def run(self):
"""Main entry point for style transfer, operates coarse-to-fine as specified by the number of scales.
"""
for self.scale in range(0, self.args.scales):
# Pre-process the input images so they have the expected size.
factor = 2 ** (self.args.scales - self.scale - 1)
content_imgs = []
for img in self.content_imgs:
content_imgs.append(resize.DownscaleBuilder(factor, cuda=self.cuda).build(img))
style_imgs = []
for img in self.style_imgs:
style_imgs.append(resize.DownscaleBuilder(factor, cuda=self.cuda).build(img))
# Determine the stating point for the optimizer, was there an output of previous scale?
if self.seed_img is None:
# a) Load an image from disk, this needs to be the exact right size.
if self.args.seed is not None:
seed_img = images.load_from_file(self.args.seed, self.device)
#seed_img = resize.DownscaleBuilder(factor).build(self.seed_img)
#print(seed_img.shape, content_img.shape)
assert seed_img.shape == content_imgs[0].shape
# b) Use completely random buffer from a normal distribution.
else:
seed_img = torch.empty_like(content_imgs[0]).normal_(std=0.5).clamp_(-2.0, +2.0)
else:
# c) There was a previous scale, so resize and add noise from normal distribution.
seed_img = (resize.DownscaleBuilder(factor, cuda=self.cuda).build(self.seed_img)
+ torch.empty_like(content_imgs[0]).normal_(std=0.1)).clamp_(-2.0, +2.0)
# Pre-compute the cross-correlation statistics for the style image layers (aka. gram matrices).
self.style_gram = {}
n = 0
for img in style_imgs:
for i, f in self.model.extract(img, layers=self.args.style_layers):
self.style_gram[n, i] = histogram.square_matrix(f - 1.0).detach()
n = n + 1
# Pre-compute feature histograms for the style image layers specified.
self.style_hist = {}
n = 0
for img in style_imgs:
for k, v in self.model.extract(img, layers=self.args.histogram_layers):
self.style_hist[n, k] = histogram.extract_histograms(v, bins=5, min=torch.tensor(-1.0), max=torch.tensor(+4.0))
n = n + 1
# Prepare and store the content image activations for image layers too.
self.content_feat = {}
n = 0
for img in content_imgs:
for i, f in self.model.extract(img, layers=self.args.content_layers):
self.content_feat[n, i] = f.detach()
n = n + 1
# Now run the optimization using L-BFGS starting from the seed image.
output = self.optimize(seed_img, self.iterations[self.scale]) #, lr=0.2)
# For the next scale, we'll reuse a biliniear interpolated version of this output.
self.seed_img = resize.UpscaleBuilder(factor, mode='bilinear').build(output).detach()
# Save the final image at the finest scale to disk.
basename = os.path.splitext(os.path.basename(self.args.content or self.args.style))[0]
images.save_to_file(self.image.clone().detach().cpu(), self.args.output or ('output/%s_final.png' % basename))
def test_match_offset(source, offset):
h = histogram.extract_histograms(source)
output = histogram.match_histograms(source + offset, h)
assert pytest.approx(0.0, abs=1e-4) == torch.max(output - source)
def test_match_identity(source):
h = histogram.extract_histograms(source)
output = histogram.match_histograms(source, h)
assert pytest.approx(0.0, abs=1e-6) == torch.max(output - source)
def test_extract_normalized(source):
h = histogram.extract_histograms(source)
assert pytest.approx(1.0, abs=1e-6) == torch.sum(h[0])
def test_extract_balanced(source):
h = histogram.extract_histograms(source)
assert pytest.approx(0.0,
abs=1e-6) == torch.mean(h[0] - 1.0 / h[0].shape[2])
def test_extract_deterministic(source):
h1 = histogram.extract_histograms(source)
h2 = histogram.extract_histograms(source)
assert (h1[0] == h2[0]).all()
