def closure():
# correct the values of updated input image
input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
if run[0] % 5 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.item(), content_score.item()))
print()
if prev:
img_handler.imshow(input_img,
unloader,
title='Run {}'.format(run))
if runs:
run_save = os.path.join(run_path, f'run{run}.jpg')
img_handler.imsave(run_save, unloader, input_img)
return style_score + content_score
while not os.path.isfile(content_path):
content_path = input('E: Image not found. Path to Content: ')
style_img = img_handler.image_loader(device, loader, style_path)
content_img = img_handler.image_loader(device, loader, content_path)
# TODO remove once image size class is implemented
assert style_img.size() == content_img.size(), \
"we need to import style and content imsages of the same size"
unloader = transforms.ToPILImage() # reconvert into PIL image
if plt_prvs:
plt.ion()
plt.figure()
img_handler.imshow(style_img, unloader, title='Style Image')
plt.figure()
img_handler.imshow(content_img, unloader, title='Content Image')
# --- IMPORTING THE MODEL ---
cnn = models.vgg19(pretrained=True).features.to(device).eval()
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
input_path = input("Input Image Path or 'noise': ")
