
gram_style = [utils.gram_matrix(y) for y in features_style]
# init optimizer
output = Variable(content_image.data, requires_grad=True)
optimizer = Adam([output], lr=args.lr)
mse_loss = torch.nn.MSELoss()
# optimizing the images
for e in range(args.iters):
utils.imagenet_clamp_batch(output, 0, 255)
optimizer.zero_grad()
features_y = vgg(output)
content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = utils.gram_matrix(features_y[m])
gram_s = Variable(gram_style[m].data, requires_grad=False)
style_loss += args.style_weight * mse_loss(gram_y, gram_s)
total_loss = content_loss + style_loss
if (e + 1) % args.log_interval == 0:
print(total_loss.data.cpu().numpy()[0])
total_loss.backward()
optimizer.step()
# save the image
output = utils.add_imagenet_mean_batch(output)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def fast_evaluate(args, basedir, contents, idx=0):
# basedir to save the data
style_model = Net(ngf=args.ngf)
style_model.load_state_dict(torch.load(args.model))
style_model.eval()
if args.cuda:
style_model.cuda()
style_loader = StyleLoader(args.style_folder,
args.style_size,
cuda=args.cuda)
for content_image in contents:
idx += 1
content_image = utils.tensor_load_rgbimage(content_image,
size=args.content_size,
keep_asp=True).unsqueeze(0)
if args.cuda:
content_image = content_image.cuda()
content_image = Variable(utils.preprocess_batch(content_image),
volatile=True)
for isx in range(style_loader.size()):
style_v = Variable(style_loader.get(isx).data, volatile=True)
style_model.setTarget(style_v)
output = style_model(content_image)
filename = os.path.join(basedir, "{}_{}.png".format(idx, isx + 1))
utils.tensor_save_bgrimage(output.data[0], filename, args.cuda)
print(filename)
def get(self, i):
idx = i % len(self.files)
filepath = os.path.join(self.folder, self.files[idx])
style = utils.tensor_load_rgbimage(filepath, self.style_size)
style = style.unsqueeze(0)
style = utils.preprocess_batch(style)
if self.cuda:
style = style.cuda()
style_v = Variable(style, requires_grad=False)
return style_v