def __call__(self, input, reference):
## Use "spl_denorm" when reading a [-1,1] input, but you want to compute the loss over a [0,1] range
# self.spl_denorm=False when your inputs and outputs are in [0,1] range already
# Note: only rgb_to_yuv() requires image in the [0,1], so this denorm is optional, depending on the net
if self.spl_denorm:
input = denorm(input)
reference = denorm(reference)
total_loss = 0
if self.rgb:
total_loss += self.trace(input, reference)
if self.yuv:
# rgb_to_yuv() needs images in [0,1] range to work
if not self.spl_denorm and self.yuv_denorm:
input = denorm(input)
reference = denorm(reference)
input_yuv = rgb_to_yuv(input)
reference_yuv = rgb_to_yuv(reference)
total_loss += self.trace(input_yuv, reference_yuv)
if self.yuvgrad:
input_h, input_v = get_image_gradients(input_yuv)
ref_h, ref_v = get_image_gradients(reference_yuv)
total_loss += self.trace(input_v, ref_v)
total_loss += self.trace(input_h, ref_h)
return total_loss
def __call__(self, x, y):
"""
Args:
x: input image batch.
y: reference image batch.
"""
if self.spl_denorm:
x = denorm(x)
y = denorm(y)
total_loss = 0
if self.rgb:
total_loss += self.trace(x, y)
if self.yuv:
# rgb_to_yuv() needs images in [0,1] range to work
if not self.spl_denorm and self.yuv_denorm:
x = denorm(x)
y = denorm(y)
input_yuv = rgb_to_yuv(x)
reference_yuv = rgb_to_yuv(y)
total_loss += self.trace(input_yuv, reference_yuv)
if self.yuvgrad:
input_h, input_v = get_image_gradients(input_yuv)
ref_h, ref_v = get_image_gradients(reference_yuv)
total_loss += self.trace(input_v, ref_v)
total_loss += self.trace(input_h, ref_h)
return total_loss
def __call__(self, input, reference):
## Use "spl_denorm" when reading a [-1,1] input, but you want to compute the loss over a [0,1] range
# Note: only rgb_to_yuv() requires image in the [0,1], so this denorm is optional, depending on the net
if self.spl_denorm == True:
input = denorm(input)
reference = denorm(reference)
input_h, input_v = get_image_gradients(input)
ref_h, ref_v = get_image_gradients(reference)
trace_v = self.trace(input_v, ref_v)
trace_h = self.trace(input_h, ref_h)
return trace_v + trace_h
def __call__(self, x, y):
"""
Args:
x: input image batch.
y: reference image batch.
"""
if self.spl_denorm:
x = denorm(x)
y = denorm(y)
input_h, input_v = get_image_gradients(x)
ref_h, ref_v = get_image_gradients(y)
trace_v = self.trace(input_v, ref_v)
trace_h = self.trace(input_h, ref_h)
return trace_v + trace_h
def forward(self, img):
if self.type == 'separator':
if self.recursions > 1:
for i in range(self.recursions - 1):
img = self.filter_low(img)
img = img - self.filter_low(img)
elif self.type == 'independent':
img = self.filter_low(img)
if self.normalize:
return denorm(img)
else:
return img