def eval_s_grad(layer, style):
nonlocal loss
current_gram = gram_matrix(self.data[layer])
n, mh, mw = self.data[layer].shape
feat = self.data[layer].reshape((n, mh * mw))
gram_diff = current_gram - style.grams[layer]
s_grad = self._arr_pool.array_like(feat)
np.dot(gram_diff, feat, s_grad)
s_grad = s_grad.reshape((n, mh, mw))
loss += lw * style_weight[layer] * norm2(gram_diff) / len(
self.styles) / 2
axpy(lw * style_weight[layer] / len(self.styles),
normalize(s_grad), self.diff[layer])
def preprocess_images(self,
pool,
content_images,
style_images,
content_layers,
style_layers,
tile_size=512,
roll=None):
"""Performs preprocessing tasks on the input images."""
# Construct list of layers to visit during the backward pass
layers = []
for layer in reversed(self.layers()):
if layer in content_layers or layer in style_layers:
layers.append(layer)
# Prepare Gram matrices from style images
if roll is None:
print_('Preprocessing the style image(s)...')
for image in style_images:
grams = {}
self.set_image(image)
roll2(self.img, roll)
feats = self.prepare_features(pool,
style_layers,
tile_size,
passes=1)
for layer in feats:
grams[layer] = gram_matrix(feats[layer])
self.styles.append(StyleData(grams))
# Prepare feature maps from content image
if roll is None:
print_('Preprocessing the content image(s)...')
n = 10
else:
n = 1
for image in content_images:
self.set_image(image)
roll2(self.img, roll)
feats = self.prepare_features(pool,
content_layers,
tile_size,
passes=n)
self.contents.append(ContentData(feats))
def preprocess_images(self,
pool,
content_images,
style_images,
content_layers,
style_layers,
tile_size=512,
roll=None):
"""Performs preprocessing tasks on the input images."""
# Construct list of layers to visit during the backward pass
layers = []
for layer in reversed(self.layers()):
if layer in content_layers or layer in style_layers:
layers.append(layer)
# Prepare Gram matrices from style images
if roll is None:
print('Preprocessing the style image(s)...')
sizes = [None]
if ARGS.style_multiscale:
vmin, vmax = ARGS.style_multiscale
size = vmax
sizes = [vmax]
while True:
size = int(round(size / np.sqrt(2)))
if size < max(32, vmin):
break
sizes.append(size)
if not self.styles:
grams = {}
count = 0
for i, image in enumerate(style_images):
scale_too_big_cond = False
for size in reversed(sizes):
if scale_too_big_cond:
break
if size:
image_scaled = resize_to_fit(image, size)
if max(image_scaled.size) == max(image.size):
scale_too_big_cond = True
if min(image_scaled.size) < 32:
continue
self.set_image(image_scaled)
h, w = image_scaled.size
print('Processing style {} at {}x{}.'.format(
i + 1, h, w))
else:
self.set_image(image)
roll2(self.img, roll)
feats = self.prepare_features(pool,
style_layers,
tile_size,
passes=1)
for layer in feats:
gram = gram_matrix(feats[layer])
if layer not in grams:
grams[layer] = gram
else:
grams[layer] += gram
count += 1
for gram in grams.values():
gram /= count
self.styles.append(StyleData(grams))
# Prepare feature maps from content image
if roll is None:
print('Preprocessing the content image(s)...')
n = 10
else:
n = 1
for image in content_images:
self.set_image(image)
roll2(self.img, roll)
feats = self.prepare_features(pool,
content_layers,
tile_size,
passes=n)
self.contents.append(ContentData(feats))