from torch.autograd import Variable
import torchvision.transforms as transforms
img_to_tensor = transforms.ToTensor()
from PIL import Image
import torchvision.models as models
vgg = models.vgg19(pretrained=True).features
vgg = vgg.cpu()
def make_model():
model = models.vgg19(pretrained=True).features[:12]
model = model.eval()
return model
def extract_feature(model, imgpath):
model.eval()
img = Image.open(imgpath)
img = img.resize((224, 224))
tensor = img_to_tensor(img)
result = model(Variable(torch.unsqueeze(tensor, dim=0).float(), requires_grad=False))
#result_npy = result.data.cpu().numpy()
return result
if __name__ =="__main__":
model = make_model()
imgpath = 'picture/content_Easy.bmp'
tmp = extract_feature(model, imgpath)
#tmp = tmp.clamp_(0 ,1)
show_img(tmp)
from torchvision import transforms
from run_code import run_style_transfer
from load_img import load_img, show_img
from torch.autograd import Variable
style_img = load_img('./picture/star.jpg')
# style_img = Variable(style_img).cpu()
style_img = Variable(style_img).cuda()
content_img = load_img('./picture/tower.jpg')
# content_img = Variable(content_img).cpu()
content_img = Variable(content_img).cuda()
input_img = content_img.clone()
out = run_style_transfer(content_img, style_img, input_img, num_epoches=200)
print("style_transfer_img is ...")
show_img(out.cpu())
save_pic = transforms.ToPILImage()(out.cpu().squeeze(0))
save_pic.save('./picture/star_tower.jpg')
from torchvision import transforms
from run_code import run_style_transfer
from load_img import load_img, show_img
from torch.autograd import Variable
style_img = load_img('./picture/style.png')
style_img = Variable(style_img)
content_img = load_img('./picture/cat.jpg')
content_img = Variable(content_img)
input_img = content_img.clone()
out = run_style_transfer(content_img, style_img, input_img, num_epoches=200)
show_img(out)
save_pic = transforms.ToPILImage()(out.squeeze(0))
save_pic.save('./picture/saved_picture.png')